TY  - JOUR
AU  - Clarida,Richard H.
AU  - Coyle,Diane
TI  - Conditional Projection by Means of Kalman Filtering
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 36
PY  - 1984
Y2  - May 1984
UR  - http://www.nber.org/papers/t0036
L1  - http://www.nber.org/papers/t0036.pdf
N1  - Author contact info:
Richard H. Clarida
Columbia University
420 West 118th Street
Room 1111, IAB
New York, NY  10027
Tel: 212/854-3676
Fax: 212/854-8059
E-Mail: rhc2@columbia.edu
AB  - 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.
ER  -