Sune Karlsson, Stepan Mazur, Hoang Nguyen
arXiv 24 May 2021 · Econometrics · publishedJournal of Economic Dynamics and Control (2022) · 24 citations (OpenAlex)
arXiv:2105.11182 · PDF · DOI · OpenAlex · Extracted main text
With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autoregression (VAR) model to account for a more realistic assumption of the multivariate distribution of the macroeconomic variables. We propose a general class of generalized hyperbolic skew Student's t distribution with stochastic volatility for the error term in the VAR model that allows us to take into account skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroeconomic variables. The analysis also gives a clear message that skewness should be taken into account for better predictions during recessions and crises.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chiu, C.-W. J., Mumtaz, H., and Pinter, G (2017) Forecasting with VAR models: Fat tails and stochastic volatility | 1.000 | 8 | 4 | 100% |
| 2 | Clark, T. E. and Ravazzolo, F (2015) Macroeconomic forecasting performance under alternative specifications of time-varying volatility | 1.000 | 6 | 4 | 100% |
| 3 | Cogley, T. and Sargent, T. J (2005) Drifts and volatilities: Monetary policies and outcomes in the post WWII US | 0.928 | 4 | 3 | 100% |
| 4 | Cúrdia, V., Del Negro, M., and Greenwald, D. L (2014) Rare shocks, great recessions | 0.928 | 4 | 3 | 100% |
| 5 | Chan, J. C. and Eisenstat, E (2018) Bayesian model comparison for time-varying parameter VARs with stochastic volatility | 0.909 | 8 | 5 | 75% |
| 6 | Clark, T. E (2011) Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility | 0.874 | 6 | 2 | 100% |
| 7 | Primiceri, G. E (2005) Time varying structural vector autoregressions and monetary policy | 0.843 | 3 | 3 | 100% |
| 8 | Liu, X (2019) On tail fatness of macroeconomic dynamics | 0.811 | 4 | 2 | 100% |
| 9 | Diebold, F. X. and Mariano, R. S (1995) Comparing predictive accuracy | 0.644 | 4 | 1 | 100% |
| 10 | Geweke, J. and Amisano, G (2010) Comparing and evaluating Bayesian predictive distributions of asset returns | 0.511 | 2 | 1 | 100% |
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