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Title: Variable selection for high-dimensional regression models with time series and heteroscedastic errors Authors:  Hai-Tang Chiou - National Tsing Hua University (Taiwan) [presenting]
Meihui Guo - National Sun Yat-sen University (Taiwan)
Ching-Kang Ing - National Tsing Hua University (Taiwan)
Abstract: Although existing literature on high-dimensional regression models is rich, the vast majority of studies have focused on independent and homogeneous error terms. We consider the problem of selecting high-dimensional regression models with heteroscedastic and time series errors, which have broad applications in economics, quantitative finance, environmental science, and many other fields. The error term in our model is not only allowed to be short- or long-range dependent, but also contains a high-dimensional dispersion function accounting for heteroscedasticity. By making use of the orthogonal greedy algorithm and the high-dimensional information criterion, we propose a new model selection procedure that can consistently choose the relevant variables in both the regression and the dispersion functions. The finite sample performance of the proposed procedure is also illustrated via simulations and real data analysis.