Forecasting Inflation and GDP Growth: Automatic Leading ......ERD TECHNICAL NOTE NO. 18 FORECASTING...

Forecasting Inflation and GDP Growth: Automatic Leading Indicator (ALI) Method versus Macro Econometric Structural Models (MESMs)


Duo Qin, Marie Anne Cagas, Geoffrey Ducanes, Nedelyn Magtibay-Ramos, and Pilipinas Quising

Printed in the Philippines

Technical Note SeriesECONOMICS AND RESEARCH DEPARTMENTERD

No.18July 2006

Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippineswww.adb.org/economicsISSN: 1655-5236Publication Stock No.

About the Asian Development Bank

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Duo Qin, Marie Anne Cagas, Geoffrey Ducanes, Nedelyn Magtibay-Ramos, and Pilipinas Quising compare the forecast performance of the automatic leading indicator (ALI) method with the macro econometric structural model (MESM) and seek ways of improving the ALI method. The ALI method is found to produce better forecasts than MESMs in general, but the method is found to involve greater uncertainty in choosing indicators, mixing data frequencies, and utilizing unrestricted vector auto-regressions. Two possible improvements are found to reduce the uncertainty.

ERD TECHNICAL NOTE NO. 18

FORECASTING INFLATION AND GDP GROWTH:AUTOMATIC LEADING INDICATOR (ALI)METHOD VERSUS MACRO ECONOMETRIC

STRUCTURAL MODELS (MESMS)

DUO QIN, MARIE ANNE CAGAS, GEOFFREY DUCANES,NEDELYN MAGTIBAY-RAMOS, AND PILIPINAS QUISING

July 2006

Duo Qin is an economist, Marie Anne Cagas and Geofrrey Ducanes are consultants, and Nedelyn Magtibay-Ramosand Pilipinas Quising are economics officers at the Macroeconomics and Finance Research Division, Economicsand Research Department, Asian Development Bank. This research stems from a project carried out by James Mitchellfor the Asian Development Bank. The authors are grateful for the technical help that Mitchell has provided.

Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippineswww.adb.org/economics

©2006 by Asian Development BankJuly 2006ISSN 1655-5236

The views expressed in this paperare those of the author(s) and do notnecessarily reflect the views or policiesof the Asian Development Bank.

FOREWORD

The ERD Technical Note Series deals with conceptual, analytical, ormethodological issues relating to project/program economic analysis orstatistical analysis. Papers in the Series are meant to enhance analytical rigorand quality in project/program preparation and economic evaluation, andimprove statistical data and development indicators. ERD Technical Notesare prepared mainly, but not exclusively, by staff of the Economics andResearch Department, their consultants, or resource persons primarily forinternal use, but may be made available to interested external parties.

CONTENTS

Abstract vii

1. Introduction 1

II. Models, Choice of ALI Indicators, Forecast Variables,and Scenarios for Comparison 3

A. Automatic Leading Indicator 3B. Indicators 4C. Modeling Consumer Price Index and Gross Domestic Product

in MESMs 4D. Forecast Variables and Comparison Statistics 5

III. Comparison of Forecast Results 8

A. Short-term Forecast Comparison 8B. Longer-term Forecast Comparison 11C. Comparison of Forecast Methods 11

IV. Modified ALI Method 16

V. Conclusion 18

Practitioner’s Note: Step-by-Step Menu of doing the ALI 26

References 27

ABSTRACT

This paper compares the forecast performance of the automatic leadingindicator (ALI) method with the macro econometric structural model (MESM)and seeks ways of improving the ALI method. Inflation and gross domesticproduct growth form the forecast objects for comparison, using data fromPeople’s Republic of China, Indonesia, and Philippines. The ALI method isfound to produce better forecasts than MESMs in general, but the methodis found to involve greater uncertainty in choosing indicators, mixing datafrequencies, and utilizing unrestricted vector auto-regressions. Two possibleimprovements are found helpful to reduce the uncertainty: (i) give theorypriority in choosing indicators and include theory-based disequilibrium shocksin the indicator sets; and (ii) reduce the vector auto-regressions by meansof the general → specific model reduction procedure.

The fox knows many things, but the hedgehog knows one big thing.

Archilochus

1. INTRODUCTION

Accurate and timely information on the current conditions of an economy is needed forgood economic policy making. Unfortunately, many countries face the perennial problem ofscarce macroeconomic data, often released with considerable delay and many at low frequency.To address this problem, conventionally, structural econometric models have been and stillare used widely to forecast key macroeconomic variables as well as to do policy simulations.These models are constrained, however, to use data of the same frequency—either quarterlyor annual—and at the same aggregative level, which is determined by a priori theories. Asmore and more micro and financial data become available at higher frequencies, alternativeprocedures have been explored that can better utilize various kinds of available data to extractthe key signals timely and efficiently. This is best reflected in the recently mounting interestin dynamic factor models.

Although economic leading indicators were developed nearly a century ago and factoranalysis was used in economics as early as the 1940s,1 these methods were marginalized ineconometric research for decades. The recent revival of leading indicator models is largelydue to the work of Stock and Watson (1989 and 1991), who proposed to extract, by meansof dynamic factor analysis, from a large pool of variables a latent “leading indicator”, or an“index of coincident indicators” as they call it, for the United States economy.2

The “automatic leading indicator” (ALI) model proposed by Camba-Mendez et al. (2001)makes use of very similar techniques as in Stock and Watson (1989).3 However, the angleof application has been reoriented. Camba-Mendez et al. (2001) focus their attention on short-term forecasts of certain officially released variables of interest, e.g., real GDP growth ofselected European countries.4 These variables are excluded from the pool of variables fromwhich a few dynamic factors are extracted. These factors are then used as forcing variablesin forecasting the variables of interest by means of a vector auto-regression (VAR) model,instead of producing one unobserved core index of the economy.

1 W. M. Persons is known as the pioneer of leading indicators; F. V. Waugh and J. R. N. Stone are among the first toapply factor analysis to economic data. See (Gilbert and Qin (2006) for the history of these econometric methods.

2 For a recent survey of dynamic factor models (DFMs), see Stock and Watson (2005).3 According to the authors, the model derives its name from the fact that the information is selected automatically from

the set of indicators.4 Another example is to forecast inflation in the United Kingdom by Kapetanios (2002).

22222 JJJJJULULULULULYYYYY 2006 2006 2006 2006 2006

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MMMMMACROACROACROACROACRO E E E E ECONOMETRICCONOMETRICCONOMETRICCONOMETRICCONOMETRIC S S S S STRUCTURALTRUCTURALTRUCTURALTRUCTURALTRUCTURAL M M M M MODELSODELSODELSODELSODELS (MESM (MESM (MESM (MESM (MESMSSSSS)))))MMMMMARIEARIEARIEARIEARIE AAAAANNENNENNENNENNE C C C C CAGASAGASAGASAGASAGAS,,,,, G G G G GEOFFREYEOFFREYEOFFREYEOFFREYEOFFREY D D D D DUCANESUCANESUCANESUCANESUCANES,,,,, N N N N NEDELEDELEDELEDELEDELYNYNYNYNYN M M M M MAGTIBAAGTIBAAGTIBAAGTIBAAGTIBAYYYYY-R-R-R-R-RAMOSAMOSAMOSAMOSAMOS,,,,, D D D D DUOUOUOUOUO Q Q Q Q QINININININ,,,,,ANDANDANDANDAND P P P P PILIPINASILIPINASILIPINASILIPINASILIPINAS Q Q Q Q QUISINGUISINGUISINGUISINGUISING

Various applications of the ALI method show that its forecasting performance can besignificantly better than that of traditional VAR models, (e.g., Banerjee et al. 2003). However,as with the traditional VAR model, it is highly sensitive to the choice of variables, and the variableset is frequently limited by finite sample size in practice. As a result, such models are oftennot well specified in terms of economic structure.

In this paper, we compare the forecasting performance of the ALI method with that ofthe macro econometric structural models (MESMs) and experiment with ways to improvethe ALI with reference to the MESM method. The comparison is experimented on forecastingtwo key macro variables, inflation and GDP growth, of three countries, namely People’s Republicof China (PRC), Indonesia, and Philippines, as macroeconometric models for these countrieshave been built recently by the Asian Development Bank (ADB). The main comparison is basedon short-run forecasts, as the ALI was developed for this in particular. But in addition, we hopeto address the following issues. How does the forecasting performance of each type of modelsprogress as the forecasting horizon is extended? How do variables that are included in theALI, but not in the MESM, affect the ALI forecasts? How much does the use of higher frequencydata of ALI (monthly) improve the forecasts as compared to those by quarterly-data-basedMESMs?

Through the comparison experiments, we also seek possible ways of improving the ALImethod with respect to the MESM method, as the former is relatively new. One key featureof MESMs is the presence of a long-run, theory-based equilibrium-correction mechanism (ECM)in all the behavioral equations, whereas ALI models only consider common movement amongshort-run changes of a pool of variables. Hence, we try to see whether the forecastingperformance of ALI improves if deviations from the long-run co-trending movement, asembodied by the ECM terms in the MESMs, are added into the ALI models. Another featureof MESMs is that every fitted equation in an MESM is obtained through a parsimonious-specification reduction process (e.g., see Hendry 1995 and Hendry and Krolzig 2001). In contrast,the VAR model used in the ALI suffers from overparameterization in general. Hence, we tryto see whether Hendry’s reduction process will be able to help sharpen the performance ofthe VAR by pruning out the overparameterized part of the VAR.

The rest of the paper is organized as follows. The next section will describe briefly theALI method,5 the choice of variable sets and related data, the basic structure of the MESMs,and the design of the comparison experiments. Empirical results for the comparison experimentsare discussed in Section III. The following section discusses possible ways of reducing theuncertainty involved in using the ALI method by adopting two key features from the MESMmodeling method. The last section summarizes the results and gives some concluding remarks.

5 For detailed theoretical description of the ALI, see Camba-Mendez et al. (2001); for detailed description of how toapply the method, see the Practitioners’ Note attached at the end of the paper.

33333ERD ERD ERD ERD ERD TTTTTECHNICALECHNICALECHNICALECHNICALECHNICAL N N N N NOTEOTEOTEOTEOTE S S S S SERIESERIESERIESERIESERIES N N N N NOOOOO..... 1818181818

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ANDANDANDANDAND S S S S SCENARIOSCENARIOSCENARIOSCENARIOSCENARIOS FORFORFORFORFOR C C C C COMPOMPOMPOMPOMPARISONARISONARISONARISONARISON

II. MODELS, CHOICE OF ALI INDICATORS, FORECAST VARIABLES,AND SCENARIOS FOR COMPARISON

A. Automatic Leading Indicator

Let Yt be the variable of forecasting interest and Zt the set of n variables, often referredto as indicator variables, form the pool for the extraction of dynamic factors. Economically,there are no set theories to restrict the choice of the n indicator variables. Statistically, allthe variables used in the ALI are required to be stationary. Hence, Yt and Zt are normallytransformed by taking their growth rates (denoted by yt, and zt), and zt is also standardized.However, they do not need to be observed at the same frequency, e.g., some zt can be quarterlyand others monthly time series.

The ALI method consists of two steps: factor extraction and forecasting. The first stepis to extract m factors, ft, using the following dynamic factor model (DFM) in the form of thestate space model representation:

z f ef ft t t

t t -1 t

= += +B

A u (1)

where A and B are parameter matrices to be estimated, and et and ut are error terms. Todetermine the number of factors, m, two recently developed statistical tests are utilized, oneby Bai and Ng (2005) and the other by Onatski (2005).6 Note that the latter test iscomputationally easier and more flexible than the former test. The Bai-Ng test requires thatthe panel data set is balanced and contains large enough n to enable a comparative judgmentof m against a max m(max). As our full data sets are mostly unbalanced and contain relativelysmall numbers of indicator variables, we are often constrained by the restriction of( ) ( )mnmn +>− 2 for the identification of the residual covariance matrix of et (see Steiger1994), a matrix that the Bai-Ng test is based upon. Nevertheless, both tests are calculatedand the larger number is normally adopted as m when the two test results differ. Next, thefactor extraction is carried out by the Kalman filter algorithm, with the initial parameterestimates obtained via principal component analysis (PCA).

The second step is to run a standard VAR model to forecast yt and ft in combination:

y

f

y

f

y

ft t

p

t p

t

⎛

⎝⎜

⎞

⎠⎟ =

⎛

⎝⎜

⎞

⎠⎟ + +

⎛

⎝⎜

⎞

⎠⎟ +

− −

Π Π1

1

ε (2)

where the minimum lag order p should be such as to entail the residuals et to satisfy the classicalassumptions.

6 Onatski’s test exploits ideas from random matrix theory, similar to the approach explored by Kapetanios (2004).




B. Indicators

A wide range of economic factors is believed to be correlated with inflation and GDP growth,such as monetary and finance variables, variables from the real sector such as industrialproduction, not to disregard all those micro factors that affect prices of individual commodities,which comprise the consumer price index (CPI), the indicator from which inflation is measured.

In the present exercise, the indicators are chosen mainly at the macro level, such as theindex of industrial production, monetary aggregates, unemployment, average labor wage rate,and short-run interest rate. Consumer confidence index or business confidence index is alsoused when such survey data are available. Monthly series of the indicators are used wheneverpossible. Otherwise, the series are in quarterly observations. A detailed list of the indicatorsand data sources for all the three countries, i.e., PRC, Indonesia, and Philippines, is given inthe Appendix. All the indicator variables are processed into standardized stationary series.The details of how the series are processed are given in the Practitioners’ Note attached atthe end of this paper.

C. Modeling Consumer Price Index and Gross Domestic Product in MESMs

The MESM of each of the three countries comprise about 70-80 variables, covering privateconsumption, investment, government, foreign trade, the three production sectors of theeconomy, labor, prices, and monetary blocks.7 The ECM form is used for all the behavioralequations, which are obtained through the general→specific dynamic specification approach.Mostly individually estimated by least squares (LS) method using quarterly data starting fromthe early 1990s, these equations in combination behave very similarly to a structural VAR modelin dynamic simulation.8

The CPI is modeled essentially as a simple mark-up of producer/wholesale prices in thelong run. Import price may also play a part. The producer prices are explained by factor pricesand/or labor productivity. In the case of the PRC and Indonesia, an indicator called GDP gapis found to impact on inflation. The GDP gap is defined as the ratio of a long-run GDP trend,generated by a simple production function, to GDP.

The real GDP is modeled via its three sectors—primary, secondary, and tertiary sectors.The secondary sector output follows a simple production function in the long run. The tertiarysector output is demand-driven, i.e., explained by income and relative prices. The primarysector output in the PRC model is also demand-driven, and follows basically an autoregressiveprocess in the other two models. Various short-run demand factors like cross-section demandfactors sometimes also impact on these output equations.

7 For more detailed description of the PRC model, see Qin et al. (2005), and for the Philippine model, see Cagas etal. (2006). These two models are relatively mature whereas the ADB Indonesia model is the latest being developed.The Indonesia model is structurally similar to the Philippine model.

8 As far as the main difference in the estimation method is concerned, it is long known that parameter estimates bysimultaneous-equation maximum likelihood (ML) or single-equation least squares (LS) methods do not tend to differsignificantly under small samples. Indeed, this is checked and verified in the cases when variables are simultaneouslydetermined, such as import and export prices.


D. Forecast Variables and Comparison Statistics

We choose inflation (measured by CPI growth) and GDP growth as the forecast variablesof interest mainly because these two are the most frequently quoted and the most monitoredmacroeconomic indicators of an economy, and are the objects of investigation in most of theliterature on leading indicators modeling methods. Moreover, they present us with a verydifferent experimental setting. While CPI data are available at a monthly frequency, GDPdata is only available at a quarterly frequency. In terms of the ADB MESMs, inflation isendogenously determined by an equation in the price block, whereas GDP is derived as thesum of the outputs of the three sectors, each endogenously determined by an equation in theoutput block. These differences are expected to broaden the generality of the comparisonresults.

However, certain features of the data samples may pose a challenge particularly to theALI method. Specifically, both Indonesia and the Philippines suffered from the East Asianfinancial crisis in the late 1990s. As a result, the related inflation series and many of the indicatorseries are more volatile than what are expected of normally distributed series (see Figure1). Another data feature is the pronounced seasonal pattern in the GDP data, as well as insome of the associated indicators, of all the three countries (see Figures 1 and 2). As the MESMsare built to forecast the published GDP series as they are, seasonal adjustment of the rawdata cannot be applied.

Standard root mean square error (RMSE) statistics are used for the evaluation of modelforecast performance and are calculated for out-of-sample forecasts, covering the period2002Q1-2005Q1.9 These are supplemented by graphs of forecast series and errors. In orderto find answers to the questions raised in the previous section, the following four scenariosare designed for the comparison exercise:

(i) Scenario A: The indicator set includes all the indicator variables listed in theAppendix

(ii) Scenario B: The indicator set only includes those variables that are used in theMESMs

(iii) Scenario C: The indicator set only includes those variables having monthlyobservations

(iv) Scenario D: The indicator set is the same as in Scenario C but the monthlyfrequency is integrated into quarterly frequency

9 In the case of the MESMs, this also involves revising data on exogenous variables from actual to what would havebeen reasonable forecasts at the time they are to be made.

SSSSSECTIONECTIONECTIONECTIONECTION II II II II IIWWWWWHYHYHYHYHY WWWWWEEEEE N N N N NEEDEEDEEDEEDEED AAAAA C C C C CONTROLONTROLONTROLONTROLONTROL G G G G GROUPROUPROUPROUPROUP

ANDANDANDANDAND H H H H HOWOWOWOWOW WWWWWEEEEE C C C C CANANANANAN G G G G GETETETETET ITITITITIT




-5%

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1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

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FIGURE 1

VARIABLES OF FORECAST INTEREST

Inflation GDP Growth

PRC

Philippines

Indonesia


-4%

-3%

-2%

-1%

0%

1%

2%

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2001 2002 2003 2004 2005

Sc Eb MESM Inflation 0%

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Sc Eb MESM GDP Growth

0%

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Sc C MESM GDP Growth

0%

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2001 2002 2003 2004 2005

Sc E MESM Inflation

-4%

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2001 2002 2003 2004 2005

Sc D MESM GDP Growth

FIGURE 2

8-STEP FORECAST RESULTS


PRC

Philippines

Indonesia

Note: The scenarios (shortened as ‘Sc’) presented here are the best fitting ALI scenarios by parsimoniously restricted VAR models

for the three countries.

SSSSSECTIONECTIONECTIONECTIONECTION II II II II IIWWWWWHYHYHYHYHY WWWWWEEEEE N N N N NEEDEEDEEDEEDEED AAAAA C C C C CONTROLONTROLONTROLONTROLONTROL G G G G GROUPROUPROUPROUPROUP

ANDANDANDANDAND H H H H HOWOWOWOWOW WWWWWEEEEE C C C C CANANANANAN G G G G GETETETETET ITITITITIT




III. COMPARISON OF FORECAST RESULTS

Note that the ALI indicator sets finally presented here differ from country to countrydue mainly to data availability (see Table 1 and the Appendix). These differences may contributeto the different results in model comparison.10 Another issue to note is that the ALI methodcan provide monthly forecasts whereas the MESMs only give quarterly forecasts. To comparetheir results, we integrate those monthly ALI forecasts into quarterly forecasts. Table 2 reportsthe two test results for the number of factors, m. Table 3 reports the numbers of lags, p, usedin the VARs based on residual mis-specification tests. These test statistics are not reported hereto keep the paper short.

10 One factor that might have caused the PRC results to differ from those of the other two countries is the unique waythat the monthly consumer price index (CPI) data are released. It is based on the current year, rather than havinga set base year, thus making it impossible to convert monthly series into quarterly series without imposing extraassumptions.

11 The RMSEs for GDP forecasts by the MESMs are calculated on the basis of the sum of forecast errors of the threesector output.

TABLE 1ALI INFORMATION: NUMBER OF INDICATORS USED

PRCPRCPRCPRCPRC PHILIPPINESPHILIPPINESPHILIPPINESPHILIPPINESPHILIPPINES INDONESIA INDONESIA INDONESIA INDONESIA INDONESIA

GDPGDPGDPGDPGDP GDPGDPGDPGDPGDP GDPGDPGDPGDPGDPINFLAINFLAINFLAINFLAINFLATIONTIONTIONTIONTION GROWTHGROWTHGROWTHGROWTHGROWTH INFLAINFLAINFLAINFLAINFLATIONTIONTIONTIONTION GROWTHGROWTHGROWTHGROWTHGROWTH INFLAINFLAINFLAINFLAINFLATIONTIONTIONTIONTION GROWTHGROWTHGROWTHGROWTHGROWTH

Scenario A 13 12 16 17 14 13Scenario B 8 8 11 14 8 8Scenario C or D 10 10 13 14 11 10Scenario E 16 14 23 19 16 15Scenario Eb 11 10 — — 10 10

A. Short-term Forecast Comparison

It is easily discernible from Table 4, as well as Figure 2, that ALI models can generatemore accurate short-run forecasts (i.e., in terms of smaller RMSEs) than the MESMs on thewhole.11 The only exception is in the case of Philippine GDP growth forecasts.

However, the main factor that has improved the forecasts turns out not to be the additionof indicators that are not included in the MESMs. If we compare the RMSEs of Scenario Awith those of Scenario B, we see that the exclusion of the additional indicators (ScenarioB) actually reduces the forecast errors in most of the cases, especially in the cases of thePRC. This suggests that MESMs do not suffer much from the missing-variable problem; thatbetter forecasts do not necessarily follow from an expansion of the indicator set; and thatpriority should be given to indicator variables with a priori theory underpinning when it comesto choosing indicators.


TABLE 3ALI: NUMBER OF LAGS USED IN THE VAR

PRCPRCPRCPRCPRC PHILIPPINESPHILIPPINESPHILIPPINESPHILIPPINESPHILIPPINES INDONESIAINDONESIAINDONESIAINDONESIAINDONESIA

Inflation

ALI scenario A 12 5 6ALI scenario B 10 5 6ALI scenario C 12 5 6ALI scenario D 4 2 4ALI scenario E 12 6 5ALI scenario Eb 10 — 6

GDP Growth

ALI scenario A 9 7 6ALI scenario B 9 7 9ALI scenario C 9 7 9ALI scenario D 4 3 4ALI scenario E 9 7 6ALI scenario Eb 9 — 6

TABLE 2ALI: TEST RESULTS FOR THE NUMBER OF FACTORS (BAI & NG TEST / ONATSKI TEST)


InflationInflationInflationInflationInflation

ALI scenario A 1 / 4 1 / 5 2 / 4ALI scenario B 4 / 3 4 / 4 4 / 3ALI scenario C 1 / 4 1 / 4 2 / 4ALI scenario D 1 / 4 4 / 4 4 / 4ALI scenario E 1 / 5 1 / 4 6 / 5ALI scenario Eb 4 / 4 — 4 / 4

GDP GrowthGDP GrowthGDP GrowthGDP GrowthGDP Growth

ALI scenario A 4 / 4 3 / 5 5 / 4ALI scenario B 3 / 4 4 / 4 3 / 3ALI scenario C 4 / 4 3 / 4 2 / 4ALI scenario D 2 / 4 3 / 4 1 / 4ALI scenario E 4 / 4 3 / 5 5 / 4ALI scenario Eb 4 / 4 — 4 / 5

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As for the contribution of higher-frequency data (i.e., comparison of Scenarios C andD), the results are mixed. The inflation forecasts of Indonesia and the Philippines clearly showthat short-term forecasts are more accurate when based on monthly data than on quarterlydata. However for GDP forecasts, this observation is only true for the Philippines. In the othertwo cases, the change in data frequency hardly shows any effects, due probably to the datafeatures of GDP series being low frequency (quarterly) and highly seasonal (see Figure 1).Relatively, the case of inflation forecast of the PRC shows clearly that higher-frequency datamight exacerbate forecast errors by bringing too much unwanted data volatility.12 This servesas a warning against the common belief that utilization of higher-frequency information (e.g.,monthly data) will generate more accurate short-run forecasts.

In summary, the better short-run accuracy of the ALI forecasts compared to those ofthe MESMs appear to derive from the greater capacity of the ALI method itself to captureshort-run dynamics. The results also show, however, that this capacity can be subdued by falseinclusion of irrelevant indicators or false exclusion of relevant indicators. Careless selectionof the variable set is indeed one of the most important factors to induce forecast failure (seeClements and Hendry 2002).

TABLE 4RMSES FOR ONE-QUARTER AHEAD FORECASTS

PRC PRC PRC PRC PRC PHILIPPINES INDONESIA PHILIPPINES INDONESIA PHILIPPINES INDONESIA PHILIPPINES INDONESIA PHILIPPINES INDONESIA

Inflation

MESM 1.295 0.515 1.092ALI scenario A (by reduced VAR) 1.273(1.206) 0.461(0.551) 1.053(1.061)ALI scenario B (by reduced VAR) 0.909(0.866) 0.430(0.408) 0.968(1.037)ALI scenario C (by reduced VAR) 1.299(1.233) 0.414(0.420) 0.967(1.000)ALI scenario D (by reduced VAR) 1.176(0.997) 0.657(0.877) 2.360(1.513)ALI scenario E (by reduced VAR) 1.214(0.928) 0.308(0.343) 0.947(0.872)ALI scenario Eb (by reduced VAR) 0.879(0.859) — 0.960(1.026)

GDP Growth

MESM 2.147 1.417 2.969ALI scenario A (by reduced VAR) 1.537(1.850) 1.897(2.166) 2.232(1.980)ALI scenario B (by reduced VAR) 1.361(1.474) 1.913(1.797) 2.115(2.208)ALI scenario C (by reduced VAR) 1.528(1.550) 1.711(1.837) 1.806(1.899)ALI scenario D (by reduced VAR) 1.524(1.241) 2.487(2.083) 1.791(1.870)ALI scenario E (by reduced VAR) 1.574(1.441) 1.873(2.370) 2.173(2.037)ALI scenario Eb (by reduced VAR) 1.169(0.879) — 2.026(1.998)

Note: The figures are generated by unrestricted VARs using the lag numbers given in Table 3. The figures in parenthesesare generated by the reduced VARs.

12 It is possible that the inferior result of scenario C to that of scenario D in the PRC case is due partly to the undesirablevolatility brought in by those monthly indicators in scenario A, which are excluded in scenario B. But it is difficult toverify this postulate here as exclusion of those monthly indicators from scenario C would result in too small an indicatorset (5 indicators) to carry out the ALI properly.


13 The location shifts form a common type of forecast failures in structural econometric modeling. They are due frequentlyto historically specific events, or institutional changes, which are excluded from theories and are totally unanticipatedex ante (e.g., see Hendry 2004 and 2005).

B. Longer-term Forecast Comparison

The main results are summarized in the RMSEs of the 8-step ahead forecasts in Tables5 and 6, as well as Figure 3. To keep the paper short, only two scenarios of the ALI are reportedhere: Scenario A and the best scenario selected for each case as compared with the MESM results.

From the inflation results in Table 5, we can see that the superior forecasting record ofthe ALI models fades away rapidly as the forecast horizon widens, roughly within two quartersor 6 months when compared with the forecasting record of the MESMs. On the other hand,GDP forecasts in Table 6 show mixed results. For the Philippines, the forecast performanceof the MESM remains the best. The ALI forecasts outperform those of the MESMs in the PRCand Indonesia cases, quite independent of the extension of the forecast horizon. In comparisonwith the inflation series, one factor that has very probably contributed to the persistence ofgood ALI forecasts over multiple steps is the dominant seasonality in the GDP growth rates,as shown in Figure 1.

On the other hand, there is one important difference between the ALI forecasts and theMESM forecasts. The MESMs produce forecasts on GDP levels and price indices whereas theALI only forecasts growth rates. In other words, the MESMs operate largely in a nonstationaryworld where many nonstationary variables could randomly drift away from the forecastedstochastic trend, known as “unanticipated location shifts”,13 whereas the ALI is largely immunefrom the location-shift problem by operating within the stationary world as the stochastic trendsin the data series have already been filtered out. This means that the ALI forecasts couldoutperform the MESM forecasts over a multiperiod horizon when the forecasts suffer fromlocation shifts. To check whether our MESM forecasts suffer from location shifts, h-step forecasterrors on the GDP levels and CPI series are plotted in Figure 4. It is evident from the figurethat the GDP level forecasts drift apart from their actual values more than the CPI forecasts,and that the drifts are most severe in the case of Indonesia and mildest in the case of thePhilippines. These help explain why the ALI multistep forecasts can outperform those of theMESMs in the cases of GDP growth forecasts in the PRC and Indonesia.

C. Comparison of Forecast Methods

The ALI forecasts presented here are actually chosen from a huge amount of modelingexperiments with different indicator variable sets, different m and p as well. This is mainlybecause of the high flexibility of the method and the relatively low computational costs. However,flexibility also implies uncertainty. As seen, the forecasting performance of the ALI is sensitiveto the choice of indicators and frequency mix, and there are no a priori rules to narrow downthe choice. Furthermore, it is difficult to judge how robust the forecasting capacity of eachfactor is in the VAR. In fact, forecasts by the existing MESMs have actually served as abenchmark for the selection of the ALI trials.





-3%

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2001 2002 2003 2004 2005

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Sc D MESM GDP Growth

FIGURE 3

8-STEPS FORECAST RESULTS

PRC

Philippines

Indonesia

Note: The scenarios (shortened as ‘Sc’) presented here are the best fitting ALI scenarios by parsimoniously restricted VAR models

for the three countries.




-4%

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200101 200103 200201 200203 200301 200303

FIGURE 4

MESM H=STEP FORECAST ERRORS

(AS PERCENTAGE TO THE ACTUAL VALUES)

PRC

Philippines

Indonesia

Constant-price GDP CPI Index




TTTTTABLEABLEABLEABLEABLE 5 5 5 5 5RMSERMSERMSERMSERMSESSSSS FORFORFORFORFOR H-Q H-Q H-Q H-Q H-QUARTERSUARTERSUARTERSUARTERSUARTERS A A A A AHEADHEADHEADHEADHEAD F F F F FORECASTSORECASTSORECASTSORECASTSORECASTS: I: I: I: I: INFLATIONNFLATIONNFLATIONNFLATIONNFLATION

QUARTERS QUARTERS QUARTERS QUARTERS QUARTERS AHEADAHEADAHEADAHEADAHEAD 11111 22222 33333 44444 55555 66666 77777 88888

PRC

MESM 1.295 1.689 2.009 2.208 1.910 1.990 2.188 2.170ALI: Scenario A 1.273 2.825 4.450 6.348 3.414 2.442 2.862 3.515ALI: Scenario B 0.909 1.968 3.199 4.528 3.796 4.563 5.371 6.306ALI: Scenario E 1.214 2.787 4.534 6.739 5.461 6.437 7.494 8.706ALI: Scenario Eb 0.879 1.840 3.054 4.177 3.688 4.384 5.143 6.025

Using parsimoniously restricted VAR:ALI: Scenario A 1.206 2.226 2.495 3.477 2.808 2.474 2.844 3.125ALI: Scenario B 0.866 1.089 1.417 2.185 2.502 2.941 3.543 3.787ALI: Scenario E 0.928 1.338 1.362 2.122 2.120 2.549 3.480 3.304ALI: Scenario Eb 0.859 1.147 1.423 2.178 2.494 2.856 3.374 3.582

Philippines

MESM 0.515 0.912 1.319 1.507 1.604 1.643 1.634 1.615ALI: Scenario A 0.461 0.971 2.012 3.025 3.927 4.454 4.532 4.583ALI: Scenario C 0.414 0.940 1.914 2.943 3.784 4.339 4.483 4.564ALI: Scenario E 0.308 0.665 1.468 2.421 3.377 3.944 4.086 4.175

Using parsimoniously restricted VAR:ALI: Scenario A 0.553 1.259 2.108 2.979 3.652 4.006 4.179 4.325ALI: Scenario C 0.420 0.891 1.647 2.495 3.189 3.489 3.605 3.651ALI: Scenario E 0.343 0.745 1.532 2.424 3.438 3.962 4.103 4.203

Indonesia

MESM 1.092 2.036 2.649 4.479 4.445 3.776 3.266 3.498ALI: Scenario A 1.053 2.450 3.152 3.836 4.251 5.294 6.353 7.233ALI: Scenario C 0.967 2.041 2.426 3.044 3.497 4.298 4.813 5.113ALI: Scenario E 0.947 2.196 3.537 4.997 6.094 6.762 6.837 6.686ALI: Scenario Eb 0.960 2.429 3.910 5.767 7.194 7.639 7.457 7.077

Using parsimoniously restricted VAR:ALI: Scenario A 1.061 2.406 3.151 3.822 4.547 5.947 7.115 8.014ALI: Scenario C 1.000 2.279 3.061 4.060 4.996 6.394 7.323 7.767ALI: Scenario E 0.872 1.836 2.681 3.382 3.732 3.756 3.913 3.659ALI: Scenario Eb 1.026 2.275 3.111 4.656 6.038 6.699 6.618 6.125


TABLE 6RMSES FOR H-QUARTERS AHEAD FORECASTS: GDP GROWTH

QUARTERS QUARTERS QUARTERS QUARTERS QUARTERS AHEADAHEADAHEADAHEADAHEAD 11111 22222 33333 44444 55555 66666 77777 88888

PRC

MESM 2.147 2.181 2.070 1.605 1.326 1.379 1.299 1.393ALI: Scenario A 1.537 0.885 1.180 1.020 1.067 0.975 1.072 1.046ALI: Scenario B 1.361 0.917 1.229 1.039 1.106 0.58 1.036 0.987ALI: Scenario E 1.574 1.058 1.112 0.980 1.099 1.233 1.174 1.030ALI: Scenario Eb 1.169 1.034 1.213 1.190 1.127 1.003 1.182 1.101Using parsimoniously restricted VAR:ALI: Scenario A 1.850 2.217 2.352 1.917 1.784 1.419 1.440 1.683ALI: Scenario B 1.474 0.967 1.239 1.246 1.239 1.482 1.655 1.665ALI: Scenario E 1.441 1.526 1.907 1.637 1.159 0.997 1.195 1.104ALI: Scenario Eb 0.879 1.010 1.039 0.917 1.157 1.137 1.297 1.316

Philippines

MESM 1.417 1.228 1.028 1.249 1.324 1.255 1.411 1.381ALI: Scenario A 1.897 2.543 2.097 2.077 2.166 2.203 2.167 2.261ALI: Scenario C 1.711 2.245 2.222 2.158 2.228 2.118 2.128 2.195ALI: Scenario E 1.873 2.538 2.093 2.084 2.168 2.212 2.172 2.266Using parsimoniously restricted VAR:ALI: Scenario A 2.166 2.512 2.518 2.135 2.000 1.877 1.894 1.964ALI: Scenario C 1.837 2.453 2.071 2.080 2.244 2.205 2.183 2.212ALI: Scenario E 2.370 3.088 2.610 2.088 1.928 1.978 2.031 1.969

Indonesia

MESM 2.969 3.554 5.016 4.624 3.942 4.163 4.941 3.655ALI: Scenario A 2.232 2.106 2.459 1.633 2.334 2.307 2.275 1.964ALI: Scenario D 1.791 2.780 3.369 3.741 3.976 2.958 2.335 3.362ALI: Scenario E 2.173 2.281 2.479 1.777 1.643 1.584 1.423 0.951ALI: Scenario Eb 2.026 2.271 2.096 1.808 2.279 2.250 1.720 1.190Using parsimoniously restricted VAR:ALI: Scenario A 1.980 2.215 2.635 2.129 1.578 1.251 1.363 1.028ALI: Scenario D 1.870 3.199 3.234 2.472 2.188 1.627 1.721 1.794ALI: Scenario E 2.037 2.457 2.620 2.316 1.396 1.101 1.038 0.960ALI: Scenario Eb 1.998 2.486 2.548 2.098 1.804 1.893 1.183 0.974





IV. MODIFIED ALI METHOD

Two key features of the MESM method emerge as potentially beneficial to the ALI methodduring the comparison of the two modeling methods. The first is the ECM specification; the secondis the general→simple model reduction procedure.

Let us first consider the ECM representation from the perspective of a VAR model of(yt, zt). The ECM representation of the yt equation in the VAR should be:

y z y Y Z vt i t ii

p

j t jj

p

t

ECM

t= + + −( ) +−=

−=

−∑ ∑Γ Φ0 1

1φ β (3)

The above equation decomposes the endogenous variable into three types of systematicshocks: exogenous short-run shocks, own lagged short-run shocks, and ECM shocks, knownalso as errors of “cointegration”, and often explained as disequilibrium from a theory-basedlong-run relation. If we compare (3) with an ALI model, we may regard the factors, f, in (1)as a summary representation of exogenous short-run shocks, i.e., type one shocks, and theown lags of the forecast variable in (2) as covering own lagged short-run shocks, i.e., typetwo shocks. However, type three shocks are not explicitly included in the ALI. It seems thatthe ALI method only summarizes co-movement in the form of covariance of a pool of variables,whereas according to many equilibrium economic theories, co-movement in the form of co-trend among certain variables plays an important role in driving the dynamics of endogenousvariables.14

Therefore, a new scenario, designated as Scenario E, is proposed to see if the ALI resultscan be improved when deviations from such co-trend, i.e., the third type of shocks, are addedto the indicator set of Scenario A. The third type of shocks is adopted from the ECM termsembedded in certain relevant equations in the MESMs.15 Notice that the extension can beexecuted in two ways. One is to add the ECM terms as indicator variables in the first step;the other is to extend the VAR model by the ECM terms during the second step. However,experiments show that the latter way is undesirable due to the data-frequency problem. Sinceall the ECM terms are at quarterly frequency, extension of VARs by these terms forces usto reduce the VARs from monthly to quarterly models, making the forecasts significantly worsethan those by the former way. Hence, Scenario E is carried out by treating the ECM termsas indicators.

In terms of short-run forecasts, the addition of the ECM terms to the ALI indicator setsimproves the forecast accuracy in most cases, especially in comparison with Scenario A, albeit

14 See Forni et al. (2004) for a detailed discussion between DFMs and structural VARs.15 The ECM terms derive from long-run relationships postulated by economic theory. On many occasions, the long-run

coefficients are imposed.


SSSSSECTIONECTIONECTIONECTIONECTION IV IV IV IV IVMMMMMODIFIEDODIFIEDODIFIEDODIFIEDODIFIED ALI MALI MALI MALI MALI METHODETHODETHODETHODETHOD

sometimes marginally (Table 4).16 The improvement is more discernible in the inflation forecasts,as the inflation series are more random and less seasonal than the GDP growth series.

When it comes to multiple-step forecasts (see Tables 5 and 6), the addition of the ECMterms generates mixed results. The additions help significantly in delaying the deterioration ofALI forecasts in the cases of inflation forecasts of the Philippines and GDP growth forecastsof Indonesia. However, it can also make the forecasts worse, as in the case of inflation forecastsin the PRC. It has not made significant differences for the rest of the cases. On balance, itseems worthwhile to take into consideration in the ALI indicator sets, disequilibrium shocksguided by economic theories. Nevertheless, caution should be exercised in choosing whichdisequilibrium shocks are the most relevant to include.

In view of the finding that results of scenario B are better than those of scenario A inthe cases of the PRC and Indonesia, another scenario (Eb) is set up that adds ECM termsto scenario B. This scenario is carried out only for the relevant two countries. Comparisonof the results (see Tables 4, 5, and 6) reveals the dominance of scenario Eb over scenarioE, especially in the case of inflation forecasts in the PRC, where both the number of factorsand the VAR lag number are smaller in scenario Eb compared to scenario E.17 This experimentsuggests that it is desirable to augment an indicator set by the ECM terms embodying therelevant long-run theories when the set is chosen under a priori theoretical guidance andthis is shown to produce relatively good forecasts.

Let us now look at how the general→simple model reduction procedure can help reducethe uncertainty in the ALI forecasts. Although the DFMs have the power of significantly reducinga large number of indicators into a few common factors, a VAR model used in the secondstep can still easily run up to over a hundred parameters when there are more than threefactors involved, making it difficult to decide how robust the VAR is in producing the forecasts.To combat the curse of dimensionality of VARs, the general→simple modeling procedure isadopted here to reduce unrestricted VARs into parsimoniously reduced VARs. Specifically, thecomputer-automated approach of PcGets is utilized to carry out the reduction efficiently (seeHendry and Krolzig 2001).

The advantages of this modification of the ALI method are immediately noticeable fromthe drastic reduction of the number of parameters reported in Table 7. As the parameternumber in each equation of a VAR shrinks to a manageable size, it becomes possible for usto examine how much and in what manner each factor contributes to the forecasts and howrobust the VAR is by means of various model specification tests. In particular, parameterconstancy can be checked via recursive estimation and parameter instability tests in view ofthe forecasting requirement.18 The results reveal that some of the VAR equations in certainscenarios suffer significantly from structural shifts, mostly due to the East Asian financial crisis,

16 For the details of the ECM terms added, see the Appendix.17 The only exceptional case here not showing better results is inflation forecasts of Indonesia. However, it should be

noted that the VAR of scenario E contains six factors whereas the VAR of scenario Eb only four factors in this case.18 PcGive is used for detailed parameter analyses. None of these model specification and reduction statistics are reported

here in order to keep the paper short.




and that some factors are largely unpredictable in the VARs. Such information enables us toassess the reliability of the VAR in generating the forecasts.

The advantages of VAR reduction is also noticeable from various RMSEs reported in Tables4–6. In view of the one-step ahead forecasts (Table 4), the VAR reduction has brought downthe RMSEs in about half of the cases. The improvement is more marked for a number of casesin the eight-step ahead forecasts (Tables 5 and 6), e.g., the inflation forecasts of the PRC andthe Philippines, and the GDP growth forecasts of Indonesia. The improvement seems due tothe fact that model reduction has significantly reduced unwanted noises in the unrestrictedVAR from getting into the forecasts. It is also found that the cases where model reductionhas not helped improve forecast accuracy tend to suffer from parameter shifts in the reducedVAR as well as from low forecastability of one or more of the factors in the related VAR.

V. CONCLUSION

This paper investigates the comparative forecast performance of the ALI method versusthe MESMs and seeks ways of improving the ALI method. Inflation and GDP growth are usedas the objects of the forecast comparison. PRC, Indonesia, and Philippines are used as thecases of the investigation. The following key results can be summarized from a huge amountof ALI experiments that have been carried out.

TABLE 7NUMBERS OF PARAMETERS REDUCED FROM UNRESTRICTED VARS TO PARSIMONIOUSLY REDUCED VARS


Inflation

ALI scenario A 300 → 52 180 → 32 150 → 47ALI scenario B 250 → 38 125 → 25 150 → 46ALI scenario C 300 → 39 125 → 28 150 → 52ALI scenario D 100 → 41 50 → 14 100 → 44ALI scenario E 432 → 73 210 → 27 245 → 61ALI scenario Eb 250 → 43 — 150 → 46

GDP Growth

ALI scenario A 225 → 77 252 → 75 216 → 75ALI scenario B 225 → 52 175 → 55 144 → 41ALI scenario C 225 → 54 175 → 60 225 → 59ALI scenario D 100 → 41 75 → 20 100 → 34ALI scenario E 225 → 61 252 → 70 216 → 76ALI scenario Eb 225 → 74 — 216 → 81

Note: Unrestricted VARs mean the VARs using the lag numbers given in Table 3.


SSSSSECTIONECTIONECTIONECTIONECTION VVVVVCCCCCONCLUSIONONCLUSIONONCLUSIONONCLUSIONONCLUSION

(i) The ALI method can generally outperform MESMs in short-run forecasts providedthat the indicator variable sets, the number of factors and the VAR lag orders arecarefully selected. However, its forecasting advantage tends to fade away as theforecast horizon increases. MESMs can be more robust for longer-run forecasts incomparison.

(ii) Freer inclusion of data information into the ALI indicator variable sets, as comparedwith the more theory-guided variable selection in the MESMs, may help improveforecast accuracy, but may also spoil it by bringing in unwanted noise. On balance,both theory and good economic sense are required in choosing indicator variables,and the tendency of including whatever data is available should be avoided.

(iii) Use of higher frequency data can help improve forecast accuracy, but it also carriesthe risk of bringing in unwanted higher frequency noise. To avoid such risk, it isadvisable to consider carefully the data features of the forecast target whenchoosing indicator variables. The common belief that higher frequency informationwill always help improve forecasts is unwarranted.

(iv) Inclusion of disequilibrium shocks as additional indicator variables in the ALI mayhelp improve the forecast accuracy, especially for multiple step forecasts. This findingsuggests that DFMs may perform better if they include theory-based disequilibriumshocks in addition to variable own shocks.

(v) The ALI method can produce models that generate better forecasts than thoseby MESMs, but the method involves greater uncertainty than the MESMs. Oneway of reducing the uncertainty related to the unrestricted VAR used in the secondstep of the ALI is to adopt the general→simple model reduction procedure fromthe MESMs. The procedure not only helps to trim out unwanted noise from enteringthe ALI forecasts but also enables modelers to examine and assess closely therobustness of the VAR model specification.

(vi) As formulation and specification uncertainty about econometric models is knownto be hard to assess with respect to the evolving economic reality, it is thus moredesirable to compare and utilize forecasts from both modeling sources than tochoose a single method.




continued.

APPENDIX

VARIABLES AND DATA SOURCES

VARIABLESVARIABLESVARIABLESVARIABLESVARIABLES FREQUENCYFREQUENCYFREQUENCYFREQUENCYFREQUENCY INFLAINFLAINFLAINFLAINFLATIONTIONTIONTIONTION GDP GROWTHGDP GROWTHGDP GROWTHGDP GROWTHGDP GROWTH SOURCESOURCESOURCESOURCESOURCE

PhilippinesPhilippinesPhilippinesPhilippinesPhilippines

91-day Treasury Bill Rate Monthly Datastream

Brent Crude - CurrentMonth, FOB U$/BBL Monthly Datastream

Consumer Price Index(1994=100) Monthly SPEI

Consumer Price Index(1994=100) ECM term Quarterly PHI Model

Domestic Credit Monthly BSP

Domestic Credit CB &DMB ECM terms Quarterly PHI Model

Exports (pesos, FOB) Monthly FTS

Foreign Exchange Rate Monthly SPEI

Government Expenditure(million pesos) Monthly SPEI

Gross Domestic Product(in 1994 constant price) Quarterly NAP

Imports (pesos, CIF) Monthly FTS

Imports ECM term Quarterly PHI Model

Imports of Consumer

Goods (pesos, CIF) Monthly FTS

Interest Rate Differential(domestic rate net of USprime lending rate) Monthly Datastream


AAAAAPPENDIXPPENDIXPPENDIXPPENDIXPPENDIX

VVVVVARIABLESARIABLESARIABLESARIABLESARIABLES ANDANDANDANDAND D D D D DATAATAATAATAATA S S S S SOURCESOURCESOURCESOURCESOURCES


Appendix. continued.

Job Vacancies Monthly SPEI

M1 (million pesos) Monthly SPEI

M1 ECM term Quarterly PHI Model

Overseas WorkersRemittances Monthly BSP

Prime Lending Rate Monthly SPEI

Rainfall Index Quarterly PAGASA

Savings Deposit Rate Monthly SPEI

Secondary Sector Value-Added (in 1994 constantprice) ECM term Quarterly PHI Model

Stock Composite Index Monthly PSE

Tertiary Sector Value-Added (in 1994 constantprice) Quarterly NAP

Tertiary Sector Value-Added ECM term Quarterly PHI Model

Unemployment Rate Quarterly LFS

Value of ProductionIndex in Manufacturing(1994=100) Monthly Datastream

Note: “ ” indicates that the variable is used as an indicator for Inflation or GDP growth.BSP means Bangko Sentral ng Pilipinas.FTS means Foreign Trade Statistics.LFS means Labor Force Survey.NAP means National Account of the Philippines.PSE means Philippine Stock Exchange.SPEI means Selected Philippine Economic Indicators.SSI means Survey of Selected Industries.

continued.





continued.


THE PRC

Average Repo Rate Monthly PBC

Balance of Trade Monthly Computedfrom IMF

Base Money (millionyuan, M0 plus RSV) Monthly QB

Base Money Supply(million yuan, net foreignassets plus netgovernment claims andborrowed reserve byfinancial institutions atPBC) Monthly QB

Brent Crude - CurrentMonth, FOB U$/BBL Monthly Datastream Chinese Renminbi to US$(GTIS) Monthly CMEI

Consumer ConfidenceIndex Monthly NBS

Consumer Price Index(1992Q1=1) Monthly NBS

Consumer Price Index(1992Q1=1) ECM term Quarterly PRC Model

Government Expenditure Monthly CMEI

Gross Domestic Product(in 1992Q1 price) Quarterly CMEI

Investments Monthly CMEI

Loans Monthly CMEI



M1 Monthly QB

M1 ECM term Quarterly

Net IndustrialProduction(Value Added) CurrentPrice Monthly CMEI & NBS

Real EffectiveExchange Rate Index- CPI Based Monthly IMF

Real Estate ClimateIndex Monthly Datastream

Secondary Sector Value-Added (in 1992Q1price) ECM term Quarterly PRC Model Shanghai CompositeStock Index Monthly NBS

Tertiary Sector Value-Added (in 1992Q1price) ECM term Quarterly PRC Model

Total Retail SalesCurrent Price Monthly CMEI

Unemployment Rate Quarterly Computed fromCSY

CMEI means China Monthly Economic Indicators.CSY means China Statistics Yearbook.IMF means International Monetary Fund.NBS means National Bureau of Statistics.PBC means People’s Bank of China.QB means Quarterly Banking.


continued.








continued.

Indonesia

Brent Crude - CurrentMonth, FOB U$/BBL Monthly Datastream Consumer Price Index Monthly BI

Consumer Price IndexECM term Quarterly INO Model

EOP ConsumerConfidence Index Monthly CEIC

EOP Interbank CallRate Monthly BI

Interest RateDifferential(domestic rate net ofUS prime lending rate) Monthly Datastream

EOP Jakarta StockExchange CompositeIndex Monthly BI

Exchange Rate–Indonesian Rupiah toUS $ (GTIS) Monthly BI

Total Exports Monthly Datastream

Total Imports Monthly Datastream

Imports of ConsumerGoods Monthly Datastream

Gross Domestic Product(constant price) Quarterly BI

Industrial Labor WageIndex Quarterly CEIC


Volume of ProductionIndex in Manufacturing Monthly CEIC M1 Monthly BI

M1 ECM term Quarterly INO Model

Commercial Bank TotalOutstanding Credits(net of credits toindividuals) Monthly Datastream

Primary Sector Value-Added (constant price) Quarterly BI

Secondary Sector Value-Added ECM term Quarterly INO Model

Tertiary Sector Value-Added ECM term Quarterly INO Model

Unemployment rate Quarterly Computed fromCEIC

BI means Bank Indonesia.CEIC means????_________________________________.








PRACTITIONER’S NOTE: STEP-BY-STEP MENU OF DOING THE ALI

This makes heavy reference to the project report “An Automatic Leading Indicator Model of ChineseInflation” by Mitchell (2004). However, the computing procedure has been greatly improved at theMacroeconomics and Finance Research Division of the Economics and Research Department, AsianDevelopment Bank, to ease the implementation of the ALI procedure. The data preparation part is nowprocessed in Excel with tailor-made macros. The ALI part is prepared with user-friendly programs inEViews.

1. Data Preparation

The first step is to select the indicator variables, Z, that will be used to extract the factors in the automaticleading indicator (ALI) models. The choice may vary from country to country depending on both thevariable of forecasting interest, Y, and data availability. As the ALI is able to accommodate and combinedata measured at different frequencies through state-space modeling, the indicators can be monthly, quarterly,or annual series.

All the variables in Z must be stationary to be used in an ALI model. Hence, nonstationary variables aretransformed appropriately to achieve stationarity. This is usually done by transforming the variables intogrowth rates, which can be approximated by taking differences of the variables in their natural logarithms.For those variables whose growth rates are not yet stationary, a second differencing is necessary to transformthem into their stationary acceleration rates.

Each of the transformed variables is then examined for the possible presence of seasonality and outliers.Seasonality can be removed using any existing technique in EViews known as “seasonal adjustment.”Outliers can be detected with the aid of the TRAMO-SEATS algorithm (available from the website of Bankof Spain). Here, it is important to use economic judgment in deciding whether to remove all the visuallyhigh volatilities as outliers. For example, high volatilities are expected during the period of the Asianfinancial crisis, and should obviously not be considered as outliers to be removed.

Finally, normalization of the transformed Z is done by subtracting the corresponding mean from eachindicator and dividing by the standard deviation. We denote the standardized indicators as z . Note that thetransformed y is not normalized.

2. Running the ALI: Step One

In order to operate the Kalman filter algorithm, we have to supply the dynamic factor model (DFM) (1)with initial values for the factors, the coefficient matrices, and the variance matrices of the error vectors.This can be done by utilizing the principal components analysis (PCA).

Notice that PCA does not allow for mixed frequency data set. Remove the lower frequency series from z beforerunning the PCA and only keep those zs that are of the highest frequency, e.g., for a set of monthly andquarterly zs, select only the monthly zs. This way, we maximize the gain from information contained in themonthly zs. The information coverage of the factors derived from the PCA can be used to help us decidehow many factors, i.e., m, to be used in the DFM (1).

In (1), the first equation refers to the signal or observation equation and the second refers to the stateequation. Notice that the number of lags in the state equation may be extended, but normally one lag isadequate.


PPPPPRACTITIONERRACTITIONERRACTITIONERRACTITIONERRACTITIONER’’’’’SSSSS N N N N NOTEOTEOTEOTEOTE

SSSSSTEPTEPTEPTEPTEP-----BYBYBYBYBY-S-S-S-S-STEPTEPTEPTEPTEP M M M M MENUENUENUENUENU OFOFOFOFOF D D D D DOINGOINGOINGOINGOING THETHETHETHETHE ALIALIALIALIALI

While estimated m principal components are used as initial values for the factors in DFM, initial conditionsfor the coefficients and the variances of the error terms are obtained by regressing z on the m principalcomponents. More precisely, regressing the m principal components on their lags gives the initial conditionsfor A in (1). The initial condition for the variance of ut is set to 1.

Having provided necessary initial conditions, the state space model is estimated using the Kalman filteralgorithm. This algorithm is used to come up with smooth estimates of the factors and their forecasts.

3. Running the ALI: Step Two

The m factors obtained from the first step are used in forecasting y by using the VAR (2). The lag order, p,in the VAR can be extended as deemed necessary. The length of the lag can be determined using statisticalcriteria such as the Bayesian Information Criterion (BIC) or the Root Mean Square Error (RMSE).

REFERENCES

Bai, J., and S. Ng. 2005. “Determining the Number of Primitive Shocks in Factor Models.” Available:http://www-personal.umich.edu/~ngse/research.html. Processed.

Banerjee, A., M. Marcellino, and I. Masten. 2003. Leading Indicators for Euro-area Inflation and GDPGrowth. IGIER Working Papers No. 235, Bocconi University, Italy.

Cagas, M. A., G. Ducanes, N. Magtibay-Ramos, D. Qin, and P. Quising. 2006. “A Small MacroeconometricModel of the Philippine Economy.” Economic Modelling 23:45-55.

Camba-Mendez, G., G. Kapetanios, R. J. Smith, and M. R. Weale. 2001. “An Automatic Leading Indicatorof Economic Activity: Forecasting GDP Growth for European Countries.” Econometrics Journal 4:S56-90).

Clements, M. P., and D. F. Hendry. 2002. “Modelling Methodology and Forecast Failure.” EconometricsJournal 5:319-44.

Forni, M., D. Giannone, M. Lippi, and L. Reichlin. 2004. Opening the Black Box: Structural Factor Modelsversus Structural VARs. CEPR Discussion Papers No. 4133, Centre for Economic Policy Research,London.

Gilbert, C. L., and D. Qin. 2006. “The First Fifty Years of Modern Econometrics.” In K. Patterson and T.Mills, eds., Palgrave Handbook of Econometrics: Volume 1 Theoretical Econometrics. Houndmills:Palgrave MacMillan.

Hendry, D. F. 2004. Unpredictability and the Foundations of Economic Forecasting. Economics WorkingPapers No. 2004-W15, Nuffield College, Oxford University.

———. 2005. “Bridging the Gap: Linking Economics and Econometrics.” In C. Diebolt and C. Kyrtsou,eds., New Trends in Macroeconomics. New York: Springer Verlag.

Hendry, D. F., and H.-M. Krolzig. 2001. Automatic Econometric Model Selection Using PcGets London:Timberlake Consultants Ltd.

Kapetanios, G. 2002. Factor Analysis Using Subspace Factor Models: Some Theoretical Results and anApplication to UK Inflation Forecasting. Queen Mary Economics Working Papers No. 466, QueenMary, University of London.

Kapetanios, G. 2004. A New Method for Determining the Number of Factors in Factor Models with LargeDatasets. Queen Mary Economics Working Papers No. 525, Queen Mary, University of London.

Mitchell, J. (2004). “An Automatic Leading Indicator Model of Chinese Inflation.” Asian DevelopmentBank, Manila. Processed.




Onatski, A. 2005. Determining the Number of Factors from Empirical Distribution of Eigenvalues. Departmentof Economics Discussion Paper Series No. 0405-19, Columbia University, New York.

Qin, D., M. A. Cagas, G. Ducanes, X.-H. He, R. Liu, S.-G. Liu, N. Magtibay-Ramos, and P. Quising. 2005.A Small Macroeconometric Model of the PRC. Economics and Research Department, AsianDevelopment Bank, Manila. Draft.

Steiger, J. H. 1994. “Factor Analysis in the 1980s and the 1990s: Some Old Debates and Some NewDevelopments.” I I. Borg and P. Mohjer, eds., Trends and Perspectives in Empirical Social Research.Berlin: Walter de Gruyter.

Stock, J. H., and M. W. Watson. 1989. “New Indexes of Coincident and Leading Economic Indicators.”In O. Blanchard and S. Fischer, eds., NBER Macroeconomic Annual 1989. Cambridge, MA: MIT Press.

——— 1991. “A Probability Model of the Coincident Economic Indicators.” In K. Lahiri and G. Moore,eds., Leading Economic Indicators New Approaches and Forecasting Records. New York: CambridgeUniversity Press.

Stock J. H., and M. W. Watson. 2005. Implications of Dynamic Factor Models for VAR Analysis. NBERWorking Paper Series No. 02138, National Bureau of Economic Research, Cambridge.

29

PUBLICATIONS FROM THEECONOMICS AND RESEARCH DEPARTMENT

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No. 12 Improving the Relevance and Feasibility ofAgriculture and Rural Development OperationalDesigns: How Economic Analyses Can Help—Richard Bolt, September 2005

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32

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Sector Leading to Cost Disease?—Duo Qin, March 2004

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No. 82 Institutions and Policies for Growth and PovertyReduction: The Role of Private Sector Development—Rana Hasan, Devashish Mitra, and MehmetUlubasoglu, July 2006

33

1. Improving Domestic Resource Mobilization ThroughFinancial Development: Overview September 1985

2. Improving Domestic Resource Mobilization ThroughFinancial Development: Bangladesh July 1986

3. Improving Domestic Resource Mobilization ThroughFinancial Development: Sri Lanka April 1987

4. Improving Domestic Resource Mobilization ThroughFinancial Development: India December 1987

5. Financing Public Sector Development Expenditurein Selected Countries: Overview January 1988

6. Study of Selected Industries: A Brief ReportApril 1988

7. Financing Public Sector Development Expenditurein Selected Countries: Bangladesh June 1988

8. Financing Public Sector Development Expenditurein Selected Countries: India June 1988

9. Financing Public Sector Development Expenditurein Selected Countries: Indonesia June 1988

10. Financing Public Sector Development Expenditurein Selected Countries: Nepal June 1988

11. Financing Public Sector Development Expenditurein Selected Countries: Pakistan June 1988

12. Financing Public Sector Development Expenditurein Selected Countries: Philippines June 1988

13. Financing Public Sector Development Expenditurein Selected Countries: Thailand June 1988

14. Towards Regional Cooperation in South Asia:ADB/EWC Symposium on Regional Cooperation

in South Asia February 198815. Evaluating Rice Market Intervention Policies:

Some Asian Examples April 198816. Improving Domestic Resource Mobilization Through

Financial Development: Nepal November 198817. Foreign Trade Barriers and Export Growth September

1988

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No. 1 ASEAN and the Asian Development Bank—Seiji Naya, April 1982

No. 2 Development Issues for the Developing Eastand Southeast Asian Countriesand International Cooperation—Seiji Naya and Graham Abbott, April 1982

No. 3 Aid, Savings, and Growth in the Asian Region—J. Malcolm Dowling and Ulrich Hiemenz,

April 1982No. 4 Development-oriented Foreign Investment

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No. 5 The Multilateral Development Banksand the International Economy’s MissingPublic Sector—John Lewis, June 1982

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No. 7 Grant Element in Bank Loans—Dal Hyun Kim, July 1982

No. 8 Shadow Exchange Rates and StandardConversion Factors in Project Evaluation—Peter Warr, September 1982

No. 9 Small and Medium-Scale ManufacturingEstablishments in ASEAN Countries:Perspectives and Policy Issues—Mathias Bruch and Ulrich Hiemenz, January1983

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No. 14 Energy and Structural Change in the Asia-Pacific Region, Summary of the ThirteenthPacific Trade and Development Conference—Seiji Naya, March 1983

No. 15 A Survey of Empirical Studies on Demandfor Electricity with Special Emphasis on PriceElasticity of Demand—Wisarn Pupphavesa, June 1983

18. The Role of Small and Medium-Scale Industries in theIndustrial Development of the Philippines April1989

19. The Role of Small and Medium-Scale ManufacturingIndustries in Industrial Development: The Experience of

Selected Asian Countries January 199020. National Accounts of Vanuatu, 1983-1987 January

199021. National Accounts of Western Samoa, 1984-1986

February 199022. Human Resource Policy and Economic Development:

Selected Country Studies July 199023. Export Finance: Some Asian Examples September 199024. National Accounts of the Cook Islands, 1982-1986

September 199025. Framework for the Economic and Financial Appraisal of

Urban Development Sector Projects January 199426. Framework and Criteria for the Appraisal and

Socioeconomic Justification of Education ProjectsJanuary 1994

27. Investing in Asia 1997 (Co-published with OECD)28. The Future of Asia in the World Economy 1998 (Co-

published with OECD)29. Financial Liberalisation in Asia: Analysis and Prospects

1999 (Co-published with OECD)30. Sustainable Recovery in Asia: Mobilizing Resources for

Development 2000 (Co-published with OECD)31. Technology and Poverty Reduction in Asia and the Pacific

2001 (Co-published with OECD)32. Asia and Europe 2002 (Co-published with OECD)33. Economic Analysis: Retrospective 200334. Economic Analysis: Retrospective: 2003 Update 200435. Development Indicators Reference Manual: Concepts and

Definitions 2004

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34

No. 16 Determinants of Paddy Production in Indonesia:1972-1981–A Simultaneous Equation ModelApproach—T.K. Jayaraman, June 1983

No. 17 The Philippine Economy: EconomicForecasts for 1983 and 1984—J.M. Dowling, E. Go, and C.N. Castillo, June1983

No. 18 Economic Forecast for Indonesia—J.M. Dowling, H.Y. Kim, Y.K. Wang,

and C.N. Castillo, June 1983No. 19 Relative External Debt Situation of Asian

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No. 20 New Evidence on Yields, Fertilizer Application,and Prices in Asian Rice Production—William James and Teresita Ramirez, July 1983

No. 21 Inflationary Effects of Exchange RateChanges in Nine Asian LDCs—Pradumna B. Rana and J. Malcolm Dowling,Jr., December 1983

No. 22 Effects of External Shocks on the Balanceof Payments, Policy Responses, and DebtProblems of Asian Developing Countries—Seiji Naya, December 1983

No. 23 Changing Trade Patterns and Policy Issues:The Prospects for East and Southeast AsianDeveloping Countries—Seiji Naya and Ulrich Hiemenz, February 1984

No. 24 Small-Scale Industries in Asian EconomicDevelopment: Problems and Prospects—Seiji Naya, February 1984

No. 25 A Study on the External Debt IndicatorsApplying Logit Analysis—Jungsoo Lee and Clarita Barretto, February1984

No. 26 Alternatives to Institutional Credit Programsin the Agricultural Sector of Low-IncomeCountries—Jennifer Sour, March 1984

No. 27 Economic Scene in Asia and Its Special Features—Kedar N. Kohli, November 1984

No. 28 The Effect of Terms of Trade Changes on theBalance of Payments and Real NationalIncome of Asian Developing Countries—Jungsoo Lee and Lutgarda Labios, January1985

No. 29 Cause and Effect in the World Sugar Market:Some Empirical Findings 1951-1982—Yoshihiro Iwasaki, February 1985

No. 30 Sources of Balance of Payments Problemin the 1970s: The Asian Experience—Pradumna Rana, February 1985

No. 31 India’s Manufactured Exports: An Analysisof Supply Sectors—Ifzal Ali, February 1985

No. 32 Meeting Basic Human Needs in AsianDeveloping Countries—Jungsoo Lee and Emma Banaria, March 1985

No. 33 The Impact of Foreign Capital Inflowon Investment and Economic Growthin Developing Asia—Evelyn Go, May 1985

No. 34 The Climate for Energy Developmentin the Pacific and Asian Region:Priorities and Perspectives—V.V. Desai, April 1986

No. 35 Impact of Appreciation of the Yen onDeveloping Member Countries of the Bank—Jungsoo Lee, Pradumna Rana, and Ifzal Ali,May 1986

No. 36 Smuggling and Domestic Economic Policiesin Developing Countries—A.H.M.N. Chowdhury, October 1986

No. 37 Public Investment Criteria: Economic InternalRate of Return and Equalizing Discount Rate—Ifzal Ali, November 1986

No. 38 Review of the Theory of Neoclassical PoliticalEconomy: An Application to Trade Policies—M.G. Quibria, December 1986

No. 39 Factors Influencing the Choice of Location:Local and Foreign Firms in the Philippines—E.M. Pernia and A.N. Herrin, February 1987

No. 40 A Demographic Perspective on DevelopingAsia and Its Relevance to the Bank—E.M. Pernia, May 1987

No. 41 Emerging Issues in Asia and Social CostBenefit Analysis—I. Ali, September 1988

No. 42 Shifting Revealed Comparative Advantage:Experiences of Asian and Pacific DevelopingCountries—P.B. Rana, November 1988

No. 43 Agricultural Price Policy in Asia:Issues and Areas of Reforms—I. Ali, November 1988

No. 44 Service Trade and Asian Developing Economies—M.G. Quibria, October 1989

No. 45 A Review of the Economic Analysis of PowerProjects in Asia and Identification of Areasof Improvement—I. Ali, November 1989

No. 46 Growth Perspective and Challenges for Asia:Areas for Policy Review and Research—I. Ali, November 1989

No. 47 An Approach to Estimating the PovertyAlleviation Impact of an Agricultural Project—I. Ali, January 1990

No. 48 Economic Growth Performance of Indonesia,the Philippines, and Thailand:The Human Resource Dimension—E.M. Pernia, January 1990

No. 49 Foreign Exchange and Fiscal Impact of a Project:A Methodological Framework for Estimation—I. Ali, February 1990

No. 50 Public Investment Criteria: Financialand Economic Internal Rates of Return—I. Ali, April 1990

No. 51 Evaluation of Water Supply Projects:An Economic Framework—Arlene M. Tadle, June 1990

No. 52 Interrelationship Between Shadow Prices, ProjectInvestment, and Policy Reforms:An Analytical Framework—I. Ali, November 1990

No. 53 Issues in Assessing the Impact of Projectand Sector Adjustment Lending—I. Ali, December 1990

No. 54 Some Aspects of Urbanizationand the Environment in Southeast Asia—Ernesto M. Pernia, January 1991

No. 55 Financial Sector and EconomicDevelopment: A Survey—Jungsoo Lee, September 1991

No. 56 A Framework for Justifying Bank-AssistedEducation Projects in Asia: A Reviewof the Socioeconomic Analysisand Identification of Areas of Improvement—Etienne Van De Walle, February 1992

No. 57 Medium-term Growth-StabilizationRelationship in Asian Developing Countriesand Some Policy Considerations—Yun-Hwan Kim, February 1993

No. 58 Urbanization, Population Distribution,and Economic Development in Asia—Ernesto M. Pernia, February 1993

No. 59 The Need for Fiscal Consolidation in Nepal:The Results of a Simulation

35

No. 1 International Reserves:Factors Determining Needs and Adequacy—Evelyn Go, May 1981

No. 2 Domestic Savings in Selected DevelopingAsian Countries—Basil Moore, assisted by A.H.M. NuruddinChowdhury, September 1981

No. 3 Changes in Consumption, Imports and Exportsof Oil Since 1973: A Preliminary Survey ofthe Developing Member Countriesof the Asian Development Bank—Dal Hyun Kim and Graham Abbott, September1981

No. 4 By-Passed Areas, Regional Inequalities,and Development Policies in SelectedSoutheast Asian Countries—William James, October 1981

No. 5 Asian Agriculture and Economic Development—William James, March 1982

No. 6 Inflation in Developing Member Countries:An Analysis of Recent Trends—A.H.M. Nuruddin Chowdhury and J. MalcolmDowling, March 1982

No. 7 Industrial Growth and Employment inDeveloping Asian Countries: Issues andPerspectives for the Coming Decade—Ulrich Hiemenz, March 1982

No. 8 Petrodollar Recycling 1973-1980.Part 1: Regional Adjustments andthe World Economy—Burnham Campbell, April 1982

No. 9 Developing Asia: The Importanceof Domestic Policies—Economics Office Staff under the direction of SeijiNaya, May 1982

No. 10 Financial Development and HouseholdSavings: Issues in Domestic ResourceMobilization in Asian Developing Countries—Wan-Soon Kim, July 1982

No. 11 Industrial Development: Role of SpecializedFinancial Institutions—Kedar N. Kohli, August 1982

No. 12 Petrodollar Recycling 1973-1980.Part II: Debt Problems and an Evaluationof Suggested Remedies—Burnham Campbell, September 1982

No. 13 Credit Rationing, Rural Savings, and FinancialPolicy in Developing Countries—William James, September 1982

No. 14 Small and Medium-Scale ManufacturingEstablishments in ASEAN Countries:Perspectives and Policy Issues—Mathias Bruch and Ulrich Hiemenz, March 1983

ECONOMIC STAFF PAPERS (ES)

No. 15 Income Distribution and EconomicGrowth in Developing Asian Countries—J. Malcolm Dowling and David Soo, March 1983

No. 16 Long-Run Debt-Servicing Capacity ofAsian Developing Countries: An Applicationof Critical Interest Rate Approach—Jungsoo Lee, June 1983

No. 17 External Shocks, Energy Policy,and Macroeconomic Performance of AsianDeveloping Countries: A Policy Analysis—William James, July 1983

No. 18 The Impact of the Current Exchange RateSystem on Trade and Inflation of SelectedDeveloping Member Countries—Pradumna Rana, September 1983

No. 19 Asian Agriculture in Transition: Key Policy Issues—William James, September 1983

No. 20 The Transition to an Industrial Economyin Monsoon Asia—Harry T. Oshima, October 1983

No. 21 The Significance of Off-Farm Employmentand Incomes in Post-War East Asian Growth—Harry T. Oshima, January 1984

No. 22 Income Distribution and Poverty in SelectedAsian Countries—John Malcolm Dowling, Jr., November 1984

No. 23 ASEAN Economies and ASEAN EconomicCooperation—Narongchai Akrasanee, November 1984

No. 24 Economic Analysis of Power Projects—Nitin Desai, January 1985

No. 25 Exports and Economic Growth in the Asian Region—Pradumna Rana, February 1985

No. 26 Patterns of External Financing of DMCs—E. Go, May 1985

No. 27 Industrial Technology Developmentthe Republic of Korea—S.Y. Lo, July 1985

No. 28 Risk Analysis and Project Selection:A Review of Practical Issues—J.K. Johnson, August 1985

No. 29 Rice in Indonesia: Price Policy and ComparativeAdvantage—I. Ali, January 1986

No. 30 Effects of Foreign Capital Inflowson Developing Countries of Asia—Jungsoo Lee, Pradumna B. Rana, and YoshihiroIwasaki, April 1986

No. 31 Economic Analysis of the EnvironmentalImpacts of Development Projects—John A. Dixon et al., EAPI, East-West Center,August 1986

No. 32 Science and Technology for Development:

—Filippo di Mauro and Ronald Antonio Butiong,July 1993

No. 60 A Computable General Equilibrium Modelof Nepal—Timothy Buehrer and Filippo di Mauro, October1993

No. 61 The Role of Government in Export Expansionin the Republic of Korea: A Revisit—Yun-Hwan Kim, February 1994

No. 62 Rural Reforms, Structural Change,and Agricultural Growth inthe People’s Republic of China—Bo Lin, August 1994

No. 63 Incentives and Regulation for Pollution Abatementwith an Application to Waste Water Treatment

—Sudipto Mundle, U. Shankar, and ShekharMehta, October 1995

No. 64 Saving Transitions in Southeast Asia—Frank Harrigan, February 1996

No. 65 Total Factor Productivity Growth in East Asia:A Critical Survey—Jesus Felipe, September 1997

No. 66 Foreign Direct Investment in Pakistan:Policy Issues and Operational Implications—Ashfaque H. Khan and Yun-Hwan Kim, July1999

No. 67 Fiscal Policy, Income Distribution and Growth—Sailesh K. Jha, November 1999

36

No. 1 Poverty in the People’s Republic of China:Recent Developments and Scopefor Bank Assistance—K.H. Moinuddin, November 1992

No. 2 The Eastern Islands of Indonesia: An Overviewof Development Needs and Potential—Brien K. Parkinson, January 1993

No. 3 Rural Institutional Finance in Bangladeshand Nepal: Review and Agenda for Reforms—A.H.M.N. Chowdhury and Marcelia C. Garcia,November 1993

No. 4 Fiscal Deficits and Current Account Imbalancesof the South Pacific Countries:A Case Study of Vanuatu—T.K. Jayaraman, December 1993

No. 5 Reforms in the Transitional Economies of Asia—Pradumna B. Rana, December 1993

No. 6 Environmental Challenges in the People’s Republicof China and Scope for Bank Assistance—Elisabetta Capannelli and Omkar L. Shrestha,December 1993

No. 7 Sustainable Development Environmentand Poverty Nexus—K.F. Jalal, December 1993

No. 8 Intermediate Services and EconomicDevelopment: The Malaysian Example—Sutanu Behuria and Rahul Khullar, May 1994

No. 9 Interest Rate Deregulation: A Brief Surveyof the Policy Issues and the Asian Experience—Carlos J. Glower, July 1994

No. 10 Some Aspects of Land Administrationin Indonesia: Implications for Bank Operations—Sutanu Behuria, July 1994

No. 11 Demographic and Socioeconomic Determinantsof Contraceptive Use among Urban Women inthe Melanesian Countries in the South Pacific:A Case Study of Port Vila Town in Vanuatu—T.K. Jayaraman, February 1995

No. 12 Managing Development throughInstitution Building— Hilton L. Root, October 1995

No. 13 Growth, Structural Change, and Optimal

OCCASIONAL PAPERS (OP)

Role of the Bank—Kedar N. Kohli and Ifzal Ali, November 1986

No. 33 Satellite Remote Sensing in the Asianand Pacific Region—Mohan Sundara Rajan, December 1986

No. 34 Changes in the Export Patterns of Asian andPacific Developing Countries: An EmpiricalOverview—Pradumna B. Rana, January 1987

No. 35 Agricultural Price Policy in Nepal—Gerald C. Nelson, March 1987

No. 36 Implications of Falling Primary CommodityPrices for Agricultural Strategy in the Philippines—Ifzal Ali, September 1987

No. 37 Determining Irrigation Charges: A Framework—Prabhakar B. Ghate, October 1987

No. 38 The Role of Fertilizer Subsidies in AgriculturalProduction: A Review of Select Issues—M.G. Quibria, October 1987

No. 39 Domestic Adjustment to External Shocksin Developing Asia—Jungsoo Lee, October 1987

No. 40 Improving Domestic Resource Mobilizationthrough Financial Development: Indonesia—Philip Erquiaga, November 1987

No. 41 Recent Trends and Issues on Foreign DirectInvestment in Asian and Pacific DevelopingCountries—P.B. Rana, March 1988

No. 42 Manufactured Exports from the Philippines:A Sector Profile and an Agenda for Reform—I. Ali, September 1988

No. 43 A Framework for Evaluating the EconomicBenefits of Power Projects—I. Ali, August 1989

No. 44 Promotion of Manufactured Exports in Pakistan—Jungsoo Lee and Yoshihiro Iwasaki, September1989

No. 45 Education and Labor Markets in Indonesia:A Sector Survey—Ernesto M. Pernia and David N. Wilson,September 1989

No. 46 Industrial Technology Capabilitiesand Policies in Selected ADCs—Hiroshi Kakazu, June 1990

No. 47 Designing Strategies and Policiesfor Managing Structural Change in Asia

—Ifzal Ali, June 1990No. 48 The Completion of the Single European Community

Market in 1992: A Tentative Assessment of itsImpact on Asian Developing Countries—J.P. Verbiest and Min Tang, June 1991

No. 49 Economic Analysis of Investment in Power Systems—Ifzal Ali, June 1991

No. 50 External Finance and the Role of MultilateralFinancial Institutions in South Asia:Changing Patterns, Prospects, and Challenges—Jungsoo Lee, November 1991

No. 51 The Gender and Poverty Nexus: Issues andPolicies—M.G. Quibria, November 1993

No. 52 The Role of the State in Economic Development:Theory, the East Asian Experience,and the Malaysian Case—Jason Brown, December 1993

No. 53 The Economic Benefits of Potable Water SupplyProjects to Households in Developing Countries—Dale Whittington and Venkateswarlu Swarna,January 1994

No. 54 Growth Triangles: Conceptual Issuesand Operational Problems—Min Tang and Myo Thant, February 1994

No. 55 The Emerging Global Trading Environmentand Developing Asia—Arvind Panagariya, M.G. Quibria, and NarhariRao, July 1996

No. 56 Aspects of Urban Water and Sanitation inthe Context of Rapid Urbanization inDeveloping Asia—Ernesto M. Pernia and Stella LF. Alabastro,September 1997

No. 57 Challenges for Asia’s Trade and Environment—Douglas H. Brooks, January 1998

No. 58 Economic Analysis of Health Sector Projects-A Review of Issues, Methods, and Approaches—Ramesh Adhikari, Paul Gertler, and AnneliLagman, March 1999

No. 59 The Asian Crisis: An Alternate View—Rajiv Kumar and Bibek Debroy, July 1999

No. 60 Social Consequences of the Financial Crisis inAsia—James C. Knowles, Ernesto M. Pernia, and MaryRacelis, November 1999

37

No. 1 Estimates of the Total External Debt ofthe Developing Member Countries of ADB:1981-1983—I.P. David, September 1984

No. 2 Multivariate Statistical and GraphicalClassification Techniques Appliedto the Problem of Grouping Countries—I.P. David and D.S. Maligalig, March 1985

No. 3 Gross National Product (GNP) MeasurementIssues in South Pacific Developing MemberCountries of ADB—S.G. Tiwari, September 1985

No. 4 Estimates of Comparable Savings in SelectedDMCs—Hananto Sigit, December 1985

No. 5 Keeping Sample Survey Designand Analysis Simple—I.P. David, December 1985

No. 6 External Debt Situation in AsianDeveloping Countries—I.P. David and Jungsoo Lee, March 1986

No. 7 Study of GNP Measurement Issues in theSouth Pacific Developing Member Countries.Part I: Existing National Accountsof SPDMCs–Analysis of Methodologyand Application of SNA Concepts—P. Hodgkinson, October 1986

No. 8 Study of GNP Measurement Issues in the SouthPacific Developing Member Countries.Part II: Factors Affecting IntercountryComparability of Per Capita GNP—P. Hodgkinson, October 1986

No. 9 Survey of the External Debt Situation

STATISTICAL REPORT SERIES (SR)

in Asian Developing Countries, 1985—Jungsoo Lee and I.P. David, April 1987

No. 10 A Survey of the External Debt Situationin Asian Developing Countries, 1986—Jungsoo Lee and I.P. David, April 1988

No. 11 Changing Pattern of Financial Flows to Asianand Pacific Developing Countries—Jungsoo Lee and I.P. David, March 1989

No. 12 The State of Agricultural Statistics inSoutheast Asia—I.P. David, March 1989

No. 13 A Survey of the External Debt Situationin Asian and Pacific Developing Countries:1987-1988—Jungsoo Lee and I.P. David, July 1989

No. 14 A Survey of the External Debt Situation inAsian and Pacific Developing Countries: 1988-1989—Jungsoo Lee, May 1990

No. 15 A Survey of the External Debt Situationin Asian and Pacific Developing Countries: 1989-1992—Min Tang, June 1991

No. 16 Recent Trends and Prospects of External DebtSituation and Financial Flows to Asianand Pacific Developing Countries—Min Tang and Aludia Pardo, June 1992

No. 17 Purchasing Power Parity in Asian DevelopingCountries: A Co-Integration Test—Min Tang and Ronald Q. Butiong, April 1994

No. 18 Capital Flows to Asian and Pacific DevelopingCountries: Recent Trends and Future Prospects—Min Tang and James Villafuerte, October 1995

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Poverty Interventions—Shiladitya Chatterjee, November 1995

No. 14 Private Investment and MacroeconomicEnvironment in the South Pacific IslandCountries: A Cross-Country Analysis—T.K. Jayaraman, October 1996

No. 15 The Rural-Urban Transition in Viet Nam:Some Selected Issues—Sudipto Mundle and Brian Van Arkadie, October1997

No. 16 A New Approach to Setting the FutureTransport Agenda—Roger Allport, Geoff Key, and Charles Melhuish,June 1998

No. 17 Adjustment and Distribution:The Indian Experience—Sudipto Mundle and V.B. Tulasidhar, June 1998

No. 18 Tax Reforms in Viet Nam: A Selective Analysis—Sudipto Mundle, December 1998

No. 19 Surges and Volatility of Private Capital Flows toAsian Developing Countries: Implicationsfor Multilateral Development Banks—Pradumna B. Rana, December 1998

No. 20 The Millennium Round and the Asian Economies:An Introduction—Dilip K. Das, October 1999

No. 21 Occupational Segregation and the GenderEarnings Gap—Joseph E. Zveglich, Jr. and Yana van der MeulenRodgers, December 1999

No. 22 Information Technology: Next Locomotive ofGrowth?—Dilip K. Das, June 2000

38

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Duo Qin, Marie Anne Cagas, Geoffrey Ducanes, Nedelyn Magtibay-Ramos, and Pilipinas Quising

Printed in the Philippines

Technical Note SeriesECONOMICS AND RESEARCH DEPARTMENTERD

No.18Ju l y 2006

Asian Development Bank6 ADB Avenue, Mandaluyong City1550 Metro Manila, Philippineswww.adb.org/economicsISSN: 1655-5236Publication Stock No.

About the Asian Development Bank

The work of the Asian Development Bank (ADB) is aimed at improving the welfare of the people in Asia and the Pacific, particularly the nearly 1.9 billion who live on less than $2 a day. Despite many success stories, Asia and the Pacific remains home to two thirds of the world’s poor. ADB is a multilateral development finance institution owned by 66 members, 47 from the region and 19 from other parts of the globe. ADB’s vision is a region free of poverty. Its mission is to help its developing member countries reduce poverty and improve the quality of life of their citizens.

ADB’s main instruments for providing help to its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. ADB’s annual lending volume is typically about $6 billion, with technical assistance usually totaling about $180 million a year.

ADB’s headquarters is in Manila. It has 26 offices around the world and has more than 2,000 employees from over 50 countries. .


Duo Qin, Marie Anne Cagas, Geoffrey Ducanes, Nedelyn Magtibay-Ramos, and Pilipinas Quising compare the forecast performance of the automatic leading indicator (ALI) method with the macro econometric structural model (MESM) and seek ways of improving the ALI method. The ALI method is found to produce better forecasts than MESMs in general, but the method is found to involve greater uncertainty in choosing indicators, mixing data frequencies, and utilizing unrestricted vector auto-regressions. Two possible improvements are found to reduce the uncertainty.

Forecasting Inflation and GDP Growth: Automatic Leading ......ERD TECHNICAL NOTE NO. 18 FORECASTING...

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Transcript of Forecasting Inflation and GDP Growth: Automatic Leading ......ERD TECHNICAL NOTE NO. 18 FORECASTING...