Forecasting and conditional projection using realistic prior distributions Público Deposited

Creator Series Issue number
  • 243
Date Created
  • 1983-08
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. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates. We provide unconditional forecasts as of 1982:12 and 1963:3* We also describe how a model such as this can be used to make conditional projections and to analyse policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12. While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help in evaluating causal hypotheses, without containing any such hypotheses themselves.

Subject (JEL) Palabra Clave Date Modified
  • 09/16/2019
Publisher
  • Federal Reserve Bank of Minneapolis. Research Division.
Resource type
License

Relaciones

En Collection:
Última modificación

Contenido Descargable

Descargar PDF

Zipped Files

Download a zip file that contains all the files in this work.

Elementos