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

doi:10.1017/CBO9780511493171.005

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

Published online by Cambridge University Press: 24 October 2009

Arnold Zellner

Edited by

Arnold Zellner and

Franz C. Palm

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Arnold Zellner: Affiliation:
Professor Emeritus of Economics and Statistics, Graduate School of Business, University of Chicago, Chicago, IL
Arnold Zellner: Affiliation:
University of Chicago
Franz C. Palm: Affiliation:
Universiteit Maastricht, Netherlands

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Summary

In this chapter an account of our experiences in modeling and forecasting the annual output growth rates of 18 industrialized countries is presented. A structural econometric modeling, time-series analysis (SEMTSA) approach is described and contrasted with other approaches. Theoretical and applied results relating to variable and model selection and point and turning-point forecasting are discussed. A summary of results and directions for future research concludes the chapter.

Introduction

In this chapter we shall provide an account of some of our experiences in modeling, forecasting, and interpreting time series data. Since the literature on these topics is so extensive, a comprehensive survey would require one or probably more volumes. Thus, we have decided to describe our approach, the experience that we have had with it, and its relation to a part of the statistical and econometric time series literature.

Obtaining good macroeconomic and microeconomic and other time series models is important since they are useful in explanation, prediction, and policy-making or control. The basic issue that is addressed in this chapter is how to produce such good time series models. Our SEMTSA approach (see, e.g., Zellner and Palm 1974, 1975; Plosser 1976, 1978; Zellner 1979, 1984, 1991; Palm 1983; Wallis 1983; Webb 1985; Manas-Anton 1986; Hong 1989; Min 1991) will be briefly compared to several other approaches that have emerged in the literature. Rather than just present theoretical procedures that may be useful in producing good models, an account will be given of both theoretical and applied results in what follows.

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Type: Chapter
Information: The Structural Econometric Time Series Analysis Approach , pp. 175 - 200

DOI: https://doi.org/10.1017/CBO9780511493171.005 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2004

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References

Adelman, I. and Adelman, F. L. (1959), “The dynamic properties of the Klein–Goldberger model,” Econometrica 27, 569–625CrossRef Google Scholar

Barro, R. J. (1978), “Unanticipated money, output and the price level in the United States,” Journal of Political Economy 86, 549–80CrossRef Google Scholar

Burns, A. F. and W. C. Mitchell (1946), Measuring Business Cycles, (New York, National Bureau of Economic Research

Christ, C. F. (1951), “A test of an econometric model for the United States, 1921–1947,” in Conference on Business Cycles (New York, National Bureau of Economic Research, 35–107Google Scholar

Cooper, R. L. (1972), “The predictive performance of quarterly econometric models of the United States,” in B. G. Hickman (ed.), Econometric Models of Cyclical Behavior (New York, Columbia University Press), 813–926

Diebold, F. X. (1989), “Forecast combination and encompassing, reconciling two divergent literatures,” International Journal of Forecasting 5, 589–92CrossRef Google Scholar

Espasa, A. and M. L. Matea (1990), “Underlying inflation in the Spanish economy: estimation and methodology,” Bank of Spain, manuscript

Fair, R. C. (1991), “How might the debate be resolved?,” Paper presented at a conference on the business cycle held at the Federal Reserve Bank of St. Louis, October

Friedman, M. (1951), “Comment on ‘A test of an econometric model for the United States, 1921–1947’, by Carl Christ,” in Conference on Business Cycles (New York, National Bureau of Economic Research), 107–14Google Scholar

Garcia-Ferrer, A., Highfield, R. A., Palm, F. C., and Zellner, A. (1987), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 5, 53–67; chapter 13 in this volumeGoogle Scholar

Geweke, J. (1988), “The secular and cyclical behavior of real GNP in nineteen OECD countries, 1957–83,” Journal of Business and Economic Statistics 6, 479–86Google Scholar

Harvey, A. C. and Todd, P. H. J. (1983), “Forecasting economic time series with structural and Box–Jenkins models: a case study,” Journal of Business and Economic Statistics 1, 299–307Google Scholar

Hickman, B. G. (ed.) (1972), Econometric Models of Cyclical Behavior (New York, Columbia University Press)

Highfield, R. A. (1986), Forecasting with Bayesian State Space Models, Doctoral dissertation, Graduate School of Business, University of Chicago

Hong, C. (1989), Forecasting Real Output Growth Rates and Cyclical Properties of Models: A Bayesian Approach, Doctoral dissertation, Department of Economics, University of Chicago

Jeffreys, H. (1967), Theory of Probability, 3rd edn. (Oxford, Oxford University Press)

Kling, J. L. (1987), “Predicting the turning points of business and economic time series,” Journal of Business 60, 201–38CrossRef Google Scholar

Lindley, D. V. and Smith, A. F. M. (1972), “Bayes estimates for the linear model,” Journal of the Royal Statistical Society, Series B 34, 1–41Google Scholar

Litterman, R. B. (1980), “A Bayesian procedure for forecasting with Bayesian vector autoregressions,” Department of Economics, MIT, Cambridge, Mass., unpublished manuscript

Litterman, R. B. (1986), “A statistical approach to economic forecasting,” Journal of Business and Economic Statistics 4, 1–4Google Scholar

Manas-Anton, L. A. (1986), Empirical Regularities in Short-Run Exchange Rate Behavior, Doctoral dissertation, Department of Economics, University of Chicago

McNees, S. K. (1986), “Forecasting accuracy of alternative techniques: a comparison of US macroeconomic forecasts,” Journal of Business and Economic Statistics 4, 5–15Google Scholar

Meese, R. and K. Rogoff (1983), “The out-of-sample failure of empirical exchange rate models: sampling error or misspecification?,” in J. A. Frenkel (ed.), Exchange Rates and International Economics (Chicago, University of Chicago Press)

Min, C. (1991), Economic Analysis and Forecasting of International Growth Rates Using a Bayesian Approach, Thesis proposal, Department of Economics, University of Chicago

Min, C. and Zellner, A. (1993), “Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates,” Journal of Econometrics, Annals 56, 89–118; chapter 17 in this volumeCrossRef Google Scholar

Mittnik, S. (1990), “Macroeconomic forecasting using pooled international data,” Journal of Business and Economic Statistics 8, 205–8Google Scholar

Nelson, C. R. (1972), “The prediction performance of the FRB–MIT–Penn model of the US economy,” American Economic Review 62, 902–17Google Scholar

Nelson, C. R. and Plosser, C. I. (1982), “Trends and random walks in macroeconomic time series,” Journal of Monetary Economics 10, 139–62CrossRef Google Scholar

Otter, P. W. (1990), “Canonical correlation in multivariate time series analysis with an application to one-year-ahead and multiyear-ahead macroeconomic forecasting,” Journal of Business and Economic Statistics 8, 453–7Google Scholar

Palm, F. C. (1983), “Structural econometric modeling and time series analysis: an integrated approach,” in A. Zellner (ed.), Applied Time Series Analysis of Economic Data (Washington, DC, Bureau of the Census, US Department of Commerce), 199–233; chapter 3 in this volume

Palm, F. C. and Zellner, A. (1992), “To combine or not to combine? Issues of combining forecasts,” Journal of Forecasting 11, 687–701CrossRef Google Scholar

Plosser, C. I. (1976), A Time Series Analysis of Seasonality in Econometric Models with an Application to a Monetary Model, Doctoral dissertation, Graduate School of Business, University of Chicago

Plosser, C. I. (1978), “A time series analysis of seasonality in econometric models,” in A. Zellner (ed.), Seasonal Analysis of Economic Time Series (Washington, DC, Bureau of the Census, US Department of Commerce, 365–407; chapter 9 in this volume

Poirier, D. J. (ed.) (1991), “Bayesian Empirical Studies in Economics and Finance,” Journal of Econometrics, Annals 49

Quenouille, M. H. (1957), The Analysis of Multiple Time Series (London: C. Griffin & Co.)

Stock, J. H. and M. W. Watson (1991), “Predicting recessions,” Paper presented at Conference on Recent Advances in Time Series Analysis and Their Impact on Economic Forecasting, Madrid

Swamy, P. A. V. B. (1971), Statistical Inference in Random Coefficient Regression Models (New York, Springer-Verlag)

Wallis, K. F. (1983), “Models for X-11 and X-11 forecast procedures for preliminary and revised seasonal adjustments,” in A. Zellner (ed.), Applied Time Series Analysis of Economic Data (Washington, DC, Bureau of the Census, US Department of Commerce), 3–11

Webb, R. I. (1985), “The behavior of speculative prices and the consistency of economic models,” Journal of Econometrics 27, 123–30; chapter 10 in this volumeCrossRef Google Scholar

Wecker, W. E. (1979), “Predicting the turning points of a time series,” Journal of Business 52, 35–50CrossRef Google Scholar

Wolff, C. C. P. (1985), Exchange Rate Models, Parameter Variation and Innovations: A Study on the Forecasting Performance of Empirical Models of Exchange Rate Determination, Doctoral dissertation, Graduate School of Business, University of Chicago

Wolff, C. C. P. (1987), “Time-varying parameters and the out-of-sample forecasting performance of structural exchange rate models,” Journal of Business and Economic Statistics 5, 87–97Google Scholar

Zellner, A. (1979), “Statistical analysis of econometric models,” Journal of the American Statistical Association 74, 628–51; chapter 2 in this volumeCrossRef Google Scholar

Zellner, A. (1984), Basic Issues in Econometrics (Chicago, University of Chicago Press), paperback edn., 1987

Zellner, A. (1991), “Comment on Ray C. Fair's thoughts on ‘How might the debate be resolved?’,” Paper presented at a conference on the business cycle held at the Federal Reserve Bank of St. Louis, October

Zellner, A. and Hong, C. (1989), “Forecasting international growth rates using Bayesian shrinkage and other procedures,” Journal of Econometrics, Annals 40, 183–202; chapter 14 in this volumeCrossRef Google Scholar

Zellner, A., C. Hong, and G. M. Gulati (1990), “Turning points in economic time series, loss structures, and Bayesian forecasting,” part VI in S. Geisser, J. S. Hodges, S. J. Press, and A. Zellner (eds.), Bayesian and Likelihood Methods in Statistics and Econometrics: Essays in Honor of George A. Barnard (Amsterdam, North-Holland), 371–93; chapter 15 in this volume

Zellner, A., Hong, C., and Min, C. (1991), “Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques,” Journal of Econometrics 49, 275–304; chapter 16 in this volumeCrossRef Google Scholar

Zellner, A. and Palm, F. C. (1974), “Time series analysis and simultaneous equation econometric models,” Journal of Econometrics 2, 17–54; chapter 1 in this volumeCrossRef Google Scholar

Zellner, A. and Palm, F. C. (1975), “Time series and structural analysis of monetary models of the US economy,” Sankhyā. The Indian Journal of Statistics, Series C 37, 12–56; chapter 6 in this volumeGoogle Scholar

Zellner, A. and S. Peck (1973), “Simulation experiments with a quarterly macroeconometric model of US economy,” in A. A. Powell and R. A. Williams (eds.), Econometric Studies of Macro and Monetary Relations (Amsterdam, North-Holland), 149–68; reprinted in A. Zellner, Basic Issues in Econometrics (Chicago, University of Chicago Press)

Zellner, A. and Revankar, N. S. (1969), “Generalized production functions,” Review of Economic Studies 36 (2), 241–50CrossRef Google Scholar

Zellner, A. and A. Siow (1980), “Posterior odds ratios for selected regression hypotheses,” in J. M. Bernardo et al. (eds.), Bayesian Statistics (Valencia, University Press), 585–603CrossRef