Focus on: Andrew Harvey September 2011

Selected Readings – September 2011 1

SELECTED READINGS

Focus on: Andrew Harvey

September 2011


INDEX

INTRODUCTION............................................................................................................. 8

1 WORKING PAPERS AND ARTICLES ................................................................ 9

1.1 Harvey A.C. and V. Oryshchenko, 2010, “Kernel density estimation for time series models”, International Journal of Forecasting. ...................................................................................9

1.2 Harvey, A.C. and S.J. Koopman, 2009, “Unobserved Components Models in Economics and Finance”, IEEE Control Systems Magazine, December, 2009, pages 71-81. .............................9

1.3 Harvey A.C., 2009, “The local quadratic trend model”, Journal of Forecasting, volume 29, pages 94-108. .....................................................................................................................................9

1.4 Harvey A.C. and D. Delle Monache, 2009, “Computing the Mean Square Error of Unobserved Components Extracted by Misspecified Time Series Models”, Journal of Economic Dynamics and Control, volume 33, Issue 2, pages 283-95. ................................................................10

1.5 Andrew C. Harvey and Thomas Trimbur, 2008, “Trend Estimation and the Hodrick-Prescott Filter”, Journal of Japanese Statistical Society, volume 38, No. 1, pages 41-9. ................10

1.6 Andrew Harvey, 2008, “Tracking a changing copula”, Journal of Empirical Finance, volume 17, Issue 3, June 2010, pages 485-500. ...................................................................................10

1.7 Andrew C. Harvey and Fabio Busetti, 2008, “When is a copula constant? A test for changing relationships”, Cambridge Working Papers in Economics No. CWPE 0841. ................11

1.8 Andrew C. Harvey and Tirthankar Chakravarty, 2008, “Beta-t-(E) GARCH”, Cambridge Working Papers in Economics No. CWPE 0840. ..........................................................11

1.9 Andrew Harvey, 2008, “Dynamic distributions and changing copulas”, Cambridge Working Papers in Economics No. CWPE 0839................................................................................12

1.10 Andrew Harvey, 2008, “Modeling the Phillips curve with Unobserved Components”, Cambridge Working Papers in Economics No. CWPE 0805. ..........................................................12

1.11 Andrew C. Harvey and Fabio Busetti, 2008, “Testing for Trend”, Bank of Italy, Economic Research Department, series Temi di discussione (Economic working papers) No. 614. 13

1.12 Andrew C. Harvey and Giuliano De Rossi, 2007, “Quantiles, expectiles and splines”, Cambridge Working Papers in Economics No. CWPE 0660. ..........................................................13

1.13 Andrew C. Harvey and Fabio Busettti, 2007, “Tests of time-invariance”, Cambridge Working Papers in Economics No. CWPE 0701................................................................................14

1.14 Andrew C. Harvey and Giuliano De Rossi, 2006, “Time-varying quantiles”, Cambridge Working Papers in Economics No. CWPE 0649................................................................................15

1.15 Andrew C. Harvey, F. Busetti, L. Forni and F. Vendetti, 2006, “Inflation convergence and divergence within the European Monetary Union”, ECB Working paper No. 574. ...............15

1.16 Andrew C. Harvey, Fabio Busetti and Silvia Fabiani, 2006, “Convergence of prices and rates of inflation”, Oxford Bulletin of Economics and Statistics, volume 68, pages 863-77. ..........16

1.17 Andrew C. Harvey and V.M. Carvalho, 2005, “Convergence in the trends and cycles of Euro Zone income”, Journal of Applied Econometrics, volume 20, pages 275-289. ......................16


1.18 Andrew C. Harvey and Fabio Busetti, 2003, “Seasonality Tests”, Journal of Business and Economic Statistics, volume 21, pages 420-36....................................................................................17

1.19 Andrew C. Harvey and J. Bernstein, 2003, “Measurement and Testing of Inequality from Time Series of Deciles with an Application to U.S. Wages”, Review of Economics and Statistics, volume 85, pages 141-52......................................................................................................17

1.20 Andrew C. Harvey, T. Trimbur and H.K. van Dijk, 2003, “Cyclical components in economic time series: a Bayesian approach”, University of Cambridge, Cambridge Working Papers in Economics No. 0302.............................................................................................................18

1.21 Andrew C. Harvey and Vasco M. Carvalho, 2002, “Growth, cycles and convergence in US regional time series”, University of Cambridge, Cambridge Working Papers in Economics No. 0221. ................................................................................................................................................18

1.22 Andrew C. Harvey and Thomas Trimbur, 2001, “General model-based filters for extracting trends and cycles in economic time series”, University of Cambridge, Cambridge Working Papers in Economics No. 0113.............................................................................................19

1.23 Andrew C. Harvey and J. Nyblom, 2001, “Testing against Smooth Stochastic Trends”, Journal of Applied Econometrics. Volume 16, pages 415-29............................................................19

1.24 Andrew C. Harvey, 2001, “Testing in Unobserved Components Models”, Journal of Forecasting, 20, pages 1-19. .................................................................................................................20

1.25 Andrew C. Harvey and F Busetti, 2001, “Testing for the Presence of a Random Walk in Series with Structural Breaks”, Journal of Time Series Analysis, volume 22, pages 127-50.........20

1.26 Andrew C. Harvey and Siem Jan Koopman, 2000, “Computing Observation Weights for Signal Extraction and Filtering”, Econometric Society, Econometric Society World Congress 2000 Contributed Papers No. 0888. ....................................................................................................21

1.27 Andrew C. Harvey and T Proietti, 2000, “A Beveridge-Nelson Smoother”, Economics Letters, volume 67, pages 139-46.........................................................................................................22

1.28 Andrew C. Harvey and C.H. Chung, 2000, “Estimating the Underlying Change in Unemployment in the UK”, Journal of the Royal Statistical Society Series A, volume 163, pages 303-39. 22

1.29 Andrew C. Harvey and J. Nyblom, 2000, “Tests of Common Stochastic Trends”, Econometric Theory, volume 16, pages 176-99..................................................................................23

1.30 Andrew C. Harvey and Siem Jan Koopman, 1999, “Signal Extraction and the Formulation of Unobserved Components Models”, Tilburg University, Center for Economic Research, Discussion Paper No. 1999-44. ...........................................................................................23

1.31 Andrew C. Harvey and Mariane Streibel 1998, “Tests for Deterministic versus Indeterministic Cycles”, Journal of Time Series Analysis, volume 19, pages 505-29. ....................24

1.32 Andrew C. Harvey and Mariane Streibel, 1997, “Testing for a Slowly Changing Level with Special Reference to Stochastic Volatility”, Faculty of Economics and Politics, University of Cambridge.............................................................................................................................................24

1.33 Andrew C. Harvey, S Koopman and M Riani, 1997, “The Modelling and Seasonal Adjustment of Weekly Observations “, Journal of Business and Economic Statistics, volume 15, pages 354-68. .........................................................................................................................................25

1.34 Andrew C. Harvey 1997, “Trends, Cycles and Autoregressions”, Economic Journal, volume 107, pages 192-201...................................................................................................................25


1.35 Andrew C. Harvey and Neil Shephard, 1996, “Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns”, Journal of Business and Economic Statistics, volume 14, pages 429-434. .......................................................................................................................................25

1.36 Andrew C. Harvey and with S Koopman, “Structural Time Series Models in Medicine”, Statistical Methods in Medical Research, 1996, volume 5, pages 23-49...........................................26

1.37 Andrew C. Harvey and A. Scott, 1994, “Seasonality in Dynamic Regression Models”, Economic Journal, volume 104, pages 1324-1345. .............................................................................26

1.38 Andrew C. Harvey, E Ruiz and Neil Shephard, 1994, “Multivariate Stochastic Variance Models”, Review of Economic Studies, 61, pages 247-64. .................................................................27

1.39 Andrew C. Harvey and A. Jaeger, 1993, “Detrending, Stylized Facts and the Business Cycle”, Journal of Applied Econometrics, volume 8, pages 231-47. ................................................27

1.40 Andrew C. Harvey and Mariane Streibel, 1993, “Estimation of Simultaneous Equation Models with Stochastic Trends”, Journal of Economic Dynamics and Control, volume 17, pages 263-87. 28

1.41 Andrew C. Harvey, Esther Ruiz and Enrique Sentana 1992, “Unobserved Component Time Series Models with ARCH Disturbances”, Journal of Econometrics, volume 52, pages 129-57. 28

1.42 Andrew C. Harvey and Pablo Marshall, 1991, “Inter-Fuel Substitution, Technical Change and the Demand for Energy in the UK Economy”, Applied Economics, volume 23, pages 1077-86. 29

1.43 Andrew C. Harvey and N. G. Shephard, 1990, “On the Probability of Estimating a Deterministic Component in the Local Level Model”, Journal of Time Series Analysis, volume 11, pages 339-47. ...................................................................................................................................29

1.44 Andrew C. Harvey and Ralph D. Snyder, 1990, “Structural Time Series Models in Inventory Control”, International Journal of Forecasting, volume 6, pages 187-98. .....................29

1.45 Andrew C. Harvey and C. Fernandes, 1990, “Time Series Models for Insurance Claims”, Journal of the Institute of Actuaries, 116, pages 513-28. ..................................................................30

1.46 Andrew C. Harvey and C. Fernandes, 1989, “Time Series Models for Count or Qualitative Observations”, Invited paper at Summer Meeting of the American Statistical Association, Washington DC, Journal of Business & Economic Statistics, volume 7, pages 409-422. 30

1.47 Andrew C. Harvey and J. Stock, 1989, “Estimating Integrated Higher Order Continuous Time Autoregressions with an Application to Money-Income Causality”, Journal of Econometrics, volume 42, pages 319-336............................................................................................30

1.48 Andrew C. Harvey and P Robinson, 1988, “Efficient Estimation of Nonstationary Time Series Regression”, Journal of Time Series Analysis, volume 9, pages 201-214. ............................31

1.49 Andrew C. Harvey and J. Stock, 1988, “Continuous Time Autoregressive Models with Common Stochastic Trends”, Journal of Economic Dynamics and Control, volume 12, pages 365-384. 31

1.50 Andrew C. Harvey and J. Durbin, 1986, “The Effects of Seat Belt Legislation on British Road Casualties: A case study in structural modelling”, International Journal of Forecasting, volume 2, Issue 4, pages 496-497. ........................................................................................................32


1.51 Andrew C. Harvey and J. H. Stock, 1985, “The Estimation of Higher Order Continuous Time Autoregressive Models”, Econometric Theory, volume 1, pages 97-112. ..............................32

1.52 Andrew C. Harvey, 1984, “A Unified View of Statistical Forecasting Procedures”, Journal of Forecasting, volume 3, pages 245-283. .............................................................................33

1.53 Andrew C. Harvey and L. Franzini, 1983, “Testing for Deterministic Trend and Seasonal Components in Time Series Models”, Biometrika, volume 70, pages 673-682. ...............33

1.54 Andrew C. Harvey and G. D. A. Phillips, 1982, “Testing for Contemporaneous Correlation of Disturbances in Systems of Regression Equations”, Bulletin of Economic Research, volume 34, pages 79-91. ......................................................................................................34

1.55 Andrew C. Harvey, 1981, “Finite Sample Prediction and Overdifferencing”, Journal of Time Series Analysis, volume 2, pages 221-232..................................................................................34

1.56 Andrew C. Harvey and G. D. A. Phillips, 1981, “Testing for Serial Correlation in Simultaneous Equation Models: Some Further Results”, Journal of Econometrics, volume 17, pages 99-105. .........................................................................................................................................34

1.57 Andrew C. Harvey and G. D. A. Phillips, 1981, “Testing for Heteroscedasticity in Simultaneous Equation Models”, Journal of Econometrics, volume 15, pages 311-340. ...............35

1.58 Andrew C. Harvey and G. D. A. Phillips, 1979, “Maximum Likelihood Estimation of Regression Models with Autoregressive Moving Average Disturbances”, Biometrika, volume 66, 1, pages 49-68. .......................................................................................................................................35

1.59 Andrew C. Harvey, 1977, “Discrimination between CES and VES Production Functions”, Annals of Economic and Social Measurement, pages 463-471.....................................36

1.60 Andrew C. Harvey and P. Collier, 1977, “Testing for Functional Misspecification in Regression Analysis”, Journal of Econometrics, volume 6, pages 103-119. ....................................36

1.61 Andrew C. Harvey and G. D. A. Phillips, 1974, “A Comparison of the Power of Some Tests for Heteroscedasticity in the General Linear Model”, Journal of Econometrics, volume 2, pages 307-316. .......................................................................................................................................36

2 ARTICLES IN EDITED VOLUMES ................................................................... 38

2.1 Andrew C. Harvey ,T. M. Trimbur and H. K. van Dijk 2007, “Bayes Estimates of the Cyclical Component in Twentieth Century US Gross Domestic Product” , In Growth and Cycle in the Eurozone, Edited by G. L. Mazzi and G. Savio, pages 76- 89,Palgrave MacMillan, Basingstoke............................................................................................................................................38

2.2 Andrew C. Harvey and T. M. Trimbur, 2007, “Trend Estimation, Signal-Noise Ratios and the Frequency of Observations” ,In Growth and Cycle in the Eurozone, Edited by G. L. Mazzi and G. Savio, pages 60-75, Palgrave MacMillan, Basingstoke. .............................................38

2.3 Andrew C. Harvey , 2006, “Forecasting with Unobserved Components Time Series Models”, Handbook of Economic Forecasting, edited by G Elliot, C Granger and A Timmermann, pages 327-412,North Holland.....................................................................................39

2.4 Andrew C. Harvey and G de Rossi, 2006, “Signal Extraction”, Palgrave Handbook of Econometrics, volume 1, edited by K Patterson and T C Mills, pages 970-1000, Palgrave MacMillan. ............................................................................................................................................39

2.5 Andrew C. Harvey, 2005, “A unified approach to testing for stationarity and unit roots”, In Identification and Inference for Econometric Models, edited by Andrews, D.W. K. and J. H. Stock, pages 403-25, Cambridge University Press: New York. ........................................................40


2.6 Andrew C. Harvey , 2004, “Tests for cycles, in State space and unobserved component models” , Harvey A. C., Koopman S. J. and Neil Shephard (eds), pages 102-119, Cambridge University Press. ...................................................................................................................................40

2.7 Andrew C. Harvey , 2002, “Trends, cycles and convergence”, Chapter 8 of Economic Growth: Sources, Trends and Cycles, Proceedings of Fifth Annual Conference of Bank of Chile, edited by N. Loayza and R. Soto, pages 221-250................................................................................41

2.8 Andrew C. Harvey, 2001, “Trend Analysis, in Encyclopedia of Environmetrics”, volume 4, D.R Brillinger (ed), pages 2243-2257, Chichester: John Wiley and Sons. ...................................41

2.9 Andrew C. Harvey and N G Shephard, 1993, “Structural Time Series Models”, in Maddala G. S., Rao C. R. and H. D. Vinod (eds), Handbook of Statistics, volume 11: Econometrics, Amsterdam: North-Holland, pages 261-30. ..............................................................41

2.10 Andrew C. Harvey , 1983, “The Formulation of Structural Time Series Models in Discrete and Continuous Time”, Invited paper at First Catalan International Symposium in Statistics, Barcelona, Questiio, volume 7, pages 563-575. .................................................................41

3 BOOKS .................................................................................................................... 42

3.1 The Econometric Analysis of Time Series, 1981, Philip Allan, Deddington. Paperback, 1983. Second edition, 1990. German translation, Oldenbourg Verlag, 1994...................................42

3.2 Time Series Models, 1981, Philip Allan, Deddington. Japanese translation, Tokyo University Press, 1985. Second edition, 1993. German translation, Oldenbourg Verlag, 1995. ....42

3.3 Forecasting. Structural Time Series Models and the Kalman Filter, 1989, Paperback, 1990, Cambridge University Press. .....................................................................................................42

4 NOTES AND COMMENTS IN JOURNALS ...................................................... 43

4.1 “A Note on the Efficiency of Kelejian's Method of Estimating Cobb-Douglas Type Functions with Multiplicative and Additive Errors”, 1976, International Economic Review, pages 506-509. 43

4.2 “An Alternative Proof and Generalization of a Test for Structural Change”, 1976, American Statistician, pages 122-123. ................................................................................................43

4.3 “Some Comments on Multicollinearity in Regression”, 1977, Applied Statistics, pages 188-191. 43

4.4 “On the Unbiasedness of Robust Regression Estimators”, 1978, Communications in Statistics A, 779-783..............................................................................................................................43

4.5 “A Note on Testing for Gaps in Seasonal Moving Average Models” (with J Tomenson), 1981, Journal of the Royal Statistical Society. Series B, pages 240-243...........................................43

4.6 “A Note on Estimating and Testing Exogenous Variable Coefficient Estimators in Simultaneous Equation Models” (with G D A Phillips), 1984, Economic Letters, volume 15, pages 301-7. 43

4.7 “A Note on the Estimation of Variances in State Space Models Using the Maximum A Posteriori Procedure” (with S Peters), 1985, IEEE Trans. on Automatic Control, AC-30, pages 1048-1050...............................................................................................................................................44

4.8 “Further Comments on Stationarity Tests in Series with Structural Breaks at Unknown Points”, 2003, Journal of Time Series Analysis. Volume 24, pages 137-40......................................44


4.9 “A note on common cycles, common trends and convergence “(with V Carvalho and T Trimbur). 2007, Journal of Business and Economic Statistics, volume 25, pages 12-20. ...............44


INTRODUCTION Andrew C. Harvey is Professor of Econometrics and Fellow of Corpus Christi

College at the University of Cambridge.

His main interests are: time series and econometrics, in particular, unobserved

components, signal extraction and volatility.

Andrew Harvey is the author of well-known textbooks on time series analysis,

econometrics and unobserved component models. He is also co-author of STAMP, a

package for structural time series models.

He wrote the textbooks ‘Time Series Models’ and ‘Econometric Analysis of Time

Series’. He has also published the monograph ‘Forecasting, Structural Time Series

Models and the Kalman filter’.

Andrew Harvey is a Fellow of the Econometric Society and the British Academy. He

is also an Associate Editor of the Journal of Time Series Analysis.

His journal articles appeared, among others, in the Journal of the American Statistical

Association, Journal of Econometrics, Econometrica, International Economic Review,

Journal of The Royal Statistical Society, Journal of Time Series Analysis, Journal of

Business and Economic Statistics, Journal of Forecasting, Econometric Theory,

Economic Journal, Journal of Economic Dynamics and Control, Applied Economics,

Journal of Applied Econometrics, Review of Economics and Statistics.

The following list is a non-exhaustive, subjective selection of Andrew Harvey’s

publications.

More information can be found at:

• The address of Andrew Harvey’s homepage at:

http://www.econ.cam.ac.uk/faculty/harvey/

Contact point: GianLuigi Mazzi, "Responsible for Euro-indicators and statistical

methodology", Estat - D5 "Key Indicators for European Policies"

[email protected].

http://www.econ.cam.ac.uk/faculty/harvey/

mailto:[email protected]


1 WORKING PAPERS AND ARTICLES

1.1 Harvey A.C. and V. Oryshchenko, 2010, “Kernel density estimation for time series models”, International Journal of Forecasting.

A time-varying probability density function, or the corresponding cumulative

distribution function, may be estimated nonparametrically by using a kernel and

weighting the observations using schemes derived from time series modelling. The

parameters, including the bandwidth, may be estimated by maximum likelihood or

cross-validation. Diagnostic checks may be carried out directly on residuals given by

the predictive cumulative distribution function. Since tracking the distribution is only

viable if it changes relatively slowly, the technique may need to be combined with a

filter for scale and/or location. The methods are applied to data on the NASDAQ

index and the Hong Kong and Korean stock market indices.

Full text available on-line at:

http://www.econ.cam.ac.uk/faculty/harvey/pubs10/TVDahvo9.pdf

1.2 Harvey, A.C. and S.J. Koopman, 2009, “Unobserved Components Models in Economics and Finance”, IEEE Control Systems Magazine, December, 2009, pages 71-81.

No abstract available.


http://www.dei.unipd.it/~chiuso/DOWNLOAD/Economics_Finance_Kalman.pdf

1.3 Harvey A.C., 2009, “The local quadratic trend model”, Journal of Forecasting, volume 29, pages 94-108.

The local quadratic trend model provides a flexible response to underlying

movements in a macroeconomic time series in its estimates of level and change. If the

underlying movements are thought of as a trend plus cycle, an estimate of the cycle

may be obtained from the quadratic term. Estimating the cycle in this way may offer a

useful alternative to other model-based methods of signal extraction, particularly

when the series is short. The properties of the filter used to extract the cycle are

http://www.econ.cam.ac.uk/faculty/harvey/pubs10/TVDahvo9.pdf

http://www.dei.unipd.it/~chiuso/DOWNLOAD/Economics_Finance_Kalman.pdf


analysed in the frequency domain and the technique is illustrated with

macroeconomic time series from several countries.


http://onlinelibrary.wiley.com/doi/10.1002/for.1144/abstract

1.4 Harvey A.C. and D. Delle Monache, 2009, “Computing the Mean Square Error of Unobserved Components Extracted by Misspecified Time Series Models”, Journal of Economic Dynamics and Control, volume 33, Issue 2, pages 283-295.

Algorithms are presented for computing mean square errors in a misspecified

unobserved components model when the true model is known. It is assumed that both

the true and misspecified models can be put in linear state space form. The algorithm

for filtering is based on the Kalman filter while that for smoothing modifies the fixed-

point smoother. Illustrations include the efficiency of the Hodrick–Prescott filter for

annual flow data and the mean square error of predictions for misspecified models

from the autoregressive integrated moving average class.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V85-4SSND1X-3&_user=10&_coverDate=02%2F28%2F2009&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1610795370&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=564894ae469db54994b46efa70c3a3ef&searchtype=a

1.5 Andrew C. Harvey and Thomas Trimbur, 2008, “Trend Estimation and the Hodrick-Prescott Filter”, Journal of Japanese Statistical Society, volume 38, No. 1, pages 41-49.



http://www.terrapub.co.jp/journals/jjss/pdf/3801/38010041.pdf

1.6 Andrew Harvey, 2008, “Tracking a changing copula”, Journal of Empirical Finance, volume 17, Issue 3, June 2010, pages 485-500.

A copula models the relationships between variables independently of their marginal

distributions. When the variables are time series, the copula may change over time.

Recursive procedures based on indicator variables are proposed for tracking these







http://www.terrapub.co.jp/journals/jjss/pdf/3801/38010041.pdf


changes over time. Estimation of the unknown parameters is by maximum likelihood.

When the marginal distributions change, pre-filtering is necessary before constructing

the indicator variables on which the recursions are based. This entails estimating time-

varying quantiles and a simple method based on time-varying histograms is proposed.

The techniques are applied to the Hong Kong and Korean stock market indices. Some

interesting and unexpected movements are detected, particularly after the attack on

the Hong Kong dollar in 1997.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VFG-4XK4593-1&_user=10&_coverDate=06%2F30%2F2010&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1610775822&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=54b92110987031a548cb782ff29d3fbe&searchtype=a

1.7 Andrew C. Harvey and Fabio Busetti, 2008, “When is a copula constant? A test for changing relationships”, Cambridge Working Papers in Economics No. CWPE 0841.

A copula defines the probability that observations from two time series lie below

given quantiles. It is proposed that stationarity tests constructed from indicator

variables be used to test against the hypothesis that the copula is changing over time.

Tests associated with different quantiles may point to changes in different parts of the

copula, with the lower quantiles being of particular interest in financial applications

concerned with risk. Tests located at the median provide an overall test of a changing

relationship. The properties of various tests are compared and it is shown that they are

still effective if pre-filtering is carried out to correct for changing volatility or, more

generally, changing quantiles. Applying the tests to daily stock return indices in Korea

and Thailand over the period 1995-9 indicates that the relationship between them is

not constant over time.


http://www.econ.cam.ac.uk/dae/repec/cam/pdf/cwpe0841.pdf

1.8 Andrew C. Harvey and Tirthankar Chakravarty, 2008, “Beta-t-(E) GARCH”, Cambridge Working Papers in Economics No. CWPE 0840.

The GARCH-t model is widely used to predict volatility. However, modeling the

conditional variance as a linear combination of past squared observations may not be








the best approach if the standardized observations are non-Gaussian. A simple

modification lets the conditional variance, or its logarithm, depend on past values of

the score of a t-distribution. The fact that the transformed variable has a beta

distribution makes it possible to derive the properties of the resulting models. A

practical consequence is that the conditional variance is more resistant to extreme

observations. Extensions to deal with leverage and more than one component are

discussed, as are the implications of distributions other than Student's t.



1.9 Andrew Harvey, 2008, “Dynamic distributions and changing copulas”, Cambridge Working Papers in Economics No. CWPE 0839.

A copula models the relationships between variables independently of their marginal

distributions. When the variables are time series, the copula may change over time. A

statistical framework is suggested for tracking these changes over time. When the

marginal distributions change, pre-filtering is necessary before constructing the

indicator variables on which the tracking of the copula is based. This entails solving

an even more basic problem, namely estimating time-varying quantiles. The methods

are applied to the Hong Kong and Korean stock market indices. Some interesting

movements are detected, particularly after the attack on the Hong Kong dollar in

1997.



1.10 Andrew Harvey, 2008, “Modeling the Phillips curve with Unobserved Components”, Cambridge Working Papers in Economics No. CWPE 0805.

The relationship between inflation and the output gap can be modeled simply and

effectively by including an unobserved random walk component in the model. The

dynamic properties match the stylized facts and the random walk component satisfies

the properties normally required for core inflation. The model may be generalized to

as to include a term for the expectation of next period's output, but it is shown that

this is difficult to distinguish from the original specification. The model is fitted as a

single equation and as part of a bivariate model that includes an equation for GDP.




Fitting the bivariate model highlights some new aspects of unobserved components

modeling. Single equation and bivariate models tell a similar story: an output gap two

per cent above trend is associated with an annual inflation rate that is one percent

above core inflation.



1.11 Andrew C. Harvey and Fabio Busetti, 2008, “Testing for Trend”, Bank of Italy, Economic Research Department, series Temi di discussione (Economic working papers) No. 614.

The paper examines various tests for assessing whether a time series model requires a

slope component. We first consider the simple t-test on the mean of first differences

and show that it achieves high power against the alternative hypothesis of a stochastic

nonstationary slope as well as against a purely deterministic slope. The test may be

modified, parametrically or nonparametrically to deal with serial correlation. Using

both local limiting power arguments and finite sample Monte Carlo results, we

compare the t-test with the nonparametric tests of Vogelsang (1998) and with a

modified stationarity test. Overall the t-test seems a good choice, particularly if it is

implemented by fitting a parametric model to the data. When standardized by the

square root of the sample size, the simple t-statistic, with no correction for serial

correlation, has a limiting distribution if the slope is stochastic. We investigate

whether it is a viable test for the null hypothesis of a stochastic slope and conclude

that its value may be limited by an inability to reject a small deterministic slope.

Empirical illustrations are provided using series of relative prices in the euro-area and

data on global temperature.


http://www.bancaditalia.it/pubblicazioni/econo/temidi/td07/td614_07/td614/en_tema_614.pdf

1.12 Andrew C. Harvey and Giuliano De Rossi, 2007, “Quantiles, expectiles and splines”, Cambridge Working Papers in Economics No. CWPE 0660.

A time-varying quantile can be fitted to a sequence of observations by formulating a

time series model for the corresponding population quantile and iteratively applying a

suitably modified state space signal extraction algorithm. It is shown that such time-





varying quantiles satisfy the defining property of fixed quantiles in having the

appropriate number of observations above and below. Expectiles are similar to

quantiles except that they are defined by tail expectations. Like quantiles, time-

varying expectiles can be estimated by a state space signal extraction algorithm and

they satisfy properties that generalize the moment conditions associated with fixed

expectiles. Time-varying quantiles and expectiles provide information on various

aspects of a time series, such as dispersion and asymmetry, while estimates at the end

of the series provide the basis for forecasting. Because the state space form can handle

irregularly spaced observations, the proposed algorithms can be easily adapted to

provide a viable means of computing spline-based non-parametric quantile and

expectile regressions.



1.13 Andrew C. Harvey and Fabio Busettti, 2007, “Tests of time-invariance”, Cambridge Working Papers in Economics No. CWPE 0701.

Quantiles provide a comprehensive description of the properties of a variable and

tracking changes in quantiles over time using signal extraction methods can be

informative. It is shown here how stationarity tests can be generalized to test the null

hypothesis that a particular quantile is constant over time by using weighted

indicators. Corresponding tests based on expectiles are also proposed; these might be

expected to be more powerful for distributions that are not heavy-tailed. Tests for

changing dispersion and asymmetry may be based on contrasts between particular

quantiles or expectiles. We report Monte Carlo experiments investigating the

effectiveness of the proposed tests and then move on to consider how to test for

relative time invariance, based on residuals from fitting a time-varying level or trend.

Empirical examples, using stock returns and U.S. inflation, provide an indication of

the practical importance of the tests.






1.14 Andrew C. Harvey and Giuliano De Rossi, 2006, “Time-varying quantiles”, Cambridge Working Papers in Economics No. CWPE 0649.

A time-varying quantile can be fitted to a sequence of observations by formulating a

time series model for the corresponding population quantile and iteratively applying a

suitably modified state space signal extraction algorithm. Quantiles estimated in this

way provide information on various aspects of a time series, including dispersion,

asymmetry and, for financial applications, value at risk. Tests for the constancy of

quantiles, and associated contrasts, are constructed using indicator variables; these

tests have a similar form to stationarity tests and, under the null hypothesis, their

asymptotic distributions belong to the Cramér von Mises family. Estimates of the

quantiles at the end of the series provide the basis for forecasting. As such they offer

an alternative to conditional quantile autoregressions and, at the same time, give some

insight into their structure and potential drawbacks.



1.15 Andrew C. Harvey, F. Busetti, L. Forni and F. Vendetti, 2006, “Inflation convergence and divergence within the European Monetary Union”, ECB Working paper No. 574.

We study the convergence properties of inflation rates among the countries of the

European Monetary Union over the period 1980-2004.

Given the Maastricht agreements and the adoption of the single currency, the sample

can be naturally split into two parts, before and after the birth of the euro. We study

convergence in the first sub-sample by means of univariate and multivariate unit root

tests on inflation differentials, arguing that the power of the tests is considerably

increased if the Dickey-Fuller regressions are run without an intercept term. Over-all,

we are able to accept the convergence hypothesis over the period 1980-1997. We then

investigate whether the second sub-sample is characterized by stable inflation rates

across the European countries. Using stationarity tests on inflation differentials, we

find evidence of diverging behaviour. In particular, we can statistically detect two

separate clusters, or convergence clubs: a lower inflation group that comprises

Germany, France, Belgium, Austria, Finland and a higher inflation one with Spain,



Netherlands, Greece, Portugal and Ireland. Italy appears to form a cluster of its own,

standing in between the other two.


http://www.ecb.int/pub/pdf/scpwps/ecbwp574.pdf

1.16 Andrew C. Harvey, Fabio Busetti and Silvia Fabiani, 2006, “Convergence of prices and rates of inflation”, Oxford Bulletin of Economics and Statistics, volume 68, pages 863-877.

We consider how unit-root and stationarity tests can be used to study the convergence

of prices and rates of inflation. We show how the joint use of these tests in levels and

first differences allows the researcher to distinguish between series that are

converging and series that have already converged, and we set out a strategy to

establish whether convergence occurs in relative prices or just in rates of inflation.

Special attention is paid to the issue of whether a mean should be extracted in

carrying out tests in first differences and whether there is an advantage to adopting a

(Dickey-Fuller) unit-root test based on deviations from the last observation. The

asymptotic distribution of this last test statistic is given and Monte Carlo simulation

experiments show that the test yields considerable power gains for highly persistent

autoregressive processes with 'relatively large' initial conditions. The tests are applied

to the monthly series of the consumer price index in the Italian regional capitals over

the period 1970-2003.


http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0084.2006.00460.x/abstract

1.17 Andrew C. Harvey and V.M. Carvalho, 2005, “Convergence in the trends and cycles of Euro Zone income”, Journal of Applied Econometrics, volume 20, pages 275-289.

Multivariate unobserved components (structural) time series models are fitted to

annual post-war observations on real income per capita in countries in the Euro-zone.

The aim is to establish stylized facts about convergence as it relates both to long-run

and short-run movements. A new model, in which convergence components are

combined with a common trend and similar cycles, is proposed. The convergence

components are formulated as a second-order error correction mechanism; this

http://www.ecb.int/pub/pdf/scpwps/ecbwp574.pdf

http://onlinelibrary.wiley.com/doi/10.1111/j.1468-0084.2006.00460.x/abstract


ensures that the extracted components change smoothly, thereby enabling them to be

separated from transitory cycles.


http://onlinelibrary.wiley.com/doi/10.1002/jae.820/abstract

1.18 Andrew C. Harvey and Fabio Busetti, 2003, “Seasonality Tests”, Journal of Business and Economic Statistics, volume 21, pages 420-36.

This article modifies and extends the test against nonstationary stochastic seasonality

proposed by Canova and Hansen. A simplified form of the test statistic in which the

nonparametric correction for serial correlation is based on estimates of the spectrum at

the seasonal frequencies is considered and shown to have the same asymptotic

distribution as the original formulation. Under the null hypothesis, the distribution of

the seasonality test statistics is not affected by the inclusion of trends, even when

modified to allow for structural breaks, or by the inclusion of regressors with

nonseasonal unit roots. A parametric version of the test is proposed, and its

performance is compared with that of the nonparametric test using Monte Carlo

experiments. A test that allows for breaks in the seasonal pattern is then derived. It is

shown that its asymptotic distribution is independent of the break point, and its use is

illustrated with a series on U.K. marriages. A general test against any form of

permanent seasonality, deterministic or stochastic, is suggested and compared with a

Wald test for the significance of fixed seasonal dummies. It is noted that tests

constructed in a similar way can be used to detect trading-day effects. An appealing

feature of the proposed test statistics is that under the null hypothesis, they all have

asymptotic distributions belonging to the Cramer-von Mises family.


http://www.jstor.org/

1.19 Andrew C. Harvey and J. Bernstein, 2003, “Measurement and Testing of Inequality from Time Series of Deciles with an Application to U.S. Wages”, Review of Economics and Statistics, volume 85, pages 141-152.

This article uses unobserved-components time series models to capture the underlying

trends in the quarterly deciles of U.S. hourly wages. Tests of stability and divergence




are developed as a means of assessing changes in inequality. The decrease in the wage

gender gap is examined, and the impact of changes in the minimum wage is assessed.


http://www.mitpressjournals.org/doi/abs/10.1162/003465303762687767?journalCode=rest

1.20 Andrew C. Harvey, T. Trimbur and H.K. van Dijk, 2003, “Cyclical components in economic time series: a Bayesian approach”, University of Cambridge, Cambridge Working Papers in Economics No. 0302.

Cyclical components in economic time series are analysed in a Bayesian framework,

thereby allowing prior notions about periodicity to be used. The method is based on a

general class of unobserved component models that allow relatively smooth cycles to

be extracted. Posterior densities of parameters and smoothed cycles are obtained using

Markov chain Monte Carlo methods. An application to estimating business cycles in

macroeconomic series illustrates the viability of the procedure for both univariate and

bivariate models.


http://repub.eur.nl/res/pub/540/feweco20021114112026.pdf

1.21 Andrew C. Harvey and Vasco M. Carvalho, 2002, “Growth, cycles and convergence in US regional time series”, University of Cambridge, Cambridge Working Papers in Economics No. 0221.

This article reports the results of fitting unobserved components (structural) time

series models to data on real income per capita in eight regions of the United States.

The aim is to establish stylised facts about cycles and convergence. A new model is

developed in which convergence components are combined with a common trend and

cycles. These convergence components are formulated as a second-order error

correction mechanism which allows temporary divergence while imposing eventual

convergence. This model is able to characterise the convergence patterns of all but the

two richest US regions; these appear to have been diverging from the others in recent

years. The use of unit root tests for testing convergence is critically assessed in the

light of these results.



http://repub.eur.nl/res/pub/540/feweco20021114112026.pdf



http://www.econ.cam.ac.uk/dae/repec/cam/pdf/wp0221.pdf

1.22 Andrew C. Harvey and Thomas Trimbur, 2001, “General model-based filters for extracting trends and cycles in economic time series”, University of Cambridge, Cambridge Working Papers in Economics No. 0113.

A new class of model-based filters for extracting trends and cycles in economic time

series is presented. These low pass and band pass filters are derived in a mutually

consistent manner as the joint solution to a signal extraction problem in an

unobserved components model. The resulting trends and cycles are computed in finite

samples using a Kalman filter and associated smoother. The filters form a class which

is a generalisation of the class of Butterworth filters, widely used in engineering. They

are very flexible and have the important property of allowing relatively smooth cycles

to be extracted from economic time series. Perfectly sharp, or ideal, band pass filters

emerge as a special case. Applying the method to a quarterly series on US investment

shows a clearly defined cycle currently at the peak of a boom.



1.23 Andrew C. Harvey and J. Nyblom, 2001, “Testing against Smooth Stochastic Trends”, Journal of Applied Econometrics. Volume 16, pages 415-29.

A trend estimated from an unobserved components model tends to be smoother when

it is modelled as an integrated random walk rather than a random walk with drift. This

article derives a test of the null hypothesis that the trend is deterministic against the

alternative that it is an integrated random walk. It is assumed that the other component

in the model is normally distributed white noise. Critical values are tabulated, the

asymptotic distribution is derived and the performance of the test is compared with

the test against a trend specified as a random walk with drift. The test is extended to

allow for serially correlated and evolving seasonal components. When there is a

stationary process containing a single autoregressive unit root close to one, a bounds

test can be applied. In the case of a first-order autoregressive disturbance, it is shown

that a consistent test can still be obtained by carrying out estimation of the nuisance

parameters under the null hypothesis. The overall conclusion is that the most effective




test against an integrated random walk is a parametric one based on the random walk

plus drift test statistic, constructed from innovations, with the nuisance parameters

estimated in the unrestricted model.


http://qed.econ.queensu.ca/jae/2001-v16.3/

1.24 Andrew C. Harvey, 2001, “Testing in Unobserved Components Models”, Journal of Forecasting, 20, pages 1-19.

This article reviews recent work on testing for the presence of non-stationary

unobserved components and presents it in a unified way. Tests against random walk

components and seasonal components are given and it is shown how the procedures

may be extended to multivariate models and models with structural breaks. Many of

the test statistics have an asymptotic distribution belonging to the class of generalized

Cramér – von Mises distributions. A test for the number of common trends, or

equivalently, co-integrating vectors, is also described.


http://onlinelibrary.wiley.com/doi/10.1002/1099-131X%28200101%2920:1%3C1::AID-FOR764%3E3.0.CO;2-3/abstract

1.25 Andrew C. Harvey and F Busetti, 2001, “Testing for the Presence of a Random Walk in Series with Structural Breaks”, Journal of Time Series Analysis, volume 22, pages 127-50.

We consider tests for the presence of a random walk component in a stationary or

trend stationary time series and extend them to series that contain structural breaks.

The locally best invariant (LBI) test is derived and the asymptotic distribution is

obtained. Then a modified test statistic is proposed. The advantage of this statistic is

that its asymptotic distribution is not dependent on the location of the break point and

its form is that of the generalized Cramer–von Mises distribution, with degrees of

freedom depending on the number of break points. The performance of this modified

test is shown, via some simulation experiments, to be comparable with that of the LBI

test. An unconditional test, based on the assumption that there is a single break at an

unknown point, is also examined. The use of the tests is illustrated with data on the

flow of the Nile and US gross national product.

http://qed.econ.queensu.ca/jae/2001-v16.3/





http://onlinelibrary.wiley.com/doi/10.1111/1467-9892.00216/abstract

1.26 Andrew C. Harvey and Siem Jan Koopman, 1999, “Computing Observation Weights for Signal Extraction and Filtering”, Econometric Society, Econometric Society World Congress 2000 Contributed Papers No. 0888.

We present algorithms for computing the weights implicitly assigned to observations

when estimating unobserved components using a model in state space form. The

algorithms are for both filtering and signal extraction. In linear time-invariant models

such weights can sometimes be obtained analytically from the Wiener-Kolmogorov

formulae. Our method is much more general, being applicable to any model with a

linear state space form, including models with deterministic components and time-

varying state matrices. It applies to multivariate models and it can be used when there

are data irregularities, such as missing observations.

The algorithms can be useful for a variety of purposes in econometrics and statistics:

(i) the weights for signal extraction can be regarded as equivalent kernel functions and

hence the weight pattern can be compared with the kernels typically used in

nonparametric trend estimation; (ii) the weight algorithm for filtering implicitly

computes the coefficients of the vector error-correction model (VECM) representation

of any linear time series model; (iii) as a by-product the mean square errors associated

with estimators may be obtained; (iv) the algorithm can be incorporated within a

Markov chain Monte Carlo (MCMC) method enabling computation of weights

assigned to observations when computing the posterior mean of unobserved

components within a Bayesian treatment.

A wide range of illustrations show how the algorithms may provide important insights

in empirical analysis. The algorithms are provided and implemented for the software

package SsfPack 2.3, that is a set of filtering, smoothing and simulation algorithms for

models in state space form (see www.ssfpack.com). Some details of implementation

and example programs are given in the appendix of the paper.


http://fmwww.bc.edu/RePEc/es2000/0888.pdf


http://fmwww.bc.edu/RePEc/es2000/0888.pdf


1.27 Andrew C. Harvey and T Proietti, 2000, “A Beveridge-Nelson Smoother”, Economics Letters, volume 67, pages 139-146.

This note defines a Beveridge–Nelson smoother, that is a two-sided signal extraction

filter for trends. The smoother is shown to be the optimal estimator of the trend when

the ARIMA model can be decomposed into an uncorrelated random walk trend and

stationary cycle components. The conditions under which such decomposition is

possible are discussed.


http://www.sciencedirect.com/science/article/B6V84-3YRVPG4-3/2/cd54c81ba11b4ca3744678c5d46c9466

1.28 Andrew C. Harvey and C.H. Chung, 2000, “Estimating the Underlying Change in Unemployment in the UK”, Journal of the Royal Statistical Society Series A, volume 163, pages 303-309.

By setting up a suitable time series model in state space form, the latest estimate of

the underlying current change in a series may be computed by the Kalman filter. This

may be done even if the observations are only available in a time-aggregated form

subject to survey sampling error. A related series, possibly observed more frequently,

may be used to improve the estimate of change further. The paper applies these

techniques to the important problem of estimating the underlying monthly change in

unemployment in the UK measured according to the definition of the International

Labour Organisation by the Labour Force Survey. The fitted models suggest a

reduction in root-mean-squared error of around 10% over a simple estimate based on

differences if a univariate model is used and a further reduction of 50% if information

on claimant counts is taken into account. With seasonally unadjusted data, the

bivariate model offers a gain of roughly 40% over the use of annual differences. For

both adjusted and unadjusted data, there is a further gain of around 10% if the next

month's figure on claimant counts is used. The method preferred is based on a

bivariate model with unadjusted data. If the next month's claimant count is known, the

root-mean-squared error for the estimate of change is just over 10000.


http://onlinelibrary.wiley.com/doi/10.1111/1467-985X.00171/abstract



http://onlinelibrary.wiley.com/doi/10.1111/1467-985X.00171/abstract


1.29 Andrew C. Harvey and J. Nyblom, 2000, “Tests of Common Stochastic Trends”, Econometric Theory, volume 16, pages 176-199.

This paper is concerned with tests in multivariate time series models made up of

random walk (with drift) and stationary components. When the stationary component

is white noise, a Lagrange multiplier test of the hypothesis that the covariance matrix

of the disturbances driving the multivariate random walk is null is shown to be locally

best invariant, something that does not automatically follow in the multivariate case.

The asymptotic distribution of the test statistic is derived for the general model. The

test is then extended to deal with a serially correlated stationary component. The main

contribution of the paper is to propose a test of the validity of a specified value for the

rank of the covariance matrix of the disturbances driving the multivariate random

walk. This rank is equal to the number of common trends, or levels, in the series. The

test is very simple insofar as it does not require any models to be estimated, even if

serial correlation is present. Its use with real data is illustrated in the context of a

stochastic volatility model, and the relationship with tests in the cointegration

literature is discussed.


http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=34991

1.30 Andrew C. Harvey and Siem Jan Koopman, 1999, “Signal Extraction and the Formulation of Unobserved Components Models”, Tilburg University, Center for Economic Research, Discussion Paper No. 1999-44.

This paper looks at unobserved components models and examines the implied

weighting patterns for signal extraction. There are three main themes. The first is the

implications of correlated disturbances driving the components, especially those cases

in which the correlation is perfect. The second is how setting up models with t-

distributed disturbances leads to weighting patterns which are robust to outliers and

breaks. The third is a comparison of implied weighting patterns with kernels used in

nonparametric trend estimation and equivalent kernels used in spline smoothing. We

also examine how weighting patterns are affected by heteroscedasticity and irregular

spacing and provide an illustrative example.




http://arno.uvt.nl/show.cgi?fid=3893

1.31 Andrew C. Harvey and Mariane Streibel 1997, “Tests for Deterministic versus Indeterministic Cycles”, Journal of Time Series Analysis, volume 19, pages 505-29.

An indeterministic cycle can be represented as an infinite moving-average process

and has a smooth peak in its spectrum. A deterministic cycle can be formulated in

such a way that it is still stationary but its cyclical behaviour is characterized by a

sudden jump in the spectral distribution function. This paper shows how both

processes can be nested within the same model and how, when the cycle is buried in

noise, the null hypothesis of a deterministic cycle can be tested. The preferred tests

are based on the residuals from regressing the observations on sine and cosine

functions of the frequency of interest. They are derived as point optimal and locally

best invariant tests and can be extended to regression models which include other

explanatory variables. The asymptotic distribution of the locally best invariant test,

which is a one-sided score test, is shown to be Cramer–von Mises with two degrees of

freedom. Modifications for dealing with serially correlated noise are discussed and the

methods are illustrated with real data.


http://www.econ.cam.ac.uk/faculty/harvey/cytest.pdf

1.32 Andrew C. Harvey and Mariane Streibel, 1997, “Testing for a Slowly Changing Level with Special Reference to Stochastic Volatility”, Faculty of Economics and Politics, University of Cambridge.

A test for the presence of a stationary first-order autoregressive process embedded in

white noise is constructed so as to be relatively powerful when the autoregressive

parameter is close to one. This is done by setting up the autoregression in such a way

that it reduces to a constant, instead of a random walk, when the autoregressive

parameter goes to one. The locally best invariant test principle is then applied. The

test is equivalent to the locally best invariant test for the presence of a random walk in

noise for which the asymptotic distribution is known to be Cramer-von Mises.

http://arno.uvt.nl/show.cgi?fid=3893

http://www.econ.cam.ac.uk/faculty/harvey/cytest.pdf


The test is compared with some standard tests for serial correlation in the context of a

stochastic volatility model and shown to have relatively high power for the parameter

values typically found with daily financial time series. The tests are examined for

different transformations of the returns and it is concluded that absolute values are

likely to be a good choice in most situations. An example is given involving daily

exchange rates.


http://www.econ.cam.ac.uk/faculty/harvey/artest.pdf

1.33 Andrew C. Harvey, S Koopman and M Riani, 1997, “The Modelling and Seasonal Adjustment of Weekly Observations “, Journal of Business and Economic Statistics, volume 15, pages 354-68.

Several important economic time series are recorded on a particular day every week.

Seasonal adjustment of such series is difficult because the number of weeks varies

between 52 and 53 and the position of the recording day changes from year to year. In

addition certain festivals, most notably Easter, take place at different times according

to the year. This article presents a solution to problems of this kind by setting up a

structural time series model that allows the seasonal pattern to evolve over time and

enables trend extraction and seasonal adjustment to be carried out by means of state-

space filtering and smoothing algorithms. The method is illustrated with a Bank of

England series on the money supply.


http://www.riani.it/pub/HKR1997-JBES.pdf

1.34 Andrew C. Harvey 1997, “Trends, Cycles and Autoregressions”, Economic Journal, volume 107, pages 192-201.




1.35 Andrew C. Harvey and Neil Shephard, 1996, “Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns”, Journal of Business and Economic Statistics, volume 14, pages 429-434.

http://www.econ.cam.ac.uk/faculty/harvey/artest.pdf

http://www.riani.it/pub/HKR1997-JBES.pdf



A stochastic volatility model may be estimated by a quasi-maximum likelihood

procedure by transforming to a linear state space form. The method is extended to

handle correlation between the two disturbances in the model and applied to data on

stock returns.


http://harrisd.net/papers/ARCHSV/Multivariate%20Stochastic%20Volatility%20Models/HarveyShephard1996JoBES.pdf

1.36 Andrew C. Harvey and with S Koopman, “Structural Time Series Models in Medicine”, Statistical Methods in Medical Research, 1996, volume 5, pages 23-49.

Structural time series models are formulated in terms of components, such as trends,

seasonals and cycles, which have a direct interpretation. This article describes such

models and gives examples of how they can be applied in medicine. Univariate

models are considered first, and then extended to include explanatory variables and

interventions. Multivariate models are then shown to provide a framework for

modelling longitudinal data and for carrying out intervention analysis with control

groups. The final sections deal with data irregularities and non-Gaussian observations.


http://smm.sagepub.com/content/5/1/23.abstract

1.37 Andrew C. Harvey and A. Scott, 1994, “Seasonality in Dynamic Regression Models”, Economic Journal, volume 104, pages 1324-1345.

We examine the implications of treating seasonality as an unobserved component

which changes slowly over time. This approach simplifies the specification of

dynamic relationships by separating non-seasonal from seasonal factors. We illustrate

this approach using the consumption model of Davidson et al. (1978), estimating a

stable error correction model between consumption, income and prices over the

period 1958-92. More generally, we argue that autoregressive models are unlikely to

model slowly changing seasonality successfully, and may confound seasonal effects

with the dynamic responses of prime interest. Our approach can be used in a wide

range of cases and involves little loss in efficiency even if seasonality is deterministic.

http://harrisd.net/papers/ARCHSV/Multivariate Stochastic Volatility Models/HarveyShephard1996JoBES.pdf

http://harrisd.net/papers/ARCHSV/Multivariate Stochastic Volatility Models/HarveyShephard1996JoBES.pdf

http://smm.sagepub.com/content/5/1/23.abstract




1.38 Andrew C. Harvey, E Ruiz and Neil Shephard, 1994, “Multivariate Stochastic Variance Models”, Review of Economic Studies, 61, pages 247-64.

Changes in variance, or volatility, over time can be modeled using the approach based

on autoregressive conditional heteroscedasticity. Another approach is to model

variance as an unobserved stochastic process. Although it is not easy to obtain the

exact likelihood function for such stochastic variance models, they tie in closely with

developments in finance theory and have certain statistical attractions. This article sets

up a multivariate model, discusses its statistical treatment, and shows how it can be

modified to capture common movements in volatility in a very natural way. The

model is then fitted to daily observations on exchange rates.


http://math.uncc.edu/~zcai/harvey-ruiz-shephard.pdf

1.39 Andrew C. Harvey and A. Jaeger, 1993, “Detrending, Stylized Facts and the Business Cycle”, Journal of Applied Econometrics, volume 8, pages 231-47.

The stylized facts of macroeconomic time series can be presented by fitting structural

time series models. Within this framework, we analyse the consequences of the

widely used detrending technique popularised by Hodrick and Prescott (1980). It is

shown that mechanical detrending based on the Hodrick–Prescott filter can lead

investigators to report spurious cyclical behaviour, and this point is illustrated with

empirical examples. Structural time-series models also allow investigators to deal

explicitly with seasonal and irregular movements that may distort estimated cyclical

components. Finally, the structural framework provides a basis for exposing the

limitations of ARIMA methodology and models based on a deterministic trend with a

single break.




http://math.uncc.edu/~zcai/harvey-ruiz-shephard.pdf



1.40 Andrew C. Harvey and Mariane Streibel, 1993, “Estimation of Simultaneous Equation Models with Stochastic Trends”, Journal of Economic Dynamics and Control, volume 17, pages 263-87.

The estimation of a single equation within a system of simultaneous equations is

considered. The equation is assumed to contain a stochastic trend component which

implicitly makes allowance for factors, such as technical progress and tastes, which

are difficult to measure. Various instrumental variable estimators and the limited

information maximum likelihood estimator are compared on the basis of asymptotic

results and small sample simulations.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V85-4NR4YRN-D&_user=10&_coverDate=03%2F31%2F1993&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612352112&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=7d10c2a7c69f7a9299420333250d5bda&searchtype=a

1.41 Andrew C. Harvey, Esther Ruiz and Enrique Sentana 1992, “Unobserved Component Time Series Models with ARCH Disturbances”, Journal of Econometrics, volume 52, pages 129-57.

This paper considers how ARCH effects may be handled in time series models

formulated in terms of unobserved components. A general model is formulated, but

this includes as special cases a random walk plus noise model with both disturbances

subject to ARCH effects, an ARCH-M model with a time-varying parameter, and a

latent factor model with ARCH effects in the factors. Although the model is not

conditionally Gaussian, an approximate filter can be obtained and used as the basis for

estimation. The performance of this method is examined on real data sets and Monte

Carlo experiments are carried out. The method is extended to handle GARCH and a

latent factor model based on the t-distribution.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-458298B-11&_user=10&_coverDate=05%2F31%2F1992&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612356399&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=0d1f31dda2983819a04c0ace36906de5&searchtype=a












1.42 Andrew C. Harvey and Pablo Marshall, 1991, “Inter-Fuel Substitution, Technical Change and the Demand for Energy in the UK Economy”, Applied Economics, volume 23, pages 1077-86.

A model for energy demand, based on a translog cost function is formulated. Unlike

previous studies, technical progress is modelled by means of stochastic, as opposed to

deterministic, trend components. The model is estimated using quarterly UK data for

four economic sectors, and forecasts of the future level of demand are made.


http://www.informaworld.com/smpp/content~db=all~content=a739332990~frm=abslink

1.43 Andrew C. Harvey and N. G. Shephard, 1990, “On the Probability of Estimating a Deterministic Component in the Local Level Model”, Journal of Time Series Analysis, volume 11, pages 339-47.

A local level model has a deterministic level when the signal-to-noise ratio q is zero.

In this paper we investigate the properties of the maximum likelihood estimator of q,

paying particular attention to the case where its true value is zero. These properties

are shown to be crucially dependent on the initial conditions employed.


http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.1990.tb00062.x/abstract

1.44 Andrew C. Harvey and Ralph D. Snyder, 1990, “Structural Time Series Models in Inventory Control”, International Journal of Forecasting, volume 6, pages 187-98.

Exponential smoothing methods are often used to forecast demand in computerized

inventory control systems. These methods, by themselves, are rather ad hoc, but they

can be given a proper statistical foundation by setting up a class of structural time

series models. The purpose of the paper is to highlight the potential role of these

models in inventory control. In particular they are used as the basis for deriving

formulae for estimating the mean and variance of the lead time demand distribution

under both constant and stochastic lead time assumptions.






http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V92-45P4GPG-5F&_user=10&_coverDate=07%2F31%2F1990&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612365453&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b472d63b9cf96c81468f8b8e826cf63c&searchtype=a

1.45 Andrew C. Harvey and C. Fernandes, 1989, “Time Series Models for Insurance Claims”, Journal of the Institute of Actuaries, 116, pages 513-28.

The distribution of insurance claims in a given time period is usually regarded as a

random sum. This paper sets up a time series model for the value of the claims and

combines it with a model for the number of claims. Thus past observations can be

used to make predictions of the future values of the random sum, and the overall

model ensures that they are discounted appropriately. It is shown that explanatory

variables can be introduced into the model, and how it can be extended to handle

several groups. The general approach is based on the recently developed structural

time series methodology.


http://www.actuaries.org.uk/sites/all/files/documents/pdf/0513-0528.pdf

1.46 Andrew C. Harvey and C. Fernandes, 1989, “Time Series Models for Count or Qualitative Observations”, Invited paper at Summer Meeting of the American Statistical Association, Washington DC, Journal of Business & Economic Statistics, volume 7, pages 407-422.



http://www.sciencedirect.com/science/article/B6V92-45PMMPV-M/2/1fe5b8092698abbc4927ce772f67b4e5

1.47 Andrew C. Harvey and J. Stock, 1989, “Estimating Integrated Higher Order Continuous Time Autoregressions with an Application to Money-Income Causality”, Journal of Econometrics, volume 42, pages 319-336.

An algorithm is presented for computing the Gaussian maximum likelihood estimator

of the parameters of a multivariate continuous time autoregressive process with

multiple roots that equal zero. Some of the variables might be observed at a point in

time (‘stocks’) and some might be observed as an integral over a unit interval






http://www.actuaries.org.uk/sites/all/files/documents/pdf/0513-0528.pdf




(‘flows’). This algorithm, based on the Kalman-Bucy filter, is used to investigate the

possibility that previous findings that money Granger-causes industrial production

spuriously arose because time-averaged variables were analyzed using discrete time

methods.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-45828Y9-6B&_user=10&_coverDate=11%2F30%2F1989&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612382298&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=c8fa8e805e054dfe8dfab7b83cb65a8d&searchtype=a

1.48 Andrew C. Harvey and P Robinson, 1988, “Efficient Estimation of Nonstationary Time Series Regression”, Journal of Time Series Analysis, volume 9, pages 201-214.

A multiple time series regression model with trending regressors has residuals that are

believed to be not only serially dependent but nonstationary. Assuming the residuals

can be decomposed as a stationary autoregressive process of known order multiplied

by an unknown time-varying scale factor, we propose estimators of the regression

coefficients and show them to be as efficient as estimators based on known scale

factors. Our estimators have features in common with adaptive estimators proposed

by Carroll (1982) and Hannan (1963) for different regression problems, involving

respectively independent residuals with heteroskedasticity of unknown type, and

stationary residuals with unknown serial dependence structure.



1.49 Andrew C. Harvey and J. Stock, 1988, “Continuous Time Autoregressive Models with Common Stochastic Trends”, Journal of Economic Dynamics and Control, volume 12, pages 365-384.

A multivariate continuous time model is presented in which a n-dimensional process

is represented as the sum of k stochastic trends plus a n-dimensional stationary term,

assumed to obey a system of higher-order autoregressive stochastic differential

equations. When k < n, the variables are cointegrated and can be represented as linear

combinations of a reduced number of common trends. An algorithm to estimate the

parameters of the model is presented for the case that the trend and stationary








disturbances are uncorrelated. This algorithm is used to extract a common (continuous

time) stochastic trend from postwar U.S. GNP and consumption.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V85-45MFRW4-K&_user=10&_coverDate=09%2F30%2F1988&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612398949&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9665bbdf348cf3b52eeaee8f68e2a856&searchtype=a

1.50 Andrew C. Harvey and J. Durbin, 1986, “The Effects of Seat Belt Legislation on British Road Casualties: A case study in structural modelling”, International Journal of Forecasting, volume 2, Issue 4, pages 496-497.

Monthly data on road casualties in Great Britain are analysed in order to assess the

effects on casualty rates of the seat belt law introduced on January 31st, 1983. Such

analysis is known technically as intervention analysis. The form of intervention

analysis used in this paper is based on structural time series modelling and differs in

significant respects from standard intervention analysis based on ARIMA modelling.

The relative merits of the two approaches are compared. Structural modelling

intervention techniques are used to estimate the changes in casualty rates for various

categories of road users following the introduction of the seat belt law.


http://www.sciencedirect.com/science/article/B6V92-49W7DN5-F/2/6cc47b9182166ef053333c49e61144ad

1.51 Andrew C. Harvey and J. H. Stock, 1985, “The Estimation of Higher Order Continuous Time Autoregressive Models”, Econometric Theory, volume 1, pages 97-117.

A method is presented for computing maximum likelihood, or Gaussian, estimators of

the structural parameters in a continuous time system of higher order stochastic

differential equations. It is argued that it is computationally efficient in the standard

case of exact observations made at equally spaced intervals. Furthermore it can be

applied in situations where the observations are at unequally spaced intervals, some

observations are missing and/or the endogenous variables are subject to measurement

error. The method is based on a state space representation and the use of the Kalman–









Bucy filter. It is shown how the Kalman-Bucy filter can be modified to deal with

flows as well as stocks.



1.52 Andrew C. Harvey, 1984, “A Unified View of Statistical Forecasting Procedures”, Journal of Forecasting, volume 3, pages 245-275.

A large number of statistical forecasting procedures for univariate time series have

been proposed in the literature. These range from simple methods, such as the

exponentially weighted moving average, to more complex procedures such as Box–

Jenkins ARIMA modelling and Harrison–Stevens Bayesian forecasting. This paper

sets out to show the relationship between these various procedures by adopting a

framework in which a time series model is viewed in terms of trend, seasonal and

irregular components. The framework is then extended to cover models with

explanatory variables. From the technical point of view the Kalman filter plays an

important role in allowing an integrated treatment of these topics.



1.53 Andrew C. Harvey and L. Franzini, 1983, “Testing for Deterministic Trend and Seasonal Components in Time Series Models”, Biometrika, volume 70, pages 673-682.

A univariate time series model can be set up as the sum of trend, seasonal and

irregular components. The trend and seasonal components will normally be stochastic,

but deterministic components arise as a special case. This paper develops a test that

the trend and seasonal components are deterministic using the approach of Lehmann.

The procedure is then extended to test for deterministic components in a model

formulated in first differences. Both tests are exact and critical values are tabulated.


http://biomet.oxfordjournals.org/content/70/3/673.full.pdf





1.54 Andrew C. Harvey and G. D. A. Phillips, 1982, “Testing for Contemporaneous Correlation of Disturbances in Systems of Regression Equations”, Bulletin of Economic Research, volume 34, pages 79-91.




1.55 Andrew C. Harvey, 1981, “Finite Sample Prediction and Overdifferencing”, Journal of Time Series Analysis, volume 2, pages 221-232.

If the process generating a time series contains a deterministic component the

differencing operations carried out to achieve stationarity may lead to an ARMA

model which is strictly noninvertible. This is known as overdifferencing but it is

shown here that overdifferencing need not have serious implications for prediction

provided that a finite sample prediction procedure is used. The proposed method is

based on the Kalman filter and it allows both the optimal predictors and their mean

square errors to be computed.



1.56 Andrew C. Harvey and G. D. A. Phillips, 1981, “Testing for Serial Correlation in Simultaneous Equation Models: Some Further Results”, Journal of Econometrics, volume 17, pages 99-105.

In this paper approximate counterparts of the exact tests earlier proposed by the

authors are examined. Type 1 error probabilities and test powers are estimated and

compared using Monte Carlo experiments. The effect on the Type 1 error

probabilities of the misspecification which results when serial correlation is present

elsewhere in the system is also investigated.





http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-4582HM5-3H&_user=10&_coverDate=09%2F30%2F1981&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612447965&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=3d77a24bd85436cd153271705ac7f921&searchtype=a

1.57 Andrew C. Harvey and G. D. A. Phillips, 1981, “Testing for Heteroscedasticity in Simultaneous Equation Models”, Journal of Econometrics, volume 15, pages 311-340.

In this paper a number of exact and approximate tests for heteroscedasticity in a

simultaneous equation model are developed. The exact tests are analogous to those

which are available single equation regression models and the approximate tests

include a Lagrangian multiplier test. Type 1 error probabilities of approximate tests

and the powers of exact and approximate tests are examined using Monte Carlo

experiments for several simple cases of heteroscedasticity.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-4582CX4-19&_user=10&_coverDate=04%2F30%2F1981&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612451792&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=ed7fa7820cbb6fdbb6180dcac013200d&searchtype=a

1.58 Andrew C. Harvey and G. D. A. Phillips, 1979, “Maximum Likelihood Estimation of Regression Models with Autoregressive Moving Average Disturbances”, Biometrika, volume 66, 1, pages 49-68.

The regression model with autoregressive-moving average disturbances may be cast

in a form suitable for the application of Kalman filtering techniques. This enables the

generalized least squares estimator to be calculated without evaluating and inverting

the covariance matrix of the disturbances. The problem of forecasting future values of

the dependent variable is also effectively solved when the Kalman filter technique is

applied.

Furthermore, the properties of the residuals produced by the filter suggest that they

may be useful for diagnostic checking of the model. The Kalman filter algorithm also

forms the basis of a method for the exact maximum likelihood estimation of the












model. This may well have computational, as well as theoretical, advantages over

other methods.



1.59 Andrew C. Harvey, 1977, “Discrimination between CES and VES Production Functions”, Annals of Economic and Social Measurement, pages 463-471.



http://www.nber.org/chapters/c10530.pdf

1.60 Andrew C. Harvey and P. Collier, 1977, “Testing for Functional Misspecification in Regression Analysis”, Journal of Econometrics, volume 6, pages 103-119.

Recursive residuals may be used to detect functional misspecification in a regression

equation. A simple t-statistic and a related Sign test may be constructed from the

residuals. The powers of these tests compare favourably with the Durbin–Watson and

other tests commonly used to detect functional misspecification from residuals. In

addition the tests are relatively robust to serial correction in an otherwise correctly

specified model, and this is a further point in their favour.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-459J75M-3M&_user=10&_coverDate=07%2F31%2F1977&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612471104&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=afc724714211d61f581ee099d8f56a86&searchtype=a

1.61 Andrew C. Harvey and G. D. A. Phillips, 1974, “A Comparison of the Power of Some Tests for Heteroscedasticity in the General Linear Model”, Journal of Econometrics, volume 2, pages 307-316.

An exact parametric test against heteroskedasticity in the general linear model is

proposed. Its power is compared with that of the test proposed by Goldfeld and

Quandt and with the BLUS test. Under a variety of circumstances all three tests are

found to be of compare power.


http://www.nber.org/chapters/c10530.pdf







The proposed test is similar to the BLUS test but is based on easily computed

recursive residuals and it is believed that the flexibility and computational simplicity

of the new test makes it attractive as a practical procedure.


http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VC0-4582JWH-1C&_user=10&_coverDate=12%2F31%2F1974&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612475314&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=30137863ee9752f308843e245f5a22c2&searchtype=a







2 ARTICLES IN EDITED VOLUMES 2.1 Andrew C. Harvey ,T. M. Trimbur and H. K. van Dijk 2007, “Bayes

Estimates of the Cyclical Component in Twentieth Century US Gross Domestic Product” , In Growth and Cycle in the Eurozone, Edited by G. L. Mazzi and G. Savio, pages 76- 89,Palgrave MacMillan, Basingstoke.

Cyclical components in economic time series are analysed in a Bayesian framework,

thereby allowing prior notions about periodicity to be used. The method is based on a

general class of unobserved component models that encompasses a range of dynamics

in the stochastic cycle. This allows for instance relatively smooth cycles to be

extracted from time series. Posterior densities of parameters and estimated

components are obtained using Markov chain Monte Carlo methods, which we

develop for both univariate and multivariate models. Features such as time-varying

amplitude may be studied by examining different functions of the posterior draws for

the cyclical component and parameters. The empirical application illustrates the

method for annual US real GDP over the last 130 years.


http://publishing.eur.nl/ir/repub/asset/1798/ei200445.pdf 2.2 Andrew C. Harvey and T. M. Trimbur, 2003, “Trend Estimation, Signal-

Noise Ratios and the Frequency of Observations” ,In Growth and Cycle in the Eurozone, Edited by G. L. Mazzi and G. Savio, pages 60-75, Palgrave MacMillan, Basingstoke.

The implied signal extraction filters in unobserved components models depend on key

signal-noise ratios. This paper examines how these ratios change with the observation

interval. The analysis is based on continuous time models and is carried out for both

stocks and flows. As a by-product, a connection is established between continuous

time flow models and the canonical decomposition. The implications of using the

Hodrick-Prescott filter to extract cycles at annual and monthly frequencies are

discussed. Many of the arguments used in the literature to set the smoothing constant

are shown to be flawed. The analysis suggests that a model-based approach is the best

way to proceed. A model formulated in continuous time, or in discrete time at a fine

time interval, automatically adapts to any observation interval if it is set up in state

http://publishing.eur.nl/ir/repub/asset/1798/ei200445.pdf


space form. Concerns about the change in the shape of the filter and the way in which

the signal-noise ratio adapts are then no longer an issue.


http://131.111.165.101/faculty/harvey/lux7.pdf 2.3 Andrew C. Harvey , 2006, “Forecasting with Unobserved Components

Time Series Models”, Handbook of Economic Forecasting, edited by G Elliot, C Granger and A Timmermann, pages 327-412,North Holland.

Structural time series models are formulated in terms of components, such as trends,

seasonals and cycles, that have a direct interpretation.

As well as providing a framework for time series decomposition by signal extraction,

they can be used for forecasting and for .nowcasting. . The structural interpretation

allows extensions to classes of models that are able to deal with various issues in

multivariate series and to cope with non- Gaussian observations and nonlinear

models. The statistical treatment is by the state space form and hence data

irregularities such as missing observations are easily handled. Continuous time

models offer further flexibility in that they can handle irregular spacing. The paper

compares the forecasting performance of structural time series models with ARIMA

and autoregressive models. Results are presented showing how observations in linear

state space models are implicitly weighted in making forecasts and hence how

autoregressive and vector error correction representations can be obtained. The use of

an auxiliary series in forecasting and nowcasting is discussed. A final section

compares stochastic volatility models with GARCH.


http://www.econ.cam.ac.uk/faculty/harvey/pubs09/forecast16.pdf

2.4 Andrew C. Harvey and G de Rossi, 2004, “Signal Extraction”, Palgrave Handbook of Econometrics, volume 1, edited by K Patterson and T C Mills, pages 970-1000 ???, Palgrave MacMillan.


http://131.111.165.101/faculty/harvey/lux7.pdf

http://www.econ.cam.ac.uk/faculty/harvey/pubs09/forecast16.pdf



http://www.econ.cam.ac.uk/faculty/harvey/UCAHGdR8.pdf 2.5 Andrew C. Harvey, 2001, “A unified approach to testing for stationarity

and unit roots”, In Identification and Inference for Econometric Models, edited by Andrews, D.W. K. and J. H. Stock, pages 403-25, Cambridge University Press: New York.

Test against nonstationary unobserved components such as stochastic trends and

seasonals can be set up in such a way that the tests statistics have asymptotic

distributions belonging to the class of generalised Cramer-von Mises distributions.

Conversely, unit root tests can be formulated, using the Lagrange multiplier principle,

so as to yield test statistics which also generalized Cramer-von Mises. These ideas

may be extended to multivariate models and to models with structural breaks thereby

providing a simple unified approach to testing in nonstationary time series. Full text

available on-line at:

http://elsa.berkeley.edu/symposia/nsf01/harvey.pdf 2.6 Andrew C. Harvey, Siem Jan Koopman, Neil Shephard (eds), 2004, “In

State space and unobserved component models”, pages 102-119, Cambridge University Press.

This volume offers a broad overview of the state-of-the-art developments in the

theory and applications of state space modelling. With fourteen chapters from twenty

three contributors, it offers a unique synthesis of state space methods and unobserved

component models that are important in a wide range of subjects, including

economics, finance, environmental science, medicine and engineering. The book is

divided into four sections: introductory papers, testing, Bayesian inference and the

bootstrap, and applications.

It will give those unfamiliar with state space models a flavour of the work being

carried out as well as providing experts with valuable state-of-the art summaries of

different topics. Offering a useful reference for all, this accessible volume makes a

significant contribution to the advancement of time series analysis.


http://bilder.buecher.de/zusatz/22/22212/22212354_vorw_1.pdf

http://www.econ.cam.ac.uk/faculty/harvey/UCAHGdR8.pdf

http://elsa.berkeley.edu/symposia/nsf01/harvey.pdf

http://bilder.buecher.de/zusatz/22/22212/22212354_vorw_1.pdf


2.7 Andrew C. Harvey , 2002, “Trends, cycles and convergence”, Chapter 8 of Economic Growth: Sources, Trends and Cycles, Proceedings of Fifth Annual Conference of Bank of Chile, edited by N. Loayza and R. Soto, pages 221-250.



http://www.bcentral.cl/estudios/banca-central/pdf/v6/221_250harvey.pdf 2.8 Andrew C. Harvey, 2001, “Trend Analysis, in Encyclopedia of

Environmetrics”, volume 4, D.R Brillinger (ed), pages 2243-2257, Chichester: John Wiley and Sons.



http://www.econ.cam.ac.uk/faculty/harvey/trend.pdf 2.9 Andrew C. Harvey and N G Shephard, 1993, “Structural Time Series

Models”, in Maddala G. S., Rao C. R. and H. D. Vinod (eds), Handbook of Statistics, volume 11: Econometrics, Amsterdam: North-Holland, pages 261-30.



http://www.nuff.ox.ac.uk/users/shephard/old_papers/pub8.pdf 2.10 Andrew C. Harvey , 1983, “The Formulation of Structural Time Series

Models in Discrete and Continuous Time”, Invited paper at First Catalan International Symposium in Statistics, Barcelona, Questiio, volume 7, pages 563-575.



http://upcommons.upc.edu/revistes/bitstream/2099/4515/4/article.pdf

http://www.bcentral.cl/estudios/banca-central/pdf/v6/221_250harvey.pdf

http://www.econ.cam.ac.uk/faculty/harvey/trend.pdf

http://www.nuff.ox.ac.uk/users/shephard/old_papers/pub8.pdf

http://upcommons.upc.edu/revistes/bitstream/2099/4515/4/article.pdf


3 BOOKS 3.1 The Econometric Analysis of Time Series, 1981, Philip Allan, Deddington.

Paperback, 1983. Second edition, 1990. German translation, Oldenbourg Verlag, 1994.

3.2 Time Series Models, 1981, Philip Allan, Deddington. Japanese translation, Tokyo University Press, 1985. Second edition, 1993. German translation, Oldenbourg Verlag, 1995.

3.3 Forecasting. Structural Time Series Models and the Kalman Filter, 1989, Paperback, 1990, Cambridge University Press.


4 NOTES AND COMMENTS IN JOURNALS

4.1 “A Note on the Efficiency of Kelejian's Method of Estimating Cobb-Douglas Type Functions with Multiplicative and Additive Errors”, 1976, International Economic Review, pages 506-509.

4.2 “An Alternative Proof and Generalization of a Test for Structural Change”, 1976, American Statistician, pages 122-123.

4.3 “Some Comments on Multicollinearity in Regression”, 1977, Applied Statistics, pages 188-191.

4.4 “On the Unbiasedness of Robust Regression Estimators”, 1978, Communications in Statistics A, 779-783.

A simple proof of the unbiasedness of L -norm and M-estimators is given for

regression models


http://www.informaworld.com/smpp/content~db=all~content=a780015108~frm=abslink 4.5 “A Note on Testing for Gaps in Seasonal Moving Average Models” (with J

Tomenson), 1981, Journal of the Royal Statistical Society. Series B, pages 240-243.

4.6 “A Note on Estimating and Testing Exogenous Variable Coefficient Estimators in Simultaneous Equation Models” (with G D A Phillips), 1984, Economic Letters, volume 15, pages 301-7.

The exact distribution of 2SLS estimators of exogenous variable parameters is derived

conditional on the endogenous parameter estimates. An approximate signific ance test

is then developed for which lower bounds for the critical values are found from the F

distribution.





http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V84-45DMSV9-DM&_user=10&_coverDate=12%2F31%2F1984&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612505326&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=8c9930a7db6fdb884805c5f0f692b21b&searchtype=a 4.7 “A Note on the Estimation of Variances in State Space Models Using the

Maximum A Posteriori Procedure” (with S Peters), 1985, IEEE Trans. on Automatic Control, AC-30, pages 1048-1050.

This note examines an estimation procedure for the unknown parameters in a state-

space model proposed by Tsang, Glover, and Bach. The method is based on the

maximum a posteriori (MAP) principle. Contrary to the claims of Tsang et al. it is

shown that the algorithm performs very poorly compared to maximum likelihood.

Some insight into the shortcomings of the MAP procedure is obtained by comparing it

to the computation of maximum likelihood estimators by the EM algorithm.Full text

available on-line at:

http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F9%2F24224%2F01103813.pdf%3Farnumber%3D1103813&authDecision=-203 4.8 “Further Comments on Stationarity Tests in Series with Structural Breaks

at Unknown Points”, 2003, Journal of Time Series Analysis. Volume 24, pages 137-40.




4.9 “A note on common cycles, common trends and convergence “(with V Carvalho and T Trimbur). 2007, Journal of Business and Economic Statistics, volume 25, pages 12-20.

This article compares and contrasts structural time series models and the common

features methodology. The way in which trends are handled is highlighted by

describing a recent structural time series model that allows convergence to a common

growth path. Post sample data are used to test its forecasting performance for income

per head in U.S. regions. A test for common cycles is proposed, its asymptotic

distribution is given, and small-sample properties are studied by Monte Carlo

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V84-45DMSV9-DM&_user=10&_coverDate=12%2F31%2F1984&_rdoc=1&_fmt=high&_orig=search&_origin=search&_sort=d&_docanchor=&view=c&_searchStrId=1612505326&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=8c9930a7db6fdb884805c5f0f692b21b&searchtype=a





http://ieeexplore.ieee.org/Xplore/login.jsp?url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F9%2F24224%2F01103813.pdf%3Farnumber%3D1103813&authDecision=-203





experiments. Applications are presented, with special attention given to the

implications of using higher-order cycles.


http://www.crei.cat/people/carvalho/carvalho_anote.pdf

http://www.crei.cat/people/carvalho/carvalho_anote.pdf

Focus on: Andrew Harvey September 2011

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