the strutural econometric

the strutural econometric

(Parte 1 de 5)

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The Structural Econometric Time Series Analysis Approach

Bringing together a collection of previously published work, this book provides a timely discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes, and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation, and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the new Marshallian macroeconomic model ( ) that features demand, supply, and entry equations for major sectors of economies is analyzed and described. This volume will prove invaluable to professionals, academics and students alike.

isH.G.B.AlexanderDistinguishedServiceProfessor Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago and Adjunct Professor, University of California atBerkeley. He has published books and many articles on the theory and application of econometrics and statistics to a wide range of problems.

. is Professor of Econometrics, Faculty of Economics and Business Administration, Maastricht University. He has published many articles on the theory and application of econometrics and statistics to a wide range of problems.

The Structural Econometric Time Series Analysis Approach

Edited by Arnold Zellner and Franz C. Palm

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo

Cambridge University Press The Edinburgh Building, Cambridge , UK


First published in print format

© Cambridge University Press, 2004 2004

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Published in the United States of America by Cambridge University Press, New York hardback eBook (EBL) eBook (EBL) hardback


List of contributors page ix Acknowledgments xi Introduction xiii

Part I The SEMTSA approach

1 Time series analysis and simultaneous equation econometric models (1974) 3 .

2 Statistical analysis of econometric models (1979) 4

3 Structural econometric modeling and time series analysis: an integrated approach (1983) 96 .

vi Contents

4 Time series analysis, forecasting, and econometric modeling: the structural econometric modeling, time series analysis (SEMTSA) approach (1994) 175

5 Large-sample estimation and testing procedures for dynamic equation systems (1980) 201 .

Part I Selected applications

6 Time series and structural analysis of monetary models of the US economy (1975) 243 .

7 Time series versus structural models: a case study of

Canadian manufacturing inventory behavior (1975) 288 .

9A time series analysis of seasonality in econometric models (1978) 332 .

10 The behavior of speculative prices and the consistency of economic models (1985) 397 .

Contents vii

1 A comparison of the stochastic processes of structural and time series exchange rate models (1987) 405 . .

Part I Macroeconomic forecasting and modeling

14 Forecasting international growth rates using Bayesian shrinkage and other procedures (1989) 485

16 Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques (1991) 528 , , -

17 Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates (1993) 559 -

19 Forecasting turning points in countries’ output growth rates: a response to Milton Friedman (1999) 612 -

Pooling in dynamic panel data models: an application to viii Contents

Part IV Disaggregation, forecasting, and modeling

21 Forecasting turning points in metropolitan employment growth rates using Bayesian techniques (1990) 637 . .

2 A note on aggregation, disaggregation, and forecasting performance (2000) 656

23 The Marshallian macroeconomic model (2000) 667

24 Bayesian modeling of economies and data requirements (2000) 677

Subject index 707 Author index 712


, ., Professor of Economics, Department of Economics, Boston College, Boston, MA

, , Chicago Partners, LLC, Chicago, IL

, . , Professor of Economics, Department of Economics, Princeton University, Princeton, NJ

, ., Professor Emeritus of Economics, Department of Economics, Johns Hopkins University, Baltimore, MD

, , Professor of Economics, Ohio State University, Columbus, OH

- , , Professor of Economics, Departamento de Analsis Economico: Economia Cuantitava, Universidad Autonoma de Madrid

, ., Dean, School of Business Administration 364, School of Business, New York University at Albany, NY

, , Department of Economics, Sookmyung Women’s University, Seoul

, , Professor of Economics, Sloan School of Management, MIT, Cambridge, MA

, .,ProfessorofEconomics,DepartmentofEconomics, University of Toledo, Toledo, OH x List of contributors

, ., Professor of Economics, Department of Economics, Pennsylvania State University, University Park, PA

, , Department of Economics, University of Toledo, Toledo, OH

, , SIMC, Banco de Espana, Madrid

, . , Department of Economics, College of Nevada Las Vegas, Las Vegas, NV

, - , Department of Economics, Hankuk University of Foreign Studies, Seoul

, . ,ProfessorofEconometrics,FacultyofEconomicsand Business Administration, Universiteit Maastricht

, . , Professor of Econometrics, Faculty of Economics and Business Administration, Universiteit Maastricht

, ., Professor of Economics, Simon Graduate School of Business Administration, University of Rochester, Rochester, NY

, . ,ProfessorofEconometrics,DepartmentofEconomics, London School of Economics and Political Science, London

, ., Professor of Economics, Department of Economics, University of California, Berkeley, CA

, . , Professor of Economics, Department of Economics, Princeton University, Princeton, NJ

, , Department of Economics University of California, Irvine, CA

, ., Professor of Economics, Department of Economics, Indiana University, Bloomington, IN

, . , McIntire School of Commerce, University of Virginia, Charlottesville, VA

, ,H .G .B . Alexander Distinguished Service Professor Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL


The following chapters are reprinted with the kind permission of the publishers listed below. Chapter 1, from the Journal of Econometrics, and chapters 1, 5, 10, 12, 16, 17, 19, and 20, from the Journal of Econometrics, by North-Holland Publishing Company; chapter 2, from the Journal of the American Statistical Association, and chapters 13, 18, and 20, from the Journal of Business and Economic Statistics,b y the American Statistical Association;chapters3and9,fromtheBureauoftheCensus,USDepartment of Commerce, by the US Government Printing Office; chapters 4 and 2, from the Journal of Forecasting,b yJ ohn Wiley & Sons; chapter 6, from Sankhya: The Indian Journal of Statistics,b y the Indian Statistical Institute; chapters 7 and 8, from the International Economic Review,b yt he Wharton School of Finance and Commerce, University of Pennsylvania and the Osaka University Institute of Economic Research Association; chapter 1, from the Review of Economics and Statistics,b y MIT Press; chapter 21, from the Journal of Regional Science,b y the Regional Science Research Institute, in cooperation with the Wharton School of Finance and Commerce, University of Pennsylvania, Blackwell Publishing Ltd; chapter 15, by North-Holland Publishing Company; chapter 23, by Kluwer Academic Publishers; and chapter 24 by Cambridge University Press.


In the early 1970s we were concerned about the relationships between multivariate and univariate time series models, such as those brilliantly analyzed by Quenouille (1957) and Box and Jenkins (1970) and multivariate dynamic structural econometric models that had been and are widely employed in explanation, prediction and policy-making. Fortunately, we discovered the relationships and reported them in our paper, Zellner and Palm (1974) that is included in part I of this volume (chapter 1). See also the other general chapters in part I discussing general features of our approach, the reactions of leading researchers, and many useful references to the literature.

Having discovered the algebraic relations connecting statistical time series and structural econometric models, we next considered how this discovery might be used to produce improved models. In this connection, we thought it important not only to emphasize a philosophical preference for sophisticatedly simple models that is discussed in several chapters in part I and Zellner, Keuzenkamp, and McAleer (2001), but also operational techniques that would help researchers actually produce improved models. As illustrated in the chapters included in this volume, our approach involves (1) deducing algebraically the implied marginal processes and transfer functions for individual variables in a multi-equation model, e.g. a vector autoregression (VAR) or a structural econometric model (SEM), and (2) comparing these derived equations’ forms and properties with those derived from the data by use of empirical model identification and testing techniques. See Palm and Zellner (1980), included in part I (chapter 5) for some early estimation and testing procedures that have been improved over the years. If the information in the data is compatible with the empirically determined, simple time series models and not with those implied by a VAR or SEM, then we conclude that the VAR or SEM needs reformulation and improvement. See, for example our (1975) paper in part I (chapter 6) analyzing monetary models of the US economy and other papers for applications of this approach to many other problems including Trivedi (1975) on modeling xiii xiv Introduction inventory behavior (chapter 7), Evans (1978) on the German hyperinflation (chapter 8), Plosser (1976) on seasonality (Chapter 9), Webb (1985) on behavior of speculative prices (chapter 10), Ahking and Miller (1987) on exchange rate models (chapter 1), and Maravall and Mathis (1994) on diagnosis of VAR models using French macroeconomic data (chapter 12). These studies demonstrate well the usefulness of our SEMTSA approach in analyzing, comparing, and improving models.

Since there is often no satisfactory model available, in part I we illustrate how relatively simple forecasting equations have been developed, studied, and tested in point and turning point forecasting experiments using modern estimation and forecasting techniques. Here the objective is to get forecasting equations that work well in point and turning point forecasting and have reasonable dynamic properties. Then the objective is to produce reasonable economic models to rationalize the good empirical performance of these empirical forecasting equations. Thus we do not in the present instance go from theory to the data but reverse the process by going from what works well empirically to theory that explains this unusual empirical finding. As mentioned in several chapters in part I, the empirical forecasting equations for countries’ annual GDP growth rates have been rationalized by Hong (1989) in terms of a Hicksian [ISLM macroeconomic model, by Min (1992) in terms of a generalized real business cycle model that he formulated and by Zellner and Anton (1986) in terms of an aggregate demand and supply model. Thus the empirical relations studied intensively in the chapters included in part I have some theoretical as well as empirical support. Note, too, that many methodological tools were developed and tested in the chapters on empirical forecasting work in part I – namely, Bayesian shrinkage estimation and prediction, optimal turning point forecasting techniques, optimal Bayesian model-combining or pooling methods, etc. Also, comparisons of forecasting root mean-squared errors (RMSEs) and mean absolute errors (MAEs) indicate that various simple forecasting equations’ performance is competitive with that of certain large-scale macroeconometric models for many economies. See, for example some comparisons reported in Garcia-Ferrer et al. (1987) and Hoogstrate, Palm, and Pfann (2000) (chapter 13 and 18) for some improved results that utilize various pooling techniques in analysis and forecasting of panel data for eighteen countries.

While the studies in part I provide useful, improved macroeconomic results, it is the case that aggregation of output and other kinds of data, say over sectors of an economy, can involve a loss of valuable information, as has been discussed many times in the past. Thus part IV presents chaptersdealingwithdisaggregation,forecasting,andmodeling.Asimple

Introduction xv experiment, reported in Zellner and Tobias (2000) (chapter 2) shows empirically how disaggregation can result in improved forecasting precision in connection with forecasting the annual medians of eighteen countries’ growth rates. In chapters 20 and 21 by LeSage and Magura (1990) and LeSage (1990), it is shown how shrinkage point and turning point forecasting procedures perform using regional data. Then in Zellner (2000) and in Zellner and Chen (2000) (chapters 23 and 24), Marshallian sector models of industrial sectors are formulated, building on the earlier work of Veloce and Zellner (1985), and tested in forecasting experiments using annual data for eleven sectors of the US economy. The annual output forecasts of the sectors are added to get a forecast of total GDP and its growth rate year by year. Such forecasts are compared with forecasts derived from models implemented with aggregate data. In this instance, it was found that it pays to disaggregate. Further work to improve and expand the Marshallian sector model in line with the SEMTSA approach is described in these chapters.

In summary, pursuing the SEMTSA approach over the years has been an exciting experience that has led to new empirical findings, improved and novel methodological tools, and improved models. We thank all those whohavecontributedtothesepositivedevelopmentsandhopethatfuture developmentswillbeevenbetter.Also,thankstotheUSNationalScience Foundation and the H. G. B. Alexander Endowment Fund, University of Chicago, for financial support. Ashwin Rattan at Cambridge University Press, provided much help in arranging for the publication of our book, for which we are most grateful.

(Parte 1 de 5)