This chapter reviews the usefulness of the Kalman filter for parameter estimation and inference about unobserved variables in linear dynamic systems. Applications include exact maximum likelihood estimation of regressions with ARMA disturbances, time-varying parameters, missing observations, forming an inference about the public's expectations about inflation, and specification of business cycle dynamics. The chapter also reviews models of changes in regime and develops the parallel between such models and linear state-space models. The chapter concludes with a brief discussion of alternative approaches to nonlinear filtering.
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ReDIF This chapter was published in: R. F. Engle & D. McFadden (ed.) Handbook of Econometrics, , chapter 50, pages 3039-3080, 1986.
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Related research
This chapter was published in the following book, which is listed on IDEAS: R. F. Engle & D. McFadden (ed.), 1986.
"Handbook of Econometrics,"
Handbook of Econometrics,
Elsevier,
edition 1, volume 4, number 4.
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Find related papers by JEL classification: C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other