Browsing by Subject "Variable selection"
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Item Open Access News media and attention spillover across energy markets: a powerful predictor of crude oil futures prices(International Association for Energy Economics, 2022) Cepni, Oğuzhan; Nguyen, Duc Khuong; Şensoy, AhmetWe develop two news-based investor attention measures from the news trends function of the Bloomberg terminal and investigate their predictive power for returns on crude oil futures contracts with various maturities. Our main results after controlling for relevant macroeconomic variables show that the Oil-based Institutional Attention Index is useful in predicting oil futures returns, especially during price downturn periods, while the forecasting accuracy is further improved when the Commodity Market Institutional Attention Index is used. This forecasting accuracy decreases, however, with the maturity of oil futures contracts. Moreover, we find some evidence of Granger-causality and regime-dependent interactions between investor attention measures and oil futures returns. Finally, variable selection algorithms matter before making predictions since they create the best forecasting results in many cases considered. These findings are important for in-formed traders and policymakers to better understand the price dynamics of the oil markets. © 2022 by the IAEE. All rights reserved.Item Open Access Variable selection in regression using maximal correlation and distance correlation(Taylor and Francis Ltd., 2015) Yenigün, C. D.; Rizzo, M. L.In most of the regression problems the first task is to select the most influential predictors explaining the response, and removing the others from the model. These problems are usually referred to as the variable selection problems in the statistical literature. Numerous methods have been proposed in this field, most of which address linear models. In this study we propose two variable selection criteria for regression based on two powerful dependence measures, maximal correlation and distance correlation. We focus on these two measures since they fully or partially satisfy the Rényi postulates for dependence measures, and thus they are able to detect nonlinear dependence structures. Therefore, our methods are considered to be appropriate in linear as well as nonlinear regression models. Both methods are easy to implement and they perform well. We illustrate the performances of the proposed methods via simulations, and compare them with two benchmark methods, stepwise Akaike information criterion and lasso. In several cases with linear dependence all four methods turned out to be comparable. In the presence of nonlinear or uncorrelated dependencies, we observed that our proposed methods may be favourable. An application of the proposed methods to a real financial data set is also provided. © 2014, Taylor & Francis.