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Essays on Model Uncertainty and Model Averaging
Skolkova, Alena ; Jurajda, Štěpán (advisor) ; Lafférs, Lukáš (referee) ; Mikusheva, Anna (referee)
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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Price Determinants of Art Photography at Auctions
Habalová, Veronika ; Šopov, Boril (advisor) ; Bauer, Michal (referee)
In the recent years, prices of art have repeatedly broken records, and the interest in investing in fine art photography has been growing. Although there is plenty of research dedicated to studying prices of paintings, fine art photography has been largely overlooked. This thesis aims to shed light on identifying price determinants for this particular medium. A new data set is collected from sold lot archives of Sotheby's and Phillips auction houses, which also provide images of some of the sold items. These images are then used to create new variables describing visual attributes of the artworks. In order to inspect the effect of color-related predictors on price, four different methods are discussed. Color is found to be significant in OLS model, but the effect diminishes when model averaging is applied. Machine learning al- gorithms - regression trees and random forests - suggest that the importance of color is relatively low. The thesis also shows that expert estimates can improved by incorporating available information and using random forests for prediction. The fact that the expert estimates are not very accurate sug- gest that they either do not use all the available information or they do not process it efficiently. 1
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Exchange Rate Forecasting: An Application with Model Averaging Techniques
Mida, Jaroslav ; Horváth, Roman (advisor) ; Bobková, Božena (referee)
The exchange rate forecasting has been an interesting topic for a long time. Beating the random walk model has been the goal of many researchers, who applied various techniques and used various datasets. We tried to beat it using bayesian model averaging technique, which pools a large amount of models and the final forecast is the average of forecasts of these models. We used quarterly data from 1980 to 2013 and attempted to predict the value of exchange rate return of five currency pairs. The novelty was the fact that none of these currency pairs included U.S. Dollar. The forecasting horizon was one, two, four and eight quarters. In addition to random walk, we also compared our results to historical average return model using several benchmarks, such as root mean squared error, mean absolute error or direction of change statistic. We found out that bayesian model averaging can not generally outperform random walk or historical average return, but in specific setting it can produce forecasts with low error and with high percentage of correctly predicted signs of change.
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