Fast Poisson estimation with high-dimensional fixed effects
Fast Poisson estimation with high-dimensional fixed effects
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DOI：
10.1177/1536867x20909691
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发表时间：
2020-03-01
期刊：
影响因子：
4.8
通讯作者：
Zylkin, Tom
中科院分区：
文献类型：
--
作者：
Correia, Sergio;Guimaraes, Paulo;Zylkin, Tom
在本文中，我们介绍了 ppmlhdfe，这是一种用于估计具有多个高维固定效应 (HDFE) 的（伪）泊松回归模型的新命令。估计是使用迭代重新加权最小二乘算法的修改版本来实现的，该算法允许在存在 HDFE 的情况下进行快速估计。由于代码是围绕 reghdfe 包构建的（Correia，2014，统计软件组件 S457874，波士顿学院经济系），因此它具有相似的语法，支持许多相同的功能，并受益于 reghdfe 用于计算高维最小二乘问题的快速收敛特性。我们引入的一些新技术进一步增强了性能，这些新技术专门用于加速 HDFE 迭代重新加权最小二乘估计。 ppmlhdfe 还实现了一种新颖且更稳健的方法来检查（伪）最大似然估计的存在。
In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package (Correia, 2014, Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe's fast convergence properties for computing high-dimensional leastsquares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE iteratively reweighted least-squares estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo)maximum likelihood estimates.