Toward optimal model averaging in regression models with time series errors

Tzu Chang F. Cheng, Ching Kang Ing, Shu Hui Yu

研究成果: 雜誌貢獻期刊論文同行評審

26 引文 斯高帕斯(Scopus)


Consider a regression model with infinitely many parameters and time series errors. We are interested in choosing weights for averaging across generalized least squares (GLS) estimators obtained from a set of approximating models. However, GLS estimators, depending on the unknown inverse covariance matrix of the errors, are usually infeasible. We therefore construct feasible generalized least squares (FGLS) estimators using a consistent estimator of the unknown inverse matrix. Based on this inverse covariance matrix estimator and FGLS estimators, we develop a feasible autocovariance-corrected Mallows model averaging criterion to select weights, thereby providing an FGLS model averaging estimator of the true regression function. We show that the generalized squared error loss of our averaging estimator is asymptotically equivalent to the minimum one among those of GLS model averaging estimators with the weight vectors belonging to a continuous set, which includes the discrete weight set used in Hansen (2007) as its proper subset.

頁(從 - 到)321-334
期刊Journal of Econometrics
出版狀態已出版 - 12月 2015


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