74

Social science research network

2008

Bartlett Correction, , Likelihood Ratio Test , Curvature

براي دانلود و مشاهده مقاله به قسمت لينكهاي مرتبط مراجعه نماييد

Abstract: Difficulties with inference in predictive regressions are generally attributed to strong persistence in the predictor series. We show that the major source of the problem is actually the nuisance intercept parameter and propose basing inference on the Restricted Likelihood, which is free of such nuisance location parameters and also possesses small curvature, making it suitable for inference. The bias of the Restricted Maximum Likelihood (REML) estimates is shown to be approximately 50% less than that of the OLS estimates near the unit root, without loss of eýciency. The error in the chi-square approximation to the distribution of the REML based Likelihood Ratio Test (RLRT) for no predictability is shown to be (3/4-rho^2)n^-1 (G3 (x) -G1 (x)) O(n^-2); where rho < 1 is the correlation of the innovation series and Gs (x) is the c.d.f. of a chi-sq random variable. This very small error, free of the AR parameter, suggests that the RLRT for predictability has very good size properties even when the regressor has strong persistence. The Bartlett corrected RLRT achieves an O(n^-2) error.

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=932996,/DL/Data Entry/DataEntryForm/EnterDocInfo.aspx,/DL/Data Entry/NewEdit/Documents/Math_English_Electronic_Articles_EditDoc_925.aspx