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Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence

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  • Franta, Michal

Abstract

A small-scale vector autoregression (VAR) is used to shed some light on the roles of extreme shocks and non-linearities during stress events observed in the economy. The model focuses on the link between credit/financial markets and the real economy and is estimated on US quarterly data for the period 1984–2013. Extreme shocks are accounted for by assuming t-distributed reduced-form shocks. Non-linearity is allowed by the possibility of regime switch in the shock propagation mechanism. Strong evidence for fat tails in error distributions is found. Moreover, the results suggest that accounting for extreme shocks rather than explicit modeling of non-linearity contributes to the explanatory power of the model. Finally, it is shown that the accuracy of density forecasts improves if non-linearities and shock distributions with fat tails are considered.

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  • Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.
    • Handle: RePEc:eee:dyncon:v:75:y:2017:i:c:p:136-157
    DOI: 10.1016/j.jedc.2016.12.005
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    Cited by:

    1. Dave, Chetan & Malik, Samreen, 2017. "A tale of fat tails," European Economic Review, Elsevier, vol. 100(C), pages 293-317.
    2. Chiu, Ching-Wai (Jeremy) & Hacioglu Hoke, Sinem, 2016. "Macroeconomic tail events with non-linear Bayesian VARs," Bank of England working papers 611, Bank of England.
    3. Siddhartha Chib & Minchul Shin & Fei Tan, 2020. "High-Dimensional DSGE Models: Pointers on Prior, Estimation, Comparison, and Prediction∗," Working Papers 20-35, Federal Reserve Bank of Philadelphia.
    4. repec:cnb:ocpubv:rb16/1 is not listed on IDEAS
    5. repec:cnb:ocpubv:rb15/1 is not listed on IDEAS
    6. repec:cnb:ocpubv:rb15/2 is not listed on IDEAS
    7. Siddhartha Chib & Minchul Shin & Fei Tan, 2023. "DSGE-SVt: An Econometric Toolkit for High-Dimensional DSGE Models with SV and t Errors," Computational Economics, Springer;Society for Computational Economics, vol. 61(1), pages 69-111, January.
    8. repec:cnb:ocpubv:rb16/2 is not listed on IDEAS

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    More about this item

    Keywords

    C11; E44; C32; Non-linearity; Fat tails; Bayesian VAR; Density forecasting;
    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
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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