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

doi:10.1017/CBO9780511493171.018

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

Published online by Cambridge University Press: 24 October 2009

Chung-Ki Min and

Arnold Zellner

Edited by

Arnold Zellner and

Franz C. Palm

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Chung-Ki Min: Affiliation:
Department of Economics, Hankuk University of Foreign Studies, Seoul
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

Introduction

In past work, Garcia-Ferrer et al. (1987) and Zellner and Hong (1989), variants of a relatively simple autoregressive model of order three containing lagged leading indicator variables, called an ARLI model, provided good one-year-ahead forecasts of annual output growth rates for eighteen industrial countries, 1974–84. In Zellner, Hong, and Gulati (1990) and Zellner, Hong, and Min (1991), this ARLI model and variants of it produced good turning point forecasts, about 70–80 percent of 158 turning points correctly forecasted. In Hong (1989), the ARLI model's cyclical properties were analyzed and its forecasting performance was shown to be slightly superior to that of a version of Barro's “money surprise” model. LeSage (1989) and LeSage and Magura (1990) have used ARLI models to forecast employment growth rates and turning points in them for eight metropolitan labor markets with satisfactory results. Blattberg and George (1991) used similar techniques in successfully forecasting sales of different brands of a product.

Some of our past work has involved use of fixed parameter models (FPMs) and time-varying parameter models (TVPMs). In the present chapter, we derive and compute posterior odds relating to our FPMs and TVPMs using data for eighteen countries, 1973–87. While there are many reasons – Lucas effects, aggregation effects, wars, etc. – for believing that parameters may be time-varying, economic theorists' models are generally fixed parameter models. Our calculated posterior odds will shed some light on the parameter constancy issue and are used to choose between FPMs' and TVPMs' forecasts year by year.

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Type: Chapter
Information: The Structural Econometric Time Series Analysis Approach , pp. 559 - 589

DOI: https://doi.org/10.1017/CBO9780511493171.018 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2004

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