Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/263942 
Year of Publication: 
2022
Series/Report no.: 
Tinbergen Institute Discussion Paper No. TI 2022-022/III
Publisher: 
Tinbergen Institute, Amsterdam and Rotterdam
Abstract: 
We investigate the asymptotic behavior of the WALS estimator, a model-averaging estimator with attractive finite-sample and computational properties. WALS is closely related to the normal location model, and hence much of the paper concerns the asymptotic behavior of the estimator of the unknown mean in the normal local model. Since we adopt a frequentist-Bayesian approach, this specializes to the asymptotic behavior of the posterior mean as a frequentist estimator of the normal location parameter. We emphasize two challenging issues. First, our definition of ignorance in the Bayesian step involves a prior on the t-ratio rather than on the parameter itself. Second, instead of assuming a local misspecification framework, we consider a standard asymptotic setup with fixed parameters. We show that, under suitable conditions on the prior, the WALS estimator is sqrt(n)-consistent and its asymptotic distribution essentially coincides with that of the unrestricted least-squares estimator. Monte Carlo simulations confirm our theoretical results.
Subjects: 
Model averaging
normal location model
consistency
asymptotic normality
WALS
JEL: 
C11
C13
C51
C52
Document Type: 
Working Paper

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