Skip to main content
SpringerLink
Log in
Book cover

Games, Economic Dynamics, and Time Series Analysis pp 306–321Cite as

The Estimation of Regression Models with Time-Varying Parameters

  • Conference paper

Abstract

In analysing time series data, the assumption that the coefficients in a regression model are constant over time may not always be reasonable. One way of handling this problem is to allow the parameters to vary over time according to a particular stochastic process. The parameters in models of this type are said to be dynamic, and they represent a generalization of models in which the parameters are random, in that they are independent of each other in different time periods; see, for example, Theil [1971, 622–627].

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Box, G.E.P., and G.M. Jenkins: Time Series Analyis: Forecasting and Control. San Francisco 1970.

    Google Scholar 

  • Brown, R.L., J. Durbin and J.M. Evans: Techniques for Testing the Constancy of Regression Relationships Over Time (with Discussion). Journal of the Royal Statistical Society, Series B, 37, 1975, 149–192.

    Google Scholar 

  • Cooley, T.F., and E.C. Prescott: Estimation in the Presence of Stochastic Parameter Variation. Econo-metrica 44, 1976, 167–184.

    Google Scholar 

  • Cooley, T.F., and K.D. Wall: Identification for Time-Varying Parameters. NBER Working Paper No. 127, 1976.

    Google Scholar 

  • Duncan, D.B., and S.D. Horn: Linear Dynamic Regression Estimation from the Viewpoint of Regression Analysis. Journal of the American Statistical Association 67, 1972, 815–821

    Google Scholar 

  • Gardner, G., A.C. Harvey, and G.D.A. Phillips: The Maximum Likelihood Estimation of Autoregressive-Moving Average Models by Kaiman Filtering, Applied Statistics 29, 1980, 311–322.

    Article  Google Scholar 

  • Gill, P.E., and W. Murray: Quasi-Newton Methods for Unconstrained Optimization. Journal of the Institute of Mathematics and Its Applications 9, 1972, 91–108.

    Article  Google Scholar 

  • Harvey, A.C.: The Estimation of Time-Varying Parameters from Panel Data. Annales de TINSEE, Special Issue on the Econometrics of Panel Data 30–31, 1978, 203–226.

    Google Scholar 

  • Harvey, A.C., and G.D.A. Phillips: Maximum Likelihood Estimation of Regression Models with Auto-regressive-Moving Average Disturbances. Biometrika 66, 1979, 49–58.

    Google Scholar 

  • Kakwani, M.C.: The Unbiasedness of Zellner’s Seemingly Unrelated Regression Equation Estimators. Journal of the American Statistical Association 62, 1967, 141–142.

    Article  Google Scholar 

  • Kaiman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions ASME Journal of Basic Engineering 82, 1960, 35–45.

    Article  Google Scholar 

  • Kaminski, P.G., A.E. Bryson and S.F. Schmidt: Discrete Square Root Filtering: a survey of current techniques. IEEE Transactions on Automatic Control AC-16, 1971, 727–737.

    Article  Google Scholar 

  • Pagan, A.R.: An Approach to Estimation and Inference for Varying Coefficient Regression Models. Unpublished paper, 1977.

    Google Scholar 

  • Phillips, G.D.A., and A.C. Harvey: A Simple Test for Serial Correlation in Regression Analysis. Journal of the American Statistical Association 69, 1974, 935–939.

    Article  Google Scholar 

  • Rosenberg, B.: Random Coefficient Models: The Analysis of a Cross-Section of Time Series by Stochastically Convergent Parameter Regression. Annals of Economic and Social Meausrement, 2, 1973, 399–428.

    Google Scholar 

  • Sarries, A.H.: A Bayesian Approach to Estimation of Time-Varying Regression Coefficients. Annals of Economic and Social Measurement 2, 1973, 501–523.

    Google Scholar 

  • Schaefer, S., et al.: Alternative Models of Systematic Risk, International Capital Markets: an Inter and Intra Country Analysis. Ed. by E. Elton and M. Gruber. Amsterdam 1975, 150-161.

    Google Scholar 

  • Schweppe, F.C.: Evaluation of Likelihood Functions for Gaussian Signals. IEEE Transactions on Information Theory, 11, 1965, 61–70.

    Article  Google Scholar 

  • Theil, H.: Principles of Econometrics. New York 1971.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The London School of Economics and Political Science, Houghton Street, Ald-wych, London, WC2A 2AE, Great Britain

    A. C. Harvey

Authors
  1. A. C. Harvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G. D. A. Phillips
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

M. Deistler E. Fürst G. Schwödiauer

Rights and permissions

Reprints and permissions

Copyright information

© 1982 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Harvey, A.C., Phillips, G.D.A. (1982). The Estimation of Regression Models with Time-Varying Parameters. In: Deistler, M., Fürst, E., Schwödiauer, G. (eds) Games, Economic Dynamics, and Time Series Analysis. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-41533-7_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-41533-7_18

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0271-9

  • Online ISBN: 978-3-662-41533-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation