Estimating the effect of a variable in a high-dimensional linear model
Article first published online: 17 JUL 2012
© 2012 The Author(s). The Econometrics Journal © 2012 Royal Economic Society.
The Econometrics Journal
Volume 15, Issue 2, pages 325–357, June 2012
How to Cite
Jensen, P. S. and Würtz, A. H. (2012), Estimating the effect of a variable in a high-dimensional linear model. The Econometrics Journal, 15: 325–357. doi: 10.1111/j.1368-423X.2011.00362.x
- Issue published online: 17 JUL 2012
- Article first published online: 17 JUL 2012
- First version received: December 2009; final version accepted: October 2011
Vol. 15, Issue 3, 535, Article first published online: 28 NOV 2012
- Effect estimation;
- High-dimensional data;
- Model selection;
Summary A problem encountered in some empirical research, e.g. growth empirics, is that the potential number of explanatory variables is large compared to the number of observations. This makes it infeasible to condition on all variables in order to determine whether a variable of interest has an effect. We assume that the effect is identified in a high-dimensional linear model specified by unconditional moment restrictions. We propose a new method that provides a consistent estimator of the effect when the variable of interest is conditional mean independent of excluded variables. Existing methods are consistent when excluded variables do not explain the outcome, but not under the conditional mean independence assumption. We also demonstrate that the new method has good properties in a Monte Carlo study.