The Estimation of Regression Models with Time-Varying Parameters

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


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].


Mean Square Error Ordinary Little Square Generalize Little Square Ordinary Little Square Estimator Prediction Error Variance 
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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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