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Macroeconometric forecasting using a cluster of dynamic factor models

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  • Christian Glocker

    (Austrian Institute of Economic Research)

  • Serguei Kaniovski

    (Austrian Institute of Economic Research)

Abstract

We propose a modeling approach based on a set of small-scale factor models linked together in a cluster with linkages derived from Granger causality tests. GDP forecasts are produced using a disaggregated approach across production, expenditure and income accounts. The method combines the advantages of large structural macroeconomic models and small factor models, making our cluster of dynamic factor models (CDFM) useful for large-scale model-consistent forecasting. The CDFM has a simple structure, and its forecasts outperform those of a variety of competing models and professional forecasters. In addition, the CDFM allows forecasters to use their own judgment to produce conditional forecasts.

Suggested Citation

  • Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    • Handle: RePEc:spr:empeco:v:63:y:2022:i:1:d:10.1007_s00181-021-02129-w
    DOI: 10.1007/s00181-021-02129-w
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    More about this item

    Keywords

    Forecasting; Dynamic factor model; Granger causality; Structural modeling;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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