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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, Litterman prior, business cycle
Anotācija Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. By using an autoregressive distributed lag 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.
Hipersaite: http://www.journal.aplimat.com/volume_3_2010/Journal_volume_3/Number_3.pdf 
Atsauce Bušs, G. Economic Forecasts with Bayesian Autoregressive Distributed Lag Model: Choosing Optimal Prior in Economic Downturn. Aplimat: Journal of Applied Mathematics, 2010, Vol.3, 191.-200.lpp. ISSN 1337-6365.
ID 10630