In this article, we describe an iterative approach for the estimation of linear
regression models with high-dimensional fixed effects. This approach is
computationally intensive but imposes minimum memory requirements. We also show
that the approach can be extended to nonlinear models and to more than two
high-dimensional fixed effects.
AbowdJ. M., CreecyR. H., and
KramarzF.2002. Computing person and firm effects using linked
longitudinal employer–employee data. Technical Paper No.
TP-2002-06, Center for Economic Studies, U.S. Census
Bureau.http://lehd.did.census.gov/led/library/techpapers/tp-2002-06.pdf.
2.
AbowdJ. M., KramarzF., and MargolisD. N.1999. High wage workers and high wage
firms. Econometrica67: 251–333.
3.
AndrewsM., SchankT., and UpwardR.2006. Practical fixed-effects estimation methods for
the three-way error-components model. Stata
Journal6: 461–481.
4.
CarneiroA., GuimarãesP., and PortugalP.2010. Real wages and the business cycle: Accounting for
worker, firm, and job heterogeneity. Unpublished
manuscript.
5.
ChamberlainG.1980. Analysis of covariance with qualitative
data. Review of Economic Studies47: 225–238.
6.
CornelissenT.2008. The Stata command felsdvreg to fit a linear
model with two high-dimensional fixed effects. Stata
Journal8: 170–189.
7.
GreeneW.2004. The behaviour of the maximum likelihood
estimator of limited dependent variable models in the presence of fixed
effects. Econometrics Journal7: 98–119.
8.
GuimarãesP.2004. Understanding the multinomial-Poisson
transformation. Stata Journal4: 265–273.
9.
GuimarãesP.2008. The fixed effects negative binomial model
revisited. Economics Letters99: 63–66.
10.
HsiaoC.2003. Analysis of Panel Data. 2nd ed.
Cambridge: Cambridge
University Press.
11.
LancasterT.2000. The incidental parameter problem since
1948. Journal of Econometrics95: 391–413.
12.
NeymanJ., and ScottE.1948. Consistent estimation from partially
consistent observations. Econometrica16: 1–32.
13.
SmythG. K.1996. Partitioned algorithms for maximum likelihood
and other nonlinear estimation. Statistics and
Computing6: 201–216.