Economic forecasts with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn

Bušs, Ginters (2009): Economic forecasts with Bayesian autoregressive distributed lag model: choosing optimal prior in economic downturn.

Abstract

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal Litterman prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag 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 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Forecasting, Bayesian inference, Bayesian autoregressive distributed lag model, optimal prior, Litterman prior, business cycle, mixed estimation
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; E - Macroeconomics and Monetary Economics > E1 - General Aggregative Models > E17 - Forecasting and Simulation: Models and Applications; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General

References

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