Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Clements, M. P. ORCID: https://orcid.org/0000-0001-6329-1341 and Galvão, A. B. (2023) Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty. Journal of Applied Econometrics, 38 (2). pp. 164-185. ISSN 1099-1255 doi: 10.1002/jae.2944

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/106195
Identification Number/DOI	10.1002/jae.2944
Refereed	Yes
Divisions	Henley Business School > Finance and Accounting
Publisher	Wiley
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