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Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy

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  • Ellis W. Tallman
  • Saeed Zaman

Abstract

This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of variables, including those that are not directly tilted but are affected through spillover effects from tilted variables. The forecast accuracy gains for inflation are substantial, statistically significant, and are competitive with the forecast accuracy from both time-varying VARs and univariate benchmarks. We view our proposal as an indirect approach to accommodating structural change and moving end points.

Suggested Citation

  • Ellis W. Tallman & Saeed Zaman, 2018. "Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy," Working Papers (Old Series) 1809, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwp:1809
    DOI: 10.26509/frbc-wp-201809
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    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Edward S. Knotek & Saeed Zaman, 2020. "Real-Time Density Nowcasts of US Inflation: A Model-Combination Approach," Working Papers 202031, Federal Reserve Bank of Cleveland.
    3. Gergely Ganics & Florens Odendahl, 2019. "Bayesian VAR forecasts, survey information and structural change in the euro area," Working Papers 1948, Banco de España;Working Papers Homepage.
    4. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    5. Gelain, Paolo & Manganelli, Simone, 2020. "Monetary policy with judgment," Working Paper Series 2404, European Central Bank.

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    More about this item

    Keywords

    Bayesian analysis; relative entropy; survey forecasts; nowcasts; density forecasts; real-time data;
    All these keywords.

    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
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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