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Bayesian averaging of classical estimates in asymmetric vector autoregressive models

机译:非对称向量自回归模型中经典估计的贝叶斯平均

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The estimated vector autoregressive (VAR) model is sensitive to model misspecifications, resulting to biased and inconsistent parameter estimates. This article extends the Bayesian averaging of classical estimates, a robustness procedure in cross-section data, to a vector time-series that is estimated using a large number of asymmetric VAR models. The proposed procedure was applied to simulated data from various forms of model misspecifications. The results of the simulation suggest that, under misspecification problems, particularly if an important variable andmoving average (MA) terms were omitted, the proposed procedure gives robust results and better forecasts than the automatically selected equal lag-length VAR model.
机译:估计的矢量自回归(VAR)模型对模型规范错误很敏感,从而导致参数估计有偏差和不一致。本文将经典估计的贝叶斯平均(横截面数据中的鲁棒性过程)扩展到使用大量非对称VAR模型估计的矢量时间序列。拟议的程序应用于各种形式的模型规格不正确的模拟数据。仿真结果表明,在错误指定的问题下,特别是如果省略了重要的变量和移动平均(MA)项,则与自动选择的等长VAR模型相比,所提出的过程可提供可靠的结果和更好的预测。

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