Econometrics - Wikipedia, The Free Encyclopedia

EconometricsFrom Wikipedia, the free encyclopedia

Econometrics is the application of mathematics, statistical methods, and, more recently, computerscience, to economic data and is described as the branch of economics that aims to give empiricalcontent to economic relations.[1] More precisely, it is "the quantitative analysis of actual economicphenomena based on the concurrent development of theory and observation, related by appropriatemethods of inference."[2] An introductory economics textbook describes econometrics as allowingeconomists "to sift through mountains of data to extract simple relationships."[3] The first known use ofthe term "econometrics" (in cognate form) was by Pawe Ciompa in 1910. Ragnar Frisch is credited withcoining the term in the sense that it is used today.[4]

Econometrics is the intersection of economics, mathematics, and statistics. Econometrics adds empiricalcontent to economic theory allowing theories to be tested and used for forecasting and policyevaluation.[5]

Contents

1 Basic econometric models: linear regression2 Theory

2.1 GaussMarkov theorem2.1.1 Linearity2.1.2 Expected error is zero2.1.3 Spherical errors2.1.4 Exogeneity of independent variables2.1.5 Full rank

3 Methods3.1 Experimental economics3.2 Data3.3 Instrumental variables3.4 Computational methods3.5 Structural econometrics

4 Example5 Journals6 Limitations and criticisms7 Notable econometricians8 See also9 Notes10 References11 Further reading12 External links

Basic econometric models: linear regression

Econometrics - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Econometrics

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Okun's law representing therelationship between GDP growth andthe unemployment rate. The fitted lineis found using regression analysis.

The basic tool for econometrics is the linear regression model. In modern econometrics, other statisticaltools are frequently used, but linear regression is still the most frequently used starting point for ananalysis.[6] Estimating a linear regression on two variables can be visualized as fitting a line through datapoints representing paired values of the independent and dependent variables.

For example, consider Okun's law, which relates GDP growth tothe unemployment rate. This relationship is represented in alinear regression where the change in unemployment rate (

) is a function of an intercept ( ), agiven value of GDP growth multiplied by a slope coefficient and an error term, :

The unknown parameters and can be estimated. Here isestimated to be 1.77 and is estimated to be 0.83. This meansthat if GDP growth increased by one percentage point, theunemployment rate would be predicted to drop by .94 points(1.77*1+0.83). The model could then be tested for statisticalsignificance as to whether an increase in growth is associatedwith a decrease in the unemployment, as hypothesized. If the estimate of were not significantlydifferent from 0, the test would fail to find evidence that changes in the growth rate and unemploymentrate were related.

Theory

See also: Estimation theory

Econometric theory uses statistical theory to evaluate and develop econometric methods.Econometricians try to find estimators that have desirable statistical properties including unbiasedness,efficiency, and consistency. An estimator is unbiased if its expected value is the true value of theparameter; It is consistent if it converges to the true value as sample size gets larger, and it is efficient ifthe estimator has lower standard error than other unbiased estimators for a given sample size. Ordinaryleast squares (OLS) is often used for estimation since it provides the BLUE or "best linear unbiasedestimator" (where "best" means most efficient, unbiased estimator) given the Gauss-Markovassumptions. When these assumptions are violated or other statistical properties are desired, otherestimation techniques such as maximum likelihood estimation, generalized method of moments, orgeneralized least squares are used. Estimators that incorporate prior beliefs are advocated by those whofavor Bayesian statistics over traditional, classical or "frequentist" approaches.

GaussMarkov theorem

The GaussMarkov theorem shows that the OLS estimator is the best (minimum variance), unbiasedestimator assuming the model is linear, the expected value of the error term is zero, errors arehomoskedastic and not autocorrelated, and there is no perfect multicollinearity.

Linearity


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The dependent variable is assumed to be a linear function of the variables specified in the model. Thespecification must be linear in its parameters. This does not mean that there must be a linear relationshipbetween the independent and dependent variables. The independent variables can take non-linear formsas long as the parameters are linear. The equation qualifies as linear while

can be transformed to be linear by replacing (beta)^2 by another parameter, say gamma.An equation with a parameter dependent on an independent variable does not qualify as linear, forexample y = alpha + beta(x) * x, where beta(x) is a function of x.

Data transformations are often used to convert an equation into a linear form (see, however, Santos Silvaand Tenreyro, 2006). For example, the CobbDouglas functionoften used in economicsis nonlinear:

But it can be expressed in linear form by taking the natural logarithm of both sides:[7]

This assumption also covers specification issues: assuming that the proper functional form has beenselected and there are no omitted variables.

Expected error is zero

The expected value of the error term is assumed to be zero. This assumption can be violated if themeasurement of the dependent variable is consistently positive or negative. The mis-measurement willbias the estimation of the intercept parameter, but the slope parameters will remain unbiased.[8]

The intercept may also be biased if there is a logarithmic transformation. See the Cobb-Douglas equationabove. The multiplicative error term will not have a mean of 0, so this assumption will be violated.[9]

This assumption can also be violated in limited dependent variable models. In such cases, both theintercept and slope parameters may be biased.[10]

Spherical errors

Error terms are assumed to be spherical otherwise the OLS estimator is inefficient. The OLS estimatorremains unbiased, however. Spherical errors occur when errors have both uniform variance(homoscedasticity) and are uncorrelated with each other.[11] The term "spherical errors" will describethe multivariate normal distribution: if in the multivariate normal density, then theequation f(x)=c is the formula for a ball centered at with radius in n-dimensional space.[12]

Heteroskedacity occurs when the amount of error is correlated with an independent variable. Forexample, in a regression on food expenditure and income, the error is correlated with income. Lowincome people generally spend a similar amount on food, while high income people may spend a very


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large amount or as little as low income people spend. Heteroskedacity can also be caused by changes inmeasurement practices. For example, as statistical offices improve their data, measurement errordecreases, so the error term declines over time.

This assumption is violated when there is autocorrelation. Autocorrelation can be visualized on a dataplot when a given observation is more likely to lie above a fitted line if adjacent observations also lieabove the fitted regression line. Autocorrelation is common in time series data where a data series mayexperience "inertia."[13] If a dependent variable takes a while to fully absorb a shock. Spatialautocorrelation can also occur geographic areas are likely to have similar errors. Autocorrelation may bethe result of misspecification such as choosing the wrong functional form. In these cases, correcting thespecification is one possible way to deal with autocorrelation.

In the presence of non-spherical errors, the generalized least squares estimator can be shown to beBLUE.[14]

Exogeneity of independent variables

This assumption is violated if the variables are endogenous. Endogeneity can be the result ofsimultaneity, where causality flows back and forth between both the dependent and independentvariable. Instrumental variable techniques are commonly used to address this problem.

Full rank

The sample data matrix must have full rank or OLS cannot be estimated. There must be at least oneobservation for every parameter being estimated and the data cannot have perfect multicollinearity.[15]

Perfect multicollinearity will occur in a "dummy variable trap" when a base dummy variable is notomitted resulting in perfect correlation between the dummy variables and the constant term.

Multicollinearity (as long as it is not "perfect") can be present resulting in a less efficient, but stillunbiased estimate.

Methods

See also: Methodology of econometrics

Applied econometrics uses theoretical econometrics and real-world data for assessing economic theories,developing econometric models, analyzing economic history, and forecasting.[16]

Econometrics may use standard statistical models to study economic questions, but most often they arewith observational data, rather than in controlled experiments. In this, the design of observational studiesin econometrics is similar to the design of studies in other observational disciplines, such as astronomy,epidemiology, sociology and political science. Analysis of data from an observational study is guided bythe study protocol, although exploratory data analysis may by useful for generating new hypotheses.[17]

Economics often analyzes systems of equations and inequalities, such as supply and demand


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hypothesized to be in equilibrium. Consequently, the field of econometrics has developed methods foridentification and estimation of simultaneous-equation models. These methods are analogous to methodsused in other areas of science, such as the field of system identification in systems analysis and controltheory. Such methods may allow researchers to estimate models and investigate their empiricalconsequences, without directly manipulating the system.

One of the fundamental statistical methods used by econometricians is regression analysis.[18]

Regression methods are important in econometrics because economists typically cannot use controlledexperiments. Econometricians often seek illuminating natural experiments in the absence of evidencefrom controlled experiments. Observational data may be subject to omitted-variable bias and a list ofother problems that must be addressed using causal analysis of simultaneous-equation models.[19]

Experimental economics

In recent decades, econometricians have increasingly turned to use of experiments to evaluate the often-contradictory conclusions of observational studies. Here, controlled and randomized experimentsprovide statistical inferences that may yield better empirical performance than do purely observationalstudies.[20]

Data

Data sets to which econometric analyses are applied can be classified as time-series data, cross-sectionaldata, panel data, and multidimensional panel data. Time-series data sets contain observations over time;for example, inflation over the course of several years. Cross-sectional data sets contain observations ata single point in time; for example, many individuals' incomes in a given year. Panel data sets containboth time-series and cross-sectional observations. Multi-dimensional panel data sets contain observationsacross time, cross-sectionally, and across some third dimension. For example, the Survey of ProfessionalForecasters contains forecasts for many forecasters (cross-sectional observations), at many points in time(time series observations), and at multiple forecast horizons (a third dimension).

Instrumental variables

In many econometric contexts, the commonly-used ordinary least squares method may not recover thetheoretical relation desired or may produce estimates with poor statistical properties, because theassumptions for valid use of the method are violated. One widely used remedy is the method ofinstrumental variables (IV). For an economic model described by more than one equation, simultaneous-equation methods may be used to remedy similar problems, including two IV variants, Two-Stage LeastSquares (2SLS), and Three-Stage Least Squares (3SLS).[21]

Computational methods

Computational concerns are important for evaluating econometric methods and for use in decisionmaking.[22] Such concerns include mathematical well-posedness: the existence, uniqueness, and stabilityof any solutions to econometric equations. Another concern is the numerical efficiency and accuracy ofsoftware.[23] A third concern is also the usability of econometric software.[24]


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Structural econometrics

Structural econometrics extends the ability of researchers to analyze data by using economic models asthe lens through which to view the data. The benefit of this approach is that any policy recommendationsare not subject to the Lucas critique since counter-factual analyses take an agent's re-optimization intoaccount. Structural econometric analyses begin with an economic model that captures the salientfeatures of the agents under investigation. The researcher then searches for parameters of the model thatmatch the outputs of the model to the data. There are two ways of doing this. The first requires theresearcher to completely solve the model and then use maximum likelihood.[25] However, there havebeen many advances that can bypass the full solution of the model and that estimate models in twostages. Importantly, these methods allow the researcher to consider more complicated models withstrategic interactions and multiple equilibria.[26]

A good example of structural econometrics is in the estimation of first price sealed bid auctions withindependent private values.[27] The key difficulty with bidding data from these auctions is that bids onlypartially reveal information on the underlying valuations, bids shade the underlying valuations. Onewould like to estimate these valuations in order to understand the magnitude of profits each biddermakes. More importantly, it is necessary to have the valuation distribution in hand to engage inmechanism design. In a first price sealed bid auction the expected payoff of a bidder is given by:

where v is the bidder valuation, b is the bid. The optimal bid solves a first order condition:

which can be re-arranged to yield the following equation for

Notice that the probability that a bid wins an auction can be estimated from a data set of completedauctions, where all bids are observed. This can be done using simple non-parametric estimators. If allbids are observed, it is then possible to use the above relation and the estimated probability function andits derivative to point wise estimate the underlying valuation. This will then allow the investigator toestimate the valuation distribution.

Example

A simple example of a relationship in econometrics from the field of labor economics is:

This example assumes that the natural logarithm of a person's wage is a linear function of the number ofyears of education that person has acquired. The parameter measures the increase in the natural log


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of the wage attributable to one more year of education. The term is a random variable representing allother factors that may have direct influence on wage. The econometric goal is to estimate theparameters, under specific assumptions about the random variable . For example, if isuncorrelated with years of education, then the equation can be estimated with ordinary least squares.

If the researcher could randomly assign people to different levels of education, the data set thusgenerated would allow estimation of the effect of changes in years of education on wages. In reality,those experiments cannot be conducted. Instead, the econometrician observes the years of education ofand the wages paid to people who differ along many dimensions. Given this kind of data, the estimatedcoefficient on Years of Education in the equation above reflects both the effect of education on wagesand the effect of other variables on wages, if those other variables were correlated with education. Forexample, people born in certain places may have higher wages and higher levels of education. Unless theeconometrician controls for place of birth in the above equation, the effect of birthplace on wages maybe falsely attributed to the effect of education on wages.

The most obvious way to control for birthplace is to include a measure of the effect of birthplace in theequation above. Exclusion of birthplace, together with the assumption that is uncorrelated witheducation produces a misspecified model. Another technique is to include in the equation additional setof measured covariates which are not instrumental variables, yet render identifiable.[28] An overviewof econometric methods used to study this problem were provided by Card (1999).[29]

Journals

The main journals which publish work in econometrics are Econometrica, the Journal of Econometrics,the Review of Economics and Statistics, Econometric Theory, the Journal of Applied Econometrics,Econometric Reviews, the Econometrics Journal,[30] Applied Econometrics and InternationalDevelopment, the Journal of Business & Economic Statistics, and the Journal of Economic and SocialMeasurement (http://www.iospress.nl/html/07479662.php).

Limitations and criticisms

See also: Criticisms of econometrics

Like other forms of statistical analysis, badly specified econometric models may show a spuriouscorrelation where two variables are correlated but causally unrelated. In a study of the use ofeconometrics in major economics journals, McCloskey concluded that economists report p values(following the Fisherian tradition of tests of significance of point null-hypotheses), neglecting concernsof type II errors; economists fail to report estimates of the size of effects (apart from statisticalsignificance) and to discuss their economic importance. Economists also fail to use economic reasoningfor model selection, especially for deciding which variables to include in a regression.[31][32]

In some cases, economic variables cannot be experimentally manipulated as treatments randomlyassigned to subjects.[33] In such cases, economists rely on observational studies, often using data setswith many strongly associated covariates, resulting in enormous numbers of models with similarexplanatory ability but different covariates and regression estimates. Regarding the plurality of modelscompatible with observational data-sets, Edward Leamer urged that "professionals ... properly withhold


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belief until an inference can be shown to be adequately insensitive to the choice of assumptions".[34]

Economists from the Austrian School argue that aggregate economic models are not well suited todescribe economic reality because they waste a large part of specific knowledge. Friedrich Hayek in hisThe Use of Knowledge in Society argued that "knowledge of the particular circumstances of time andplace" is not easily aggregated and is often ignored by professional economists.[35][36]

Notable econometricians

Sir James BallJames DurbinWilliam GreeneLars Peter HansenJerry HausmanFumio HayashiDavid Forbes HendryJames HeckmanLawrence KleinCEV LeserHalbert White

See also

Augmented DickeyFuller testChoice modellingCowles FoundationEconometric softwareFinancial modelingGranger causalityImportant publications in econometricsMacroeconomic modelMethodological individualismPredetermined variablesSingle equation methods (econometrics)Spatial econometricsUnit root

Notes^ M. Hashem Pesaran (1987). "Econometrics," The New Palgrave: A Dictionary of Economics, v. 2, p. 8[pp. 8-22]. Reprinted in J. Eatwell et al., eds. (1990). Econometrics: The New Palgrave, p. 1(http://books.google.com/books?id=gBsgr7BPJsoC&dq=econometrics&printsec=find&pg=PA1=false#v=onepage&q&f=false) [pp. 1-34]. Abstract (http://www.dictionaryofeconomics.com/article?id=pde2008_E000007&edition=current&q=Econometrics&topicid=&result_number=2) (2008revision by J. Geweke, J. Horowitz, and H. P. Pesaran).

1.


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^ P. A. Samuelson, T. C. Koopmans, and J. R. N. Stone (1954). "Report of the Evaluative Committee forEconometrica," Econometrica 22(2), p. 142. [p p. 141 (http://www.jstor.org/pss/1907538)-146], asdescribed and cited in Pesaran (1987) above.

2.

^ Paul A. Samuelson and William D. Nordhaus, 2004. Economics. 18th ed., McGraw-Hill, p. 5.3.^ H. P. Pesaran (1990), "Econometrics," Econometrics: The New Palgrave, p. 2 (http://books.google.com/books?id=gBsgr7BPJsoC&dq=econometrics&printsec=find&pg=PA2=false#v=onepage&q&f=false), citingRagnar Frisch (1936), "A Note on the Term 'Econometrics'," Econometrica, 4(1), p. 95. Aris Spanos (2008), "statistics and economics," The New Palgrave Dictionary of Economics, 2ndEdition. Abstract. (http://www.dictionaryofeconomics.com/article?id=pde2008_S000502&edition=current&q=statistics&topicid=&result_number=1)

4.

^ Geweke, Horowitz & Pesaran 2008.5.^ Greene (2012), 12.6.^ Kennedy 2003, p. 110.7.^ Kennedy 2003, p. 129.8.^ Kennedy 2003, p. 131.9.^ Kennedy 2003, p. 130.10.^ Kennedy 2003, p. 133.11.^ Greene 2012, p. 23-note.12.^ Greene 2010, p. 22.13.^ Kennedy 2003, p. 135.14.^ Kennedy 2003, p. 205.15.^ Clive Granger (2008). "forecasting," The New Palgrave Dictionary of Economics, 2nd Edition. Abstract.(http://www.dictionaryofeconomics.com/article?id=pde2008_F000161&edition=current&q=forecast&topicid=&result_number=7)

16.

^ Herman O. Wold (1969). "Econometrics as Pioneering in Nonexperimental Model Building,"Econometrica, 37(3), pp. 369 (http://www.jstor.org/pss/1912787)-381.

17.

^ For an overview of a linear implementation of this framework, see linear regression.18.^ Edward E. Leamer (2008). "specification problems in econometrics," The New Palgrave Dictionary ofEconomics. Abstract. (http://www.dictionaryofeconomics.com/article?id=pde2008_S000200&edition=current&q=Specification%20problems%20in%20econometrics&topicid=&result_number=1)

19.

^ H. Wold 1954. "Causality and Econometrics," Econometrica, 22(2), p p. 162 (http://www.jstor.org/pss/1907540)-177. Kevin D. Hoover (2008). "causality in economics and econometrics," The New Palgrave Dictionary ofEconomics, 2nd Edition. Abstract (http://www.dictionaryofeconomics.com/article?id=pde2008_C000569&q=experimental%20methods%20in%20economics&topicid=&result_number=11) and galley proof.(http://econ.duke.edu/~kdh9/Source%20Materials/Research/Palgrave_Causality_Final.pdf)

20.

^ Peter Kennedy (economist) (2003). A Guide to Econometrics, 5th ed. Description (http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=9577), preview (http://books.google.com/books?id=B8I5SP69e4kC&printsec=find&pg=PR5=gbs_atb#v=onepage&q&f=false), and TOC(http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=9577&mode=toc), ch. 9, 10, 13, and 18.

21.

^ Keisuke Hirano (2008). "decision theory in econometrics," The New Palgrave Dictionary ofEconomics, 2nd Edition. Abstract (http://www.dictionaryofeconomics.com/article?id=pde2008_D000244&edition=current&q=Computational%20economics&topicid=&result_number=19). James O. Berger (2008). "statistical decision theory," The New Palgrave Dictionary of Economics, 2ndEdition. Abstract. (http://www.dictionaryofeconomics.com/article?id=pde2008_S000251&edition=&field=keyword&q=statistical%20decision%20theory&topicid=&result_number=1)

22.

^ B. D. McCullough and H. D. Vinod (1999). "The Numerical Reliability of Econometric Software,"Journal of Economic Literature, 37(2), pp. 633-665 (http://www.pages.drexel.edu/~bdm25/jel.pdf).

23.


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^ Vassilis A. Hajivassiliou (2008). "computational methods in econometrics," The New PalgraveDictionary of Economics, 2nd Edition. Abstract. (http://www.dictionaryofeconomics.com/article?id=pde2008_C000559&edition=current&q=&result_number=1) Richard E. Quandt (1983). "Computational Problems and Methods," ch. 12, in Handbook ofEconometrics, v. 1, pp. 699 (http://www.sciencedirect.com/science/article/pii/S1573441283010168)-764. Ray C. Fair (1996). "Computational Methods for Macroeconometric Models," Handbook ofComputational Economics, v. 1, pp. [1] (http://www.sciencedirect.com/science/article/pii/S1574002196010052143)-169.

24.

^ Rust, John (1987). "Optimal Replacement of GMC Bus Engines: An Empirical Model of HaroldZurcher". Econometrica 55 (5): 9991033. JSTOR 1911259 (//www.jstor.org/stable/1911259).

25.

^ Hotz, V. Joseph; Miller, Robert A. (1993). "Conditional Choice Probabilities and the Estimation ofDynamic Models". Review of Economic Studies 60 (3): 497529. JSTOR 2298122 (//www.jstor.org/stable/2298122).

26.

^ Guerre, E.; Perrigne, I.; Vuong, Q. (2000). "Optimal Nonparametric Estimation of First Price Auctions".Econometrica 68 (3): 525574. doi:10.1111/1468-0262.00123 (http://dx.doi.org/10.1111%2F1468-0262.00123).

27.

^ Pearl, Judea (2000). Causality: Model, Reasoning, and Inference. Cambridge University Press.ISBN 0521773628.

28.

^ Card, David (1999). "The Causal Effect of Education on Earning". In Ashenfelter, O.; Card, D.Handbook of Labor Economics. Amsterdam: Elsevier. pp. 18011863. ISBN 0444822895.

29.

^ "The Econometrics Journal - Wiley Online Library" (http://www.wiley.com/bw/journal.asp?ref=1368-4221). Wiley.com. Retrieved 2013-10-08.

30.

^ McCloskey (May 1985). "The Loss Function has been mislaid: the Rhetoric of Significance Tests".American Economic Review 75 (2).

31.

^ Stephen T. Ziliak and Deirdre N. McCloskey (2004). "Size Matters: The Standard Error of Regressions inthe American Economic Review," Journal of Socio-economics, 33(5), pp. 527-46(http://faculty.roosevelt.edu/Ziliak/doc/Size%20Matters%20Journal%20of%20Socio-Economics%20Ziliak%20and%20McCloskey.pdf) (press +).

32.

^ Leamer, Edward (March 1983). "Let's Take the Con out of Econometrics" (http://www.jstor.org/pss/1803924). American Economic Review 73 (1): 34.

33.

^ Leamer, Edward (March 1983). "Let's Take the Con out of Econometrics" (http://www.jstor.org/pss/1803924). American Economic Review 73 (1): 43.

34.

^ Robert F. Garnett. What Do Economists Know? New Economics of Knowledge. Routledge, 1999. ISBN978-0-415-15260-0. p. 170

35.

^ G. M. P. Swann. Putting Econometrics in Its Place: A New Direction in Applied Economics. EdwardElgar Publishing, 2008. ISBN 978-1-84720-776-0. p. 62-64

36.

References

Handbook of Econometrics Elsevier. Links to volume chapter-preview links: Zvi Griliches and Michael D. Intriligator, ed. (1983). v. 1 (http://www.sciencedirect.com/science/handbooks/15734412/1); (1984),v. 2 (http://www.sciencedirect.com/science/handbooks/15734412/2); (1986), description (http://www.elsevier.com/wps/find/bookdescription.cws_home/601080/description#description), v. 3 (http://www.sciencedirect.com/science/handbooks/15734412/3); (1994), description (http://www.elsevier.com/wps/find/bookdescription.cws_home/601081/description#description), v. 4 (http://www.sciencedirect.com/science/handbooks/15734412/4) Robert F. Engle and Daniel L. McFadden, ed. (2001).Description (http://www.elsevier.com


10 of 13 15/03/2014 15:34

/wps/find/bookdescription.cws_home/601082/description#description), v. 5(http://www.sciencedirect.com/science/handbooks/15734412/5) James J. Heckman and Edward E. Leamer, ed. (2007). Description (http://www.elsevier.com/wps/find/bookdescription.cws_home/712946/description#description), v. 6A(http://www.sciencedirect.com/science/handbooks/15734412/6/part/PA) & v. 6B(http://www.sciencedirect.com/science/handbooks/15734412/6/part/PB)Handbook of Statistics, v. 11, Econometrics (1993), Elsevier. Links to first-page chapter previews.(http://www.sciencedirect.com/science/handbooks/01697161/11)International Encyclopedia of the Social & Behavioral Sciences (2001), Statistics, "Econometricsand Time Series," links (http://www.sciencedirect.com/science?_ob=RefWorkIndexURL&_idxType=SC&_cdi=23486&_refWorkId=21&_explode=151000377,151000380&_alpha=&_acct=C000050221&_version=1&_userid=10&md5=10d43da5ed3104bf3d8bb99f72c80e11&refID=151000380#151000380) to first-page previews of 21 articles.Angrist, Joshua & Pischke, JrnSteffen (2010). "The Credibility Revolution in EmpiricalEconomics: How Better Research Design Is Taking the Con out of Econometrics], 24(2), ,pp. 330. Abstract. (http://www.ingentaconnect.com/content/aea/jep/2010/00000024/00000002/art00001)Eatwell, John, et al., eds. (1990). Econometrics: The New Palgrave. Article-preview links(http://books.google.com/books?id=gBsgr7BPJsoC&dq=econometrics&printsec=find&pg=PR5=false#v=onepage&q&f=false) (from The New Palgrave: A Dictionary of Economics,1987).Geweke, John; Horowitz, Joel; Pesaran, Hashem (2008). "Econometrics"(http://www.dictionaryofeconomics.com.proxyau.wrlc.org/article?id=pde2008_E000007). InDurlauf, Steven N.; Blume, Lawrence E. The New Palgrave Dictionary of Economics (PalgraveMacmillan). doi:10.1057/9780230226203.0425 (http://dx.doi.org/10.1057%2F9780230226203.0425).Greene, William H. (1999, 4th ed.) Econometric Analysis, Prentice Hall.Hayashi, Fumio. (2000) Econometrics, Princeton University Press. ISBN 0-691-01018-8Description and contents links. (http://press.princeton.edu/titles/6946.html)Hamilton, James D. (1994) Time Series Analysis, Princeton University Press. Description(http://press.princeton.edu/titles/5386.html) and preview. (http://books.google.com/books/p/princeton?id=B8_1UBmqVUoC&printsec=frontcover&cd=1&source=gbs_ViewAPI&hl=en#v=onepage&q&f=false)Hughes Hallett, Andrew J. "Econometrics and the Theory of Economic Policy: TheTinbergen-Theil Contributions 40 Years On," Oxford Economic Papers (1989) 41#1 pp 189214Kelejian, Harry H., and Wallace E. Oates (1989, 3rd ed.) Introduction to Econometrics.Kennedy, Peter (2003). A guide to econometrics. Cambridge, Mass: MIT Press.ISBN 978-0-262-61183-1.Russell Davidson and James G. MacKinnon (2004). Econometric Theory and Methods. NewYork: Oxford University Press. Description. (http://www.oup.com/us/catalog/general/subject/Economics/Econometrics/~~/dmlldz11c2EmY2k9OTc4MDE5NTEyMzcyMg==?view=usa&ci=9780195123722#Description)Mills, Terence C., and Kerry Patterson, ed. Palgrave Handbook of Econometrics:

(2007) v. 1: Econometric Theoryv. 1. Links (http://www.palgrave.com/products/title.aspx?pid=269866) to description and contents.(2009) v. 2, Applied Econometrics. Palgrave Macmillan. ISBN 978-1-4039-1799-7 Links(http://www.palgrave.com/products/title.aspx?PID=267962) to description and contents.


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Pearl, Judea (2009, 2nd ed.). Causality: Models, Reasoning and Inference, Cambridge UniversityPress, Description (http://books.google.com/books?id=wnGU_TsW3BQC&source=gbs_navlinks_s), TOC (http://bayes.cs.ucla.edu/BOOK-09/book-toc-final.pdf), andpreview, ch. 1-10 (http://books.google.com/books?id=wnGU_TsW3BQC&printsec=find&pg=PR7=gbs_atb#v=onepage&q&f=false) and ch. 11 (http://bayes.cs.ucla.edu/BOOK-09/ch11-toc-plus-p331-final.pdf). 5 economics-journal reviews (http://bayes.cs.ucla.edu/BOOK-2K/),including Kevin D. Hoover, Economics Journal.Pindyck, Robert S., and Daniel L. Rubinfeld (1998, 4th ed.). Econometric Methods and EconomicForecasts, McGraw-Hill.Santos Silva, J.M.C. and Tenreyro, Silvana (2006), The Log of Gravity, The Review ofEconomics and Statistics, 88(4), pp. 641658. Studenmund, A.H. (2011, 6th ed.). Using Econometrics: A Practical Guide. Contents (chapter-preview) links. (http://www.coursesmart.com/9780131367760/chap01)Wooldridge, Jeffrey (2003). Introductory Econometrics: A Modern Approach. Mason: ThomsonSouth-Western. ISBN 0-324-11364-1 Chapter-preview links in brief (http://books.google.com/books?id=64vt5TDBNLwC&printsec=find&pg=PR3=gbs_atb#v=onepage&q&f=false) anddetail. (http://books.google.com/books?id=64vt5TDBNLwC&printsec=find&pg=PR4=gbs_atb#v=onepage&q&f=false)

Further reading

Econometric Theory book on WikibooksGiovanini, Enrico Understanding Economic Statistics (http://www.oecd.org/statistics/understandingeconomicstatistics), OECD Publishing, 2008, ISBN 978-92-64-03312-2

External links

Journal of Financial Econometrics (http://jfec.oxfordjournals.org/)Econometric Society (http://www.econometricsociety.org)The Econometrics Journal (http://www.ectj.org)Econometric Links (http://www.econometriclinks.com)Teaching Econometrics (http://www.economicsnetwork.ac.uk/subjects/econometrics.htm) (Indexby the Economics Network (UK))Applied Econometric Association (http://www.aea-eu.com)The Society for Financial Econometrics (http://sofie.stern.nyu.edu/)The interview with Clive Granger - Nobel winner in 2003, about econometrics(http://philpapers.org/rec/BRACAI-3)

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