Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Michael Clements and Ana Beatriz Galvão

Journal of Applied Econometrics, 2023, vol. 38, issue 2, 164-185

Abstract: Macroeconomic data are subject to data revisions. Yet, the usual way of generating real‐time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real‐time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:38:y:2023:i:2:p:164-185

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