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Forecasting economy with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn

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  • Bušs, Ginters

Abstract

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to variables containing more recent information. Second, greater uncertainty brought by a rapid economic downturn requires more space for coefficient variation, which is set by the overall tightness parameter. It is shown that the optimal overall tightness parameter may increase to such an extent that Bayesian ADL becomes equivalent to frequentist ADL.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 17273.

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Date of creation: 13 Sep 2009
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Handle: RePEc:pra:mprapa:17273

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Related research

Keywords: Forecasting; Bayesian inference; Bayesian autoregressive distributed lag model; optimal prior; Litterman prior; business cycle; mixed estimation; grid search;

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  1. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
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