NATIONAL BUREAU OF ECONOMIC RESEARCH
NATIONAL BUREAU OF ECONOMIC RESEARCH

Conditional Projection by Means of Kalman Filtering

Richard H. Clarida, Diane Coyle

NBER Technical Working Paper No. 36
Issued in May 1984
NBER Program(s):   ME

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.

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