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Real-time forecasting with a mixed-frequency VAR

Frank Schorfheide and Dongho Song

No 701, Working Papers from Federal Reserve Bank of Minneapolis

Abstract: This paper develops a vector autoregression (VAR) for macroeconomic time series which are observed at mixed frequencies ? quarterly and monthly. The mixed-frequency VAR is cast in state-space form and estimated with Bayesian methods under a Minnesota-style prior. Using a real-time data set, we generate and evaluate forecasts from the mixed-frequency VAR and compare them to forecasts from a VAR that is estimated based on data time-aggregated to quarterly frequency. We document how information that becomes available within the quarter improves the forecasts in real time.

Keywords: Bayesian statistical decision theory; Forecasting; Vector autoregression (search for similar items in EconPapers)
Date: 2012
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-mst
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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http://www.minneapolisfed.org/publications_papers/pub_display.cfm?id=4942 (application/pdf)
http://www.minneapolisfed.org/research/wp/wp701.pdf

Related works:
Journal Article: Real-Time Forecasting With a Mixed-Frequency VAR (2015) Downloads
Working Paper: Real-Time Forecasting with a Mixed-Frequency VAR (2013) Downloads
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