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Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs

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Abstract

This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR.

Suggested Citation

  • Florian, Huber & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021. "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," Working Papers 2021-01, Joint Research Centre, European Commission.
  • Handle: RePEc:jrs:wpaper:202101
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    Cited by:

    1. Eller, Markus & Hauzenberger, Niko & Huber, Florian & Schuberth, Helene & Vashold, Lukas, 2021. "The impact of macroprudential policies on capital flows in CESEE," Journal of International Money and Finance, Elsevier, vol. 119(C).
    2. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    3. Serena Ng, 2021. "Modeling Macroeconomic Variations After COVID-19," Papers 2103.02732, arXiv.org, revised Jul 2021.
    4. Martin Guth, 2022. "Predicting Default Probabilities for Stress Tests: A Comparison of Models," Papers 2202.03110, arXiv.org.
    5. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    6. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
    7. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.
    8. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    9. Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.

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    More about this item

    Keywords

    Regression tree models; Bayesian; macroeconomic forecasting; vector autoregressions;
    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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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