Penalized Poisson Pseudo Maximum Likelihood Regression
Computing A'A
Bootstrap Lasso Implementation (in development)
Cluster-robust Standard Error Estimation
Checking for Perfect Multicollinearity
Fixed Effects Computation
Faster Matrix Multiplication
Faster Least Squares Estimation
Finding Ridge Regression Solutions
Faster Ridge Regression
Faster Standard Deviation
Faster Weighted Mean
Generating a List of Fixed Effects
Generating Model Structure
PPML Estimation with HDFE
PPML Estimation with HDFE
Iceberg Lasso Implementation (in development)
Many Outer Products
General Penalized PPML Estimation
General Penalized PPML Estimation
One-Shot Penalized PPML Estimation with HDFE
Plugin Lasso Estimation
Plugin Lasso Estimation
One-Shot Penalized PPML Estimation with HDFE
penppml: Penalized Poisson Pseudo Maximum Likelihood Regression
Pipe operator
Iceberg Lasso Implementation (in development)
Filtering fixed effect lists
Weighted Standardization
XeeX Matrix Computation
Implementing Cross Validation
A set of tools that enables efficient estimation of penalized Poisson Pseudo Maximum Likelihood regressions, using lasso or ridge penalties, for models that feature one or more sets of high-dimensional fixed effects. The methodology is based on Breinlich, Corradi, Rocha, Ruta, Santos Silva, and Zylkin (2021) <http://hdl.handle.net/10986/35451> and takes advantage of the method of alternating projections of Gaure (2013) <doi:10.1016/j.csda.2013.03.024> for dealing with HDFE, as well as the coordinate descent algorithm of Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01> for fitting lasso regressions. The package is also able to carry out cross-validation and to implement the plugin lasso of Belloni, Chernozhukov, Hansen and Kozbur (2016) <doi:10.1080/07350015.2015.1102733>.