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Real-time forecasting with a mixed-frequency VAR

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  • Frank Schorfheide
  • Dongho Song

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.

Suggested Citation

  • Frank Schorfheide & Dongho Song, 2012. "Real-time forecasting with a mixed-frequency VAR," Working Papers 701, Federal Reserve Bank of Minneapolis, revised 2012.
    • Handle: RePEc:fip:fedmwp:701
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    References listed on IDEAS

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    More about this item

    Keywords

    Vector autoregression; Bayesian statistical decision theory; Forecasting;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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