Selecting Instrumental Variables in a Data Rich EnvironmentSerena Ng () and Jushan Bai Journal of Time Series Econometrics, 2009, vol. 1, issue 1, 34 Abstract: Practitioners often have at their disposal a large number of instruments that are weakly exogenous for the parameter of interest. However, not every instrument has the same predictive power for the endogenous variable, and using too many instruments can induce bias. We consider two ways of handling these problems. The first is to form principal components from the observed instruments, and the second is to reduce the number of instruments by subset variable selection. For the latter, we consider boosting, a method that does not require an a priori ordering of the instruments. We also suggest a way to pre-order the instruments and then screen the instruments using the goodness of fit of the first stage regression and information criteria. We find that the principal components are often better instruments than the observed data except when the number of relevant instruments is small. While no single method dominates, a hard-thresholding method based on the t test generally yields estimates with small biases and small root-mean-squared errors. Keywords: relevant instruments; principal components; information criteria; hard-thresholding; boosting; factor model; panel model (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bpj:jtsmet:v:1:y:2009:i:1:n:4 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of Time Series Econometrics is currently edited by Javier Hidalgo More articles in Journal of Time Series Econometrics from De Gruyter |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.