Arbeitspapier

Asymptotic properties of the weighted average least squares (WALS) estimator

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.

Sprache
Englisch

Erschienen in
Series: Tinbergen Institute Discussion Paper ; No. TI 2022-022/III

Klassifikation
Wirtschaft
Bayesian Analysis: General
Estimation: General
Model Construction and Estimation
Model Evaluation, Validation, and Selection
Thema
Model averaging
normal location model
consistency
asymptotic normality
WALS

Ereignis
Geistige Schöpfung
(wer)
De Luca, Giuseppe
Magnus, Jan
Peracchi, Franco
Ereignis
Veröffentlichung
(wer)
Tinbergen Institute
(wo)
Amsterdam and Rotterdam
(wann)
2022

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • De Luca, Giuseppe
  • Magnus, Jan
  • Peracchi, Franco
  • Tinbergen Institute

Entstanden

  • 2022

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