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Autoregressive Lag-Order Selection Using Conditional Saddlepoint Approximations


Butler, Ronald; Paolella, Marc (2017). Autoregressive Lag-Order Selection Using Conditional Saddlepoint Approximations. Econometrics, 5(3):43.

Abstract

A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.

Abstract

A new method for determining the lag order of the autoregressive polynomial in regression models with autocorrelated normal disturbances is proposed. It is based on a sequential testing procedure using conditional saddlepoint approximations and permits the desire for parsimony to be explicitly incorporated, unlike penalty-based model selection methods. Extensive simulation results indicate that the new method is usually competitive with, and often better than, common model selection methods.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Language:English
Date:19 September 2017
Deposited On:22 Nov 2017 14:02
Last Modified:19 Feb 2018 09:21
Publisher:MDPI Publishing
ISSN:2225-1146
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.3390/econometrics5030043
Related URLs:http://www.mdpi.com/2225-1146/5/3/43/htm (Publisher)
Other Identification Number:merlin-id:15387

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