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Forecasting Credit Dynamics : VAR, VECM or modern Factor-Augmented VAR approach?

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Listed:
  • Szydlo, Jan

    (University of Warwick)

Abstract

Following the financial crisis of 2008, central banks started paying more attention to the issue of financial stability and to the amount of credit circulating in the economy. However, the methods used to forecast credit often are underdeveloped and don’t make the most out of access to big data. This paper evaluates the performance of various models in forecasting the Dynamics of Credit to the Non-Financial Sector in the United States. It explores three approaches: the reduced form Vector Autoregressive model, Vector Error Correction model and Factor-Augmented Autoregressive model. The paper compares the RMSE of the models and finds that FAVAR approach outperforms traditional VAR and VEC models and produces more accurate forecasts of credit dynamics.

Suggested Citation

  • Szydlo, Jan, 2023. "Forecasting Credit Dynamics : VAR, VECM or modern Factor-Augmented VAR approach?," Warwick-Monash Economics Student Papers 63, Warwick Monash Economics Student Papers.
    • Handle: RePEc:wrk:wrkesp:63
    as

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    File URL: https://warwick.ac.uk/fac/soc/economics/research/wmesp/manage/63_-_jan_szydlo.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Macroeconometric Forecasting ; Big data ; Credit JEL classifications: C53 ; C55 ; E47 ; E51;
    All these keywords.

    JEL classification:

    • 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
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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