Florian Huber, Massimiliano Marcellino, Tobias Scheckel
arXiv 16 Apr 2023 · Econometrics
arXiv:2304.07856 · PDF · DOI · OpenAlex · Extracted main text
Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter Model misspecification in multivariate econometric models can strongly influence estimates of quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution. We propose a simple method for addressing these specification issues in the context of Bayesian VARs. Our method, called coarsened Bayesian VARs (cBVARs), replaces the exact likelihood with a coarsened likelihood that takes into account that the model might be misspecified along important but unknown dimensions. Since endogenous variables in a VAR can feature different degrees of misspecification, our model allows for this and automatically detects the degree of misspecification. The resulting cBVARs perform well in simulations for several types of misspecification. Applied to US data, cBVARs improve point and density forecasts compared to standard BVARs.
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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 | Grünwald and van Ommen (2017) Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It | 1.000 | 8 | 4 | 100% |
| 2 | Miller and Dunson (2018) Robust bayesian inference via coarsening | 0.874 | 8 | 2 | 100% |
| 3 | Chan (2022) Asymmetric conjugate priors for large bayesian vars | 0.874 | 7 | 2 | 100% |
| 4 | González-Casasús and Schorfheide (2025) Misspecification-robust shrinkage and selection for var forecasts and irfs | 0.843 | 4 | 4 | 75% |
| 5 | McCracken and Ng (2016) Fred-md: A monthly database for macroeconomic research | 0.737 | 3 | 2 | 100% |
| 6 | Bhattacharya, Pati, and Yang (2019) Bayesian fractional posteriors | 0.644 | 2 | 2 | 100% |
| 7 | Holmes and Walker (2017) Assigning a value to a power likelihood in a general bayesian model | 0.644 | 2 | 2 | 100% |
| 8 | Koop (2013) Forecasting with medium and large bayesian vars | 0.644 | 2 | 2 | 100% |
| 9 | Schorfheide (2005) Var forecasting under misspecification | 0.644 | 2 | 2 | 100% |
| 10 | Karlsson, Mazur, and Nguyen (2023) Vector autoregression models with skewness and heavy tails | 0.511 | 2 | 2 | 50% |
Showing the top 10 of 48 scored citations.