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Prof. Heather Anderson and Prof. Farshid Vahid's Lecture Notice

Publish Date: 2017/12/11 16:32:12    Hits:

Title:High-Dimensional Predictive Regression in the Presence of Cointegration

Presenter:Prof. Heather Anderson

Host:Prof. Ying Fan

Time:2017.12.11 15:00-16:00



We propose a Least Absolute Shrinkage and Selection Operator (LASSO) estimator of a predictive regression in which stock returns are conditioned on a large set of lagged covariates, some of which are highly persistent and potentially cointegrated. We establish the asymptotic properties of the proposed LASSO estimator and validate our theoretical findings using simulation studies. The application of this proposed LASSO approach to forecasting stock returns suggests that a cointegrating relationship among the persistent predictors leads to a significant improvement in the prediction of stock returns over various competing forecasting methods with respect to mean squared error.

About the presenter

Heather M. Anderson holds the Maureen Brunt Chair in Economics and Econometrics at Monash University in Melbourne Australia, and is an Elected Fellow of the Academy of the Social Sciences in Australia. She wrote her PhD thesis under the supervision of Clive Granger (who later received the Nobel Prize in economics) at the University of California in San Diego, and has held academic positions at the University of Texas at Austin, Texas A and M University, the Australian National University and Monash University. Her research interests include financial econometrics, nonlinear time series, macro-econometrics and forecasting. Her work has been published in theReview of Economics and Statistics, theJournal of Econometrics, theJournal of Applied Econometricsand theJournal of Business and Economic Statistics. She was coeditor ofEmpirical Economicsfrom 2006 until 2015 and is currently on the editorial board of several journals, including theJournal of Applied Econometrics.