華南經(jīng)濟(jì)論壇第269期：侯成瀚副教授講座
題目：Macroeconomic Forecasting with Large Bayesian VARs: Global-local Priors and the Illusion of Sparsity
主講人：侯成瀚 湖南大學(xué)經(jīng)濟(jì)管理研究中心副教授
時(shí)間：2019年10月28日（周一）下午3:00
地點(diǎn)：學(xué)院301會(huì)議室
摘要：A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector auto-regressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe and Normal-Gamma, can systematically improve upon the forecast accuracy of two commonly used benchmarks: hierarchical Minnesota prior and stochastic search variable selection prior (SSVS), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
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