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主讲人 |
Hongqi Chen |
简介 |
<p>We extend Quantile Partial Correlation Regression (QPCR) to high-dimensional time-series settings by establishing selection consistency of QPCR under dependent, heteroskedastic, and potentially unbounded processes. We introduce an EBIC-type tuning criterion that recovers the true quantile model with high probability. Applying QPCR to predict monthly industrial production growth using a large number of macrofinancial predictors, we find that labour-market and financial conditions are the primary drivers of downside growth risk, while there is little predictive content for upside risk. Decomposing downside risk into its individual components, we construct sector-specific indices that predict it, while controlling for information from other sectors, thereby isolating the downside risks emanating from each sector. </p> |
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主讲人简介 |
<p>Hongqi Chen is currently an Assistant Professor at the College of Finance and Statistics, Hunan University. He received his Ph.D. from the University of Illinois. His research interests include econometric theory, applied econometrics and statistics, with a focus on high-dimensional statistical methods and quantile regression. </p> |
期数 |
高级计量经济学与统计学系列讲座2026年春季学期第七讲（总200讲） |
