论文标题
稀疏性幻觉的幻想：先前灵敏度的练习
The Illusion of the Illusion of Sparsity: An exercise in prior sensitivity
论文作者
论文摘要
大数据的出现提出了一个问题，即当有大量可能的解释变量时，如何建模经济关系。我们通过比较在贝叶斯方法中使用密集或稀疏模型的可能性来重新审视问题，从而可以进行可变选择和收缩。更具体地说，我们讨论了Giannone，Lenza和Primiceri（2020）通过“尖峰和slab”之前的结果，这在经济数据中暗示了“稀疏性”，因为无法检测到明确的稀疏模式。我们进一步修订了模型的后验分布，并提出了三个实验，以评估所采用的先前分布的鲁棒性。我们发现，稀疏性模式对回归系数的先前分布敏感，并提供了模型间接诱导可变选择和收缩的证据，这表明“稀疏性幻觉”本身就是一种幻觉。代码可在github.com/bfava/illusionfillusion上找到。
The emergence of Big Data raises the question of how to model economic relations when there is a large number of possible explanatory variables. We revisit the issue by comparing the possibility of using dense or sparse models in a Bayesian approach, allowing for variable selection and shrinkage. More specifically, we discuss the results reached by Giannone, Lenza, and Primiceri (2020) through a "Spike-and-Slab" prior, which suggest an "illusion of sparsity" in economic data, as no clear patterns of sparsity could be detected. We make a further revision of the posterior distributions of the model, and propose three experiments to evaluate the robustness of the adopted prior distribution. We find that the pattern of sparsity is sensitive to the prior distribution of the regression coefficients, and present evidence that the model indirectly induces variable selection and shrinkage, which suggests that the "illusion of sparsity" could be, itself, an illusion. Code is available on github.com/bfava/IllusionOfIllusion.
