Annals of the Institute of Statistical Mathematics

, Volume 52, Issue 1, pp 108–122

Time-Varying Parameters Prediction

Authors

  • Carlo Grillenzoni
    • IUAV: University Institute of Architecture of Venice
Article

DOI: 10.1023/A:1004189000171

Cite this article as:
Grillenzoni, C. Annals of the Institute of Statistical Mathematics (2000) 52: 108. doi:10.1023/A:1004189000171

Abstract

This paper develops a method of adaptive modeling that may be applied to forecast non-stationary time series. The starting point are time-varying coefficients models introduced in statistics, econometrics and engineering. The basic step of modeling is represented by the implementation of adaptive recursive estimators for tracking parameters. This is achieved by unifying basic algorithms—such as recursive least squares (RLS) and extended Kalman filter (EKF)—into a general scheme and next by selecting its coefficients with the minimization of the sum of squared prediction errors. This defines a non-linear estimation problem that may be analyzed in the context of the conditional least squares (CLS) theory. A numerical application on the IBM stock price series of Box-Jenkins illustrates the method and shows its good forecasting ability.

Conditional least squaresextended Kalman filterIBM stock price seriesrecursive least squarestime-varying parameter models

Copyright information

© The Institute of Statistical Mathematics 2000