arXiv 28 Sep 2021 · Econometrics · publishedEconometrics and Statistics (2025)
arXiv:2109.13648 · PDF · DOI · OpenAlex · Extracted main text
A new mixture vector autoregressive model based on Gaussian and Student's $t$ distributions is introduced. As its mixture components, our model incorporates conditionally homoskedastic linear Gaussian vector autoregressions and conditionally heteroskedastic linear Student's $t$ vector autoregressions. For a $p$th order model, the mixing weights depend on the full distribution of the preceding $p$ observations, which leads to attractive practical and theoretical properties such as ergodicity and full knowledge of the stationary distribution of $p+1$ consecutive observations. A structural version of the model with statistically identified shocks is also proposed. The empirical application studies the effects of the Euro area monetary policy shock. We fit a two-regime model to the data and find the effects, particularly on inflation, stronger in the regime that mainly prevails before the Financial crisis than in the regime that mainly dominates after it. The introduced methods are implemented in the accompanying R package gmvarkit.
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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 | Virolainen S. (forthcoming) A statistically identified structural vector autoregression with endogeneously switching volatility regime | 1.000 | 19 | 6 | 100% |
| 2 | Virolainen S (2022) A mixture autoregressive model based on Gaussian and Student's $t$-distributions | 0.950 | 7 | 6 | 86% |
| 3 | Virolainen S (2024) gmvarkit: Estimate Gaussian and Student's $t$ Mixture Vector Autoregressive Models | 0.941 | 6 | 6 | 83% |
| 4 | Kalliovirta L., Meitz M., Saikkonen P (2016) Gaussian mixture vector autoregression | 0.874 | 9 | 4 | 67% |
| 5 | Koop G., Pesaran M., Potter S (1996) Impulse response analysis in nonlinear multivariate models | 0.843 | 4 | 3 | 75% |
| 6 | Wu J., Xia F (2016) Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound | 0.811 | 5 | 2 | 80% |
| 7 | Burgard J., Neuenkirch M., Nöckel M (2019) State-Dependent Transmission of Monetary Policy in the Euro Area | 0.811 | 4 | 2 | 100% |
| 8 | Meitz M., Preve D., Saikkonen P (2023) A mixture autoregressive model based on Student's $t$-distribution | 0.763 | 9 | 7 | 44% |
| 9 | Kalliovirta L., Meitz M., Saikkonen P (2015) A Gaussian Mixture Autoregressive Model for Univariate Time Series | 0.737 | 3 | 3 | 67% |
| 10 | Lütkepohl H (2005) New Introduction to Multiple Time Series Analysis | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 44 scored citations.