Eseje o modelové nejistotě a modelovém průměrování

Skolkova, Alena

In the first chapter of this dissertation I study the properties of a model averaging estimator with ridge regularization. I propose the ridge-regularized modifications of Mallows model averaging (Hansen, 2007, Econometrica}, 75) and heteroskedasticity-robust Mallows model averaging (Liu and Okui, 2013, The Econometrics Journal, 16) to leverage the capabilities of averaging and ridge regularization simultaneously. Via a simulation study, I examine the finite-sample improvements obtained by replacing least-squares with a ridge regression. Ridge-based model averaging is especially useful when one deals with sets of moderately to highly correlated predictors, because the underlying ridge regression accommodates correlated predictors without blowing up estimation variance. A two-model theoretical example shows that the relative reduction of mean squared error is increasing with the strength of the correlation. I also demonstrate the superiority of the ridge- regularized modifications via empirical examples focused on wages and economic growth. The second chapter focuses on the use of elastic-net regression for instrumental variable estimation. I investigate the relative performance of the lasso and elastic-net estimators for fitting the first-stage as part of IV estimation. Because elastic-net includes...

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