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Last Night a Shrinkage Saved My Life: Economic Growth, Model Uncertainty and Correlated Regressors

机译:昨晚收缩挽救了我的命:经济增长,模型不确定性和相关的回归变量

摘要

We compare the predictive ability of Bayesian methods which deal simultaneously with model uncertainty and correlated regressors in the framework of cross-country growth regressions. In particular, we assess methods with spike and slab priors combined with different prior specifications for the slope parameters in the slab. Our results indicate that moving away from Gaussian g-priors towards Bayesian ridge, LASSO or elastic net specifications has clear advantages for prediction when dealing with datasets of (potentially highly) correlated regressors, a pervasive characteristic of the data used hitherto in the econometric literature.
机译:我们比较了在跨国增长回归框架下同时处理模型不确定性和相关回归的贝叶斯方法的预测能力。特别是,我们评估了先验先验和先验先验的方法,并结合了先验指标对先验参数的要求。我们的结果表明,在处理(可能是高度相关的)相关回归数据集时,从高斯g优先级向贝叶斯岭,LASSO或弹性网指标移动具有明显的预测优势,这是计量经济学文献中迄今使用的数据的普遍特征。

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