README.md

In bchivers-stanford/xtreg2way: Feasible Estimation of the Two-Way Fixed Effect Model

xtreg2way: An Algorithm to Estimate the Two-Way Fixed Effects Model

The xtreg2way package is an algorithm to efficiently estimate a two-way fixed effects model. This algorithm is adapted from the Matlab code written for this paper by Paulo Somaini and Frank A Wolak.

Abstract

We present an algorithm to estimate the two-way fixed effect linear model. The algorithm relies on the Frisch-Waugh-Lovell theorem and applies to ordinary least squares (OLS), two-stage least squares (TSLS) and generalized method of moments (GMM) estimators. The coefficients of interest are computed using the residuals from the projection of all variables on the two sets of fixed effects. Our algorithm has three desirable features. First, it manages memory and computational resources efficiently which speeds up the computation of the estimates. Second, it allows the researcher to estimate multiple specifications using the same set of fixed effects at a very low computational cost. Third, the asymptotic variance of the parameters of interest can be consistently estimated using standard routines on the residualized data.

How do I use it?

There are three ways you can interface with the algorithm.

In the most basic way, you can input all variables, including your y independent variable, matrix of X dependent variables, and iid, tid as your corresponding group identifiers:

output <- xtreg2way(y,X,iid,tid,w)

After running the algorithm once, the output will contain a list output$struc. The list struc contains matrices that do not depend on the columns passed. Thus, you can save time by running the algorithm with struc defined on a new set of columns.

output2 <- xtreg2way(y,newColumnsX, struc=struc)

Finally, instead of passing your variables explicitly as y and X, you can pass a formula and a dataframe.

output3 <- xtreg2way(y~x1+x2, myDataFrame, iid, tid)

Computing standard errors

You can control the standard error computation using the options se & cluster .

output3 <- xtreg2way(..., se ="0")

computes standard errors assuming homoscedasticity and no within group correlation or serial correlation. This option will break if there is a clustervar selected.

output3 <- xtreg2way(..., se ="1",cluster=clustervar)

produces the standard errors robust to heteroscedasticity and serial correlation proposed by Arellano (1987). This option will work with cluster specification, and will choose clustervar=iid if clustervar option is omitted.

output3 <- xtreg2way(..., se ="2")

generates errors robust to heteroscedasticity but assumes no correlation within group or serial correlation, i.e., the "robust" estimator in Stata. his option will break if there is a clustervar selected.

If se is omitted and clustervar is selected, then it is set to "1".

If se and cluster are omitted, then se is set to "1" and cluster is set to iid, and the Arellano (1987) estimator is computed.

Whats in this repo?

• R/
  • This folder contains all R code for the xtreg2way function, as well as helper functions
• man/
  • This folder contains function documentation.
  • This documentation is automatically built using roxygen2. To edit the documentation:
    • Edit the documentation in each function's R/... file
    • Run devtools::document() from an R console
    • Save and push this updated documentation
• tests/
  • This folder currently contains a single test, under tests/testthat/test_xtreg2way.R
• vignettes/
  • This folder contains a single vignette, which is an RMarkdown file containing example code.

Installation

Manual

Download this repository and extract it. From the directory where this README.MD file is located type devtools::install() in the R console. After the installation is complete, load the library typing library(xtreg2way) in R.

From CRAN

In the R console type install.packages('xtreg2way'). Then, load the library typing library(xtreg2way).

References

Arellano, M. (1987), Computing Robust Standard Errors for Within-Groups Estimators, Oxford Bulletin of Economics and Statistics, 49, issue 4, p. 431–434.

Cameron, A. C., & Miller, D. L. (2015). A practitioner’s guide to cluster-robust inference. Journal of human resources, 50(2), 317-372.

Somaini, P. and F.A. Wolak, (2016), An Algorithm to Estimate the Two-Way Fixed Effects Model, Journal of Econometric Methods, 5, issue 1, p. 143-152.

bchivers-stanford/xtreg2way documentation built on Jan. 5, 2023, 7:58 p.m.