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

doi:10.1017/CBO9780511493171.015

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

Published online by Cambridge University Press: 24 October 2009

Arnold Zellner and

Chansik Hong

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
Chansik Hong: Affiliation:
Department of Economics, Sookmyung Women's University, Seoul
Arnold Zellner: Affiliation:
University of Chicago
Franz C. Palm: Affiliation:
Universiteit Maastricht, Netherlands

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Summary

Introduction

In our past work, Garcia-Ferrer et al. (1987), we employed several methods to forecast growth rates of real output (GNP or GDP) for eight European Economic Community (EEC) countries and the United States year by year for the period 1974–81. It was found that diffuse prior or least squares forecasts based on an autoregressive model of order 3 including leading indicator variables, denoted by AR(3)LI, were reasonably good in terms of forecast root mean-squared error (RMSE) relative to those of three naive models and of AR(3) models without leading indicator variables. Also, it was found that certain shrinkage forecasting techniques produced improved forecasting results for many countries and that our simple mechanical forecasts compared favorably with [Organization for Economic Cooperation and Development] (OECD) annual forecasts which were constructed using elaborate models and judgmental adjustments.

In the present chapter our main objectives are to extend our earlier work by (1) providing further analysis of shrinkage forecasting techniques, (2) providing forecasting results for an extended time period, 1974–84, for our past sample of nine countries, (3) applying our forecasting techniques to data relating to nine additional countries, and (4) reporting results of forecasting experiments using a simple modification of our AR(3)LI model.

The importance of checking the forecasting performance of our techniques using new data is reflected in objectives (2) and (3) above.

Type: Chapter
Information: The Structural Econometric Time Series Analysis Approach , pp. 485 - 505

DOI: https://doi.org/10.1017/CBO9780511493171.015 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2004

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References

Bowman, H. W. and A. M. Laporte (1975), “Stochastic optimization in recursive equation systems with random parameters with an application to control of the money supply,” in S. E. Fienberg and A. Zellner (eds.), Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage (Amsterdam, North-Holland), 441–62

Garcia-Ferrer, A., Highfield, R. A., Palm, F. C., and Zellner, A. (1987), “Macro-economic forecasting using pooled international data,” Journal of Business and Economic Statistics 5(1), 53–67; chapter 13 in this volumeGoogle Scholar

Highfield, R. A. (1986), “Forecasting with Bayesian state – space models,” Unpublished doctoral dissertation, Graduate School of Business, University of Chicago

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

Miller, R. B. and W. G. Fortney (1984), “Industry-wide expense standards using random coefficient regression,” Insurance: Mathematics and Economics 3, 19–33CrossRef

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, Proceedings of the Conference on Applied Time Series Analysis of Economic Data, October 13–15, 1981 (Washington, DC, US Government Printing Office), 199–233; see chapter 3 in this volume

Smyth, D. J. (1983), “Short-run macroeconomic forecasting: the OECD performance,” Journal of Forecasting 2, 37–49CrossRef Google Scholar

Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics (New York, John Wiley); reprinted (Melbourne, Fla., Krieger, 1987)

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. (1983), “Applications of Bayesian analysis in econometrics,” The Statistician 32, 23–34CrossRef Google Scholar

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

Zellner, A. (1986), “On assessing prior distributions and Bayesian regression analysis with g-prior distributions,” in P. K. Goel and A. Zellner (eds.), Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti (Amsterdam, North-Holland), 233–43

Zellner, A. (1987), “Macroeconomics, econometrics and time series analysis,” Revista Espanola de Economia 4, 3–9Google 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. (1975), “Time series and structural analysis of monetary models of the US economy,” Sankhyā: The Journal of the Indian Statistical Association, Series C 37, 12–56; chapter 6 in this volumeGoogle Scholar