Toward optimal model averaging in regression models with time series errors

Tzu-Chang F. Cheng, Ching-Kang Ing () and Shu-Hui Yu

Journal of Econometrics, 2015, vol. 189, issue 2, 321-334

Abstract: 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.

Keywords: Asymptotic efficiency; Autocovariance-corrected Mallows model averaging; Banded Cholesky factorization; Feasible generalized least squares estimator; High-dimensional covariance matrix; Time series errors (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:189:y:2015:i:2:p:321-334

DOI: 10.1016/j.jeconom.2015.03.026

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