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

Jan Szydlo
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Jan Szydlo: University of Warwick

Warwick-Monash Economics Student Papers from Warwick Monash Economics Student Papers

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.

Keywords: Macroeconometric Forecasting; Big data; Credit JEL classifications: C53; C55; E47; E51 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-mon
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:wrkesp:63

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