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

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
Franz C. Palm
Affiliation:
Professor of Econometrics, Faculty of Economics and Business Administration, Maastricht University
Arnold Zellner
Affiliation:
University of Chicago
Franz C. Palm
Affiliation:
Universiteit Maastricht, Netherlands
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Summary

Introduction

In this chapter we take up the analysis of dynamic simultaneous equation models (SEMs) within the context of general linear multiple time series processes such as studied by Quenouille (1957). As noted by Quenouille, if a set of variables is generated by a multiple time series process, it is often possible to solve for the processes generating individual variables, namely the “final equations” of Tinbergen (1940), and these are in the autoregressive-moving average (ARMA) form. ARMA processes have been studied intensively by Box and Jenkins (1970). Further, if a general multiple time series process is appropriately specialized, we obtain a usual dynamic SEM in structural form. By algebraic manipulations, the associated reduced form and transfer function equation systems can be derived. In what follows, these equation systems are presented and their properties and uses are indicated.

It will be shown that assumptions about variables being exogenous, about lags in structural equations of SEMs, and about serial correlation properties of structural disturbance terms have strong implications for the properties of transfer functions and final equations that can be tested. Further, we show how large sample posterior odds and likelihood ratios can be used to appraise alternative hypotheses. In agreement with Pierce and Mason (1971), we believe that testing the implications of structural assumptions for transfer functions and, we add, final equations is an important element in the process of iterating in on a model that is reasonably in accord with the information in a sample of data.

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Publisher: Cambridge University Press
Print publication year: 2004

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References

Akaike, H., 1973, “Maximum likelihood identification of Gaussian autoregressive-moving average models,” Biometrika 60, 255–65CrossRefGoogle Scholar
Bartlett, M. S., 1946, “On the theoretical specification of the sampling properties of autocorrelated time series,” Journal of the Royal Statistical Society B 8, 27–41
Box, G. E. P. and G. M. Jenkins, 1970, Time Series Analysis, Forecasting and Control (San Francisco, Holden-Day)
Byron, R. P., 1973, “The computation of maximum likelihood estimates for linear simultaneous systems with moving average disturbances,” Department of Economics, Australian National University, manuscript
Chetty, V. K., 1966, “Bayesian analysis of some simultaneous equation models and specification errors,” Doctoral dissertation, University of Wisconsin, Madison, unpublished
Chetty, V. K. 1968, “Bayesian analysis of Haavelmo's models,” Econometrica 36, 582–602CrossRefGoogle Scholar
Dhrymes, P. J., 1970, Econometrics, Statistical Foundations and Applications (New York, Harper & Row)
Haavelmo, T., 1947, “Methods of measuring the marginal propensity to consume,” Journal of the American Statistical Society 42, 105–22; reprinted in W. Hood and TC. Koopmans (eds.), Studies in Econometric Methods (New York, John Wiley, 1953)
Hannan, E. J., 1969, “The identification of vector mixed auto-regressive-moving average systems,” Biometrika 57, 233–5Google Scholar
Hannan, E. J. 1971, “The identification problem for multiple equation systems with moving average errors,” Econometrica 39, 715–65CrossRefGoogle Scholar
Jeffreys, H., 1961, Theory of Probability (Oxford, Clarendon Press)
Jorgenson, D. W., 1966, “Rational distributed lag functions,” Econometrica 34, 135–49CrossRefGoogle Scholar
Kmenta, J., 1971, Elements of Econometrics (New York, Macmillan)
Lindley, D. V., 1961, “The use of prior probability distributions in statistical inference and decision,” in J. Neyman (ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, I, 453–68
Marschak, J., 1950, “Statistical inference in economics, an introduction,” in T. C. Koopmans (ed.), Statistical Inference in Dynamic Economic Models (New York, John Wiley)
Nelson, C. R., 1970, “Joint estimation of parameters of correlated time series,” Graduate School of Business, University of Chicago, manuscript
Palm, F. C., 1972, “On mixed prior distributions and their application in distributed lag models,” CORE Discussion Paper 7222, University of Louvain
Pierce, D. A. and J. M. Mason, 1971, “On estimating the fundamental dynamic equations of structural econometric models,” Paper presented at the Winter Meeting of the Econometric Society, New Orleans
Quenouille, M. H., 1957, The Analysis of Multiple Time series (London, C. Griffin and Co.)
Silvey, S. D., 1970, Statistical Inference (Baltimore, Penguin)
Theil, H. and J. C. D. Boot, 1962, “The final form of econometric equation systems,” Review of the International Statistical Institute 30, 136–52; reprinted in A. Zellner (ed.), Readings in Economic Statistics and Econometrics (Boston, Little Brown, 1968)
Tinbergen, J., 1940, “Econometric business cycle research,” Review of Economic Studies 7, 73–90CrossRefGoogle Scholar
Wold, H., 1953, Demand Analysis: A Study in Econometrics (New York, John Wiley)
Zellner, A., 1959, “Review of ‘The analysis of multiple time-series’ by M. H. Quenouille,” Journal of Farm Economics 41, 682–4
Zellner, A. 1971, An introduction to Bayesian Inference in Econometrics (New York, John Wiley)
Zellner, A. After completing this paper, the following Ph.D. Thesis, dealing with related topics, was brought to our attention by Dennis Aigner:
Haugh, L. D., 1972, “The identification of time series interrelationships with special reference to dynamic regression models,” Department of Statistics, University of Wisconsin, Madison

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