@ARTICLE{17820886_1984,
author = {Doan, Thomas and Litterman, Robert B. and Sims, Christopher},
keywords = {bayesian method, vector autoregression},
title = {Forecasting and Conditional Projection Using Realistic Prior
Distributions },
journal = {Econometric Reviews},
year = {1984},
month = {},
volume = {3},
number = {1},
pages = {1-100},
url = {http://ecsocman.hse.ru/text/17820886/},
publisher = {},
language = {ru},
abstract = {This paper develops a forecasting procedure based on a Bayesian
method for estimating vector autoregressions. The procedure is
applied to ten macroeconomic variables and is shown to improve
out-of-sample forecasts relative to univariate equations. Authors
provided uniconditional forecasts as 1982:12 and 1983:3. They also
describes how such as this can be used to make conditional
projections and to analyze policy alternatives. As an example, they
analyzed a Congressional Budget Office forecast made in 1982:12.
While no automatic casual interpretations arise from models like
ours, they provide a detailed characterization of the dynamic
statistical interdependence of a set of economic variable, which may
help in evaluating casual hypoteses, without containing any such
hypotheses themselves. },
annote = {This paper develops a forecasting procedure based on a Bayesian
method for estimating vector autoregressions. The procedure is
applied to ten macroeconomic variables and is shown to improve
out-of-sample forecasts relative to univariate equations. Authors
provided uniconditional forecasts as 1982:12 and 1983:3. They also
describes how such as this can be used to make conditional
projections and to analyze policy alternatives. As an example, they
analyzed a Congressional Budget Office forecast made in 1982:12.
While no automatic casual interpretations arise from models like
ours, they provide a detailed characterization of the dynamic
statistical interdependence of a set of economic variable, which may
help in evaluating casual hypoteses, without containing any such
hypotheses themselves. }
}