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Publikācija: Economic Forecasts with Bayesian Autoregressive Distributed Lag Model: Choosing Optimal Prior in Economic Downturn

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Nosaukums oriģinālvalodā Economic Forecasts with Bayesian Autoregressive Distributed Lag Model: Choosing Optimal Prior in Economic Downturn
Pētniecības nozare 1. Dabaszinātnes
Pētniecības apakšnozare 1.1. Matemātika
Autori Ginters Bušs
Atslēgas vārdi Forecasting, Bayesian inference, Bayesian autoregressive distributed lag model, optimal prior
Anotācija Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. By using an autoregressive distributed lag (ADL) model, this paper analyzes how optimal Litterman prior changes when an economy is hit by a recession. 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 by 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.
Atsauce Bušs, G. Economic Forecasts with Bayesian Autoregressive Distributed Lag Model: Choosing Optimal Prior in Economic Downturn. No: 6th Colloquium on Modern Tools for Business Cycle Analysis: "The Lessons from Global Economic Crisis": Book of Abstracts, Luksemburga, Luxembourg, 26.-29. septembris, 2010. Luxembourg: Eurostat, 2010, 53.-53.lpp.
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