Template-Type: ReDIF-Paper 1.0
Title: Correcting Small Sample Bias in Linear Models With Many Covariates
Author-Name: Miren Azkarate-Askasua
Author-Email: azkarate-askasua@uni-mannheim.de
Author-Name: Miguel Zerecero
Author-Email: mzerecer@uci.edu
Classification-JEL: C13, C23, C55, J30, J31
Keywords:  Variance components, Many regressors, Fixed effects, Bias correction

Abstract: Estimations of quadratic forms in the parameters of linear models exhibit small-sample bias. The direct
computation for a bias correction is not feasible when the number of covariates is large. We propose a boot-strap 
method for correcting this bias that accommodates different assumptions on the structure of the error
term including general heteroscedasticity and serial correlation. Our approach is suited to correct variance
decompositions and the bias of multiple quadratic forms of the same linear model without increasing the
computational cost. We show with Monte Carlo simulations that our bootstrap procedure is effective in
correcting the bias and find that is faster than other methods in the literature. Using administrative data
for France, we apply our method by carrying out a variance decomposition of a linear model of log wages
with person and firm fixed effects. We find that the person and firm effects are less important in explaining
the variance of log wages after correcting for the bias and depending on the specification their correlation
becomes positive after the correction.

Note: 
Length: 35
Creation-Date: 2022-11
Revision-Date: 
File-URL: https://www.crctr224.de/research/discussion-papers/archive/dp376
File-Format: application/pdf
Handle: RePEc:bon:boncrc:CRCTR224_2022_376