arXiv 26 May 2023 · Econometrics
arXiv:2305.16827 · PDF · DOI · OpenAlex · Extracted main text
The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM since this would lead to computationally infeasible Bayesian inference in larger VARs and potentially a sensitivity to the way the variables are ordered in the VAR. Instead we develop a particular additive error structure inspired by Bayesian nonparametric treatments of random effects in panel data models. We show that this leads to a model which allows for computationally fast and order-invariant inference in large VARs with nonparametric shocks. Our empirical results with nonparametric VARs of various dimensions shows that nonparametric treatment of the VAR errors is particularly useful in periods such as the financial crisis and the pandemic.
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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 | Carriero et al (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 1.000 | 6 | 3 | 100% |
| 2 | Chan et al (2021) Large Order-Invariant Bayesian VARs with Stochastic Volatility | 0.737 | 3 | 2 | 100% |
| 3 | Arias et al Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models | 0.644 | 2 | 2 | 100% |
| 4 | Dunson and Xing (2009) Nonparametric Bayes Modeling of Multivariate Categorical Data | 0.644 | 2 | 2 | 100% |
| 5 | Frühwirth-Schnatter et al (2004) Bayesian Analysis of the Heterogeneity Model | 0.644 | 2 | 2 | 100% |
| 6 | Carriero et al (2022) Addressing COVID-19 outliers in BVARs with stochastic volatility | 0.644 | 2 | 2 | 100% |
| 7 | Huber and Feldkircher (2019) Adaptive shrinkage in Bayesian vector autoregressive models self | 0.644 | 2 | 2 | 100% |
| 8 | McCracken and Ng (2020) FRED-QD: A quarterly database for macroeconomic research | 0.511 | 2 | 2 | 50% |
| 9 | Brown and Griffin (2010) Inference with normal-gamma prior distributions in regression problems | 0.511 | 2 | 1 | 100% |
| 10 | Bhattacharya et al (2016) Fast sampling with Gaussian scale mixture priors in high-dimensional regression | 0.511 | 2 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.