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Department seminar by Liang Peng, Georgia Institute of TechnologyExport this event to calendar

Thursday, November 8, 2012 — 4:00 PM EST

 "Interval estimation for random coefficient AR(1) model and predictive regression model"

The first part of this talk is to use empirical likelihood methods based on a weighted score equation to construct a unified confidence interval for the coefficient in a random coefficient AR(1) model. Unlike existing methods, the new methods do not need to distinguish whether the coefficient is random or deterministic and whether the process is stationary or non-stationary. A simulation study confirms the good finite sample behavior of the proposed methods, and we apply the new methods to study U.S. macroeconomic data.

The second part of this talk is about inference for predictive regression models, which have been studied extensively in financial econometrics during the recent years. For testing the predictability, procedures based on the least squares estimator and other bias-corrected estimators proposed in the literature suffer from a complicated asymptotic limit, which depends on whether the predicting variable is stationary or non-stationary or has an infinity variance. Here we propose a unified method to test the predictability regardless of predicting variable being stationary, or non-stationary or having an infinite variance. An empirical study is examined too.

Location 
M3 - Mathematics 3
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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