Conditional Projection by Means of Kalman Filtering

13 Pages Posted: 28 Dec 2006 Last revised: 9 Aug 2010

See all articles by Richard Clarida

Richard Clarida

Columbia University - Graduate School of Arts and Sciences - Department of Eco; National Bureau of Economic Research (NBER)

Diane Coyle

Bennett Institute for Public Policy

Date Written: May 1984

Abstract

We establish that the recursive, state-space methods of Kalman filtering and smoothing can be used to implement the Doan, Litterman, and Sims (1983) approach to econometric forecast and policy evaluation. Compared with the methods outlined in Doan, Litterman, and Sims, the Kalman algorithms are more easily programmed and modified to incorporate different linear constraints, avoid cumbersome matrix inversions, and provide estimates of the full variance covariance matrix of the constrained projection errors which can be used directly, under standard normality assumptions, to test statistically the likelihood and internal consistency of the forecast under study.

Suggested Citation

Clarida, Richard H. and Coyle, Diane, Conditional Projection by Means of Kalman Filtering (May 1984). NBER Working Paper No. t0036. Available at SSRN: https://ssrn.com/abstract=579736

Richard H. Clarida (Contact Author)

Columbia University - Graduate School of Arts and Sciences - Department of Eco ( email )

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National Bureau of Economic Research (NBER)

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Diane Coyle

Bennett Institute for Public Policy ( email )

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Great Britain

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