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15 - Turning points in economic time series, loss structures, and Bayesian forecasting (1990)

Published online by Cambridge University Press:  24 October 2009

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
Gaurand M. Gulati
Affiliation:
Georgetown University, Law Center, Washington, DC
Arnold Zellner
Affiliation:
University of Chicago
Franz C. Palm
Affiliation:
Universiteit Maastricht, Netherlands
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Summary

Introduction

In a letter commenting on a draft of Zellner (1987), Barnard (1987) wrote, “I very much liked your emphasis on the need for sophisticated, simple model building and testing in social science.” Apparently, Barnard and many other scientists are disturbed by the complexity of many models put forward in econometrics and other social sciences. And indeed we think that they should be disturbed since not a single complicated model has worked very well in explaining past data and in predicting as yet unobserved data. In view of this fact, in Garcia-Ferrer et al. (1987) and Zellner and Hong (1989), a relatively simple, one-equation model for forecasting countries' annual output growth rates was formulated, applied, and found to produce good forecasts year by year, 1974–84 for eighteen countries. This experience supports Barnard's and many others' preference for the use of sophisticatedly simple models and methods. See Zellner (1988) for further discussion of this issue.

In the present chapter, we extend our previous work to consider the problem of forecasting future values and turning points of economic time series given explicit loss structures. Kling (1987, pp. 201–4) has provided a good summary of past work on forecasting turning points by Moore (1961, 1983), Zarnowitz (1967), Wecker (1979), Moore and Zarnowitz (1982), Neftci (1982), and others. In this work there is an emphasis on the importance and difficulty of forecasting turning points.

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Publisher: Cambridge University Press
Print publication year: 2004

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References

Barnard, G. A. (1987), Personal communication
Burns, A. F. and W. C. Mitchell (1946), Measuring Business Cycles (New York, National Bureau of Economic Research)
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 (1), 53–67; chapter 13 in this volumeGoogle Scholar
Kling, J. L. (1987), “Predicting the turning points of business and economic time series,” Journal of Business 60, 201–38CrossRefGoogle Scholar
Moore, G. H. (1961), Business Cycle Indicators 1 (Princeton, Princeton University Press)
Moore, G. H. (1983), Business Cycles, Inflation and Forecasting, 2nd edn. (Cambridge, Mass., Ballinger)
Moore, G. H. and Zarnowitz, V. (1982), “Sequential signals of recession and recovery,” Journal of Business 55, 57–85Google Scholar
Neftci, S. H. (1982), “Optimal prediction of cyclical downturns,” Journal of Economic Dynamics and Control 4, 225–41CrossRefGoogle Scholar
Press, S. J. and Zellner, A. (1978), “Posterior distribution for the multiple correlation coefficient with fixed regressors,” Journal of Econometrics 8, 307–21CrossRefGoogle Scholar
Varian, H. (1975), “A Bayesian approach to real estate assessment,” in S. E. Fienberg and A. Zellner (eds.), Studies in Bayesian Econometrics and Statistics in Honor of Leonard J. Savage (Amsterdam, North-Holland), 195–208
Wecker, W. E. (1979), “Predicting the turning points of a time series,” Journal of Business 52, 35–50CrossRefGoogle Scholar
Zarnowitz, V. (1967), An Appraisal of Short-Term Economic Forecasts (New York, National Bureau of Economic Research)
Zarnowitz, V. (1985), “Recent work on business cycles in historical perspective,” Journal of Economic Literature 23, 523–80Google Scholar
Zellner, A. (1986), “Bayesian estimation and prediction using asymmetric loss functions,” Journal of the American Statistical Association 81, 446–51CrossRefGoogle Scholar
Zellner, A. (1987), “Science, economics and public policy,” American Economist 31, 3–7CrossRefGoogle Scholar
Zellner, A. (1988), “Causality and causal laws in economics,” in D. Aigner and A. Zellner (eds.), Causality, special issue of the Journal of Econometrics 39, 7–21CrossRef
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 volumeCrossRefGoogle Scholar

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