Title

Conditional Projection by Means of Kalman Filtering

Document Type

Discussion Paper

Publication Date

4-1-1984

CFDP Number

702

CFDP Pages

12

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.

This document is currently not available here.

Share

COinS