UBC Theses and Dissertations
Instrumental variables selection : a comparison between regularization and post-regularization methods Di Gravio, Chiara
Instrumental variables are commonly used in statistics, econometrics, and epidemiology to obtain consistent parameter estimates in regression models when some of the predictors are correlated with the error term. However, the properties of these estimators are sensitive to the choice of valid instruments. Since in many applications, valid instruments come in a bigger set that includes also weak and possibly irrelevant instruments, the researcher needs to select a smaller subset of variables that are relevant and strongly correlated with the predictors in the model. This thesis reviews part of the instrumental variables literature, examines the problems caused by having many potential instruments, and uses different variables selection methods in order to identify the relevant instruments. Specifically, the performance of different techniques is compared by looking at the number of relevant variables correctly detected, and at the root mean square error of the regression coefficients’ estimate. Simulation studies are conducted to evaluate the performance of the described methods.
Item Citations and Data
Attribution-NoDerivs 2.5 Canada