In: Handbook of Econometrics
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.
|This chapter was published in: ||This item is provided by Elsevier in its series Handbook of Econometrics with number
4-50.||Handle:|| RePEc:eee:ecochp:4-50||Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/bookseriesdescription.cws_home/BS_HE/description|
When requesting a correction, please mention this item's handle: RePEc:eee:ecochp:4-50. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If references are entirely missing, you can add them using this form.