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The Estimation of Regression Models with Time-Varying Parameters

  • A. C. Harvey
  • G. D. A. Phillips

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].

Keywords

Mean Square Error Ordinary Little Square Generalize Little Square Ordinary Little Square Estimator Prediction Error Variance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1982

Authors and Affiliations

  • A. C. Harvey
    • 1
  • G. D. A. Phillips
  1. 1.The London School of Economics and Political ScienceAld-wych, LondonGreat Britain

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