Bayesian Estimation of Fixed Effects Models with Large Datasets*
提出一种吉布斯抽样方法，在不创建虚拟变量的情况下估计固定效应模型，避免了大数据集下的内存负担，并简化了有限因变量模型的推断与估计，适用于银行、区域和贷款用途的三维固定效应分类问题。
Abstract In hierarchical prior longitudinal models, random effects are estimated by the Gibbs sampler. We show that fixed effects can be handled by a similar Gibbs sampler under a diffuse prior on the unobserved heterogeneity. The dummy variable approach for fixed effects is computationally intensive and has the out‐of‐memory risk, while the Gibbs sampler can reproduce the dummy variable estimator without creating dummy variables, and therefore avoids the memory burden. Compared to alternating projections and other classical approaches, our method simplifies both inference and estimation of the limited dependent variable models with fixed effects. The proposed method is applied to a real‐world mortgage dataset for classification with three‐way fixed effects on banks, regions, and loan purposes.