期刊
JOURNAL OF FINANCIAL ECONOMETRICS
卷 -, 期 -, 页码 -出版社
OXFORD UNIV PRESS
DOI: 10.1093/jjfinec/nbad030
关键词
integrated; nearly integrated; random weighting; unit roots; weighted estimation equation; C12; C58; G12
This article examines the discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables and proposes a unified inference procedure based on weighted estimation for statistical inference. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is suggested for constructing confidence regions. The proposed method outperforms existing methods in simulations and further explores the predictability of asset returns in a setting with endogenous state variables.
There is a discrepancy in the limiting distributions of least-squares estimators for stationary and integrated variables. For statistical inference, it must be decided which distribution should be used in advance. This motivates us to develop a unifying inference procedure based on weighted estimation. The asymptotic distributions of the proposed estimators are developed and a random weighting bootstrap method is proposed for constructing confidence regions. The proposed method outperforms existing methods (with time constant or time-varying error variance) in simulations. We further study the predictability of asset returns in a setting where some of our state variables are endogenous.
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