Kapetanios, G. and Psaradakis, Zacharias (2014) Semiparametric sieve-type generalized least squares inference. Econometric Reviews 35 (6), pp. 951-985. ISSN 0747-4938.
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Abstract
This article considers the problem of statistical inference in linear regression models with dependent errors. A sieve-type generalized least squares (GLS) procedure is proposed based on an autoregressive approximation to the generating mechanism of the errors. The asymptotic properties of the sieve-type GLS estimator are established under general conditions, including mixingale-type conditions as well as conditions which allow for long-range dependence in the stochastic regressors and/or the errors. A Monte Carlo study examines the finite-sample properties of the method for testing regression hypotheses.
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis, available online: http://wwww.tandfonline.com/10.1080/07474938.2014.975639 |
| Keyword(s) / Subject(s): | Autoregressive approximation, Generalized least squares, Linear regression, Long-range dependence, Short-range dependence, C22 |
| School: | Birkbeck Faculties and Schools > Faculty of Business and Law > Birkbeck Business School |
| Depositing User: | Zacharias Psaradakis |
| Date Deposited: | 19 Jun 2015 11:26 |
| Last Modified: | 02 Aug 2023 17:17 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/12369 |
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