Economics at your fingertips  

Higher order expansions for error variance matrix estimates in the Gaussian AR(1) linear regression model

Yiannis Karavias (), Spyridon D. Symeonides and Elias Tzavalis ()

Statistics & Probability Letters, 2018, vol. 135, issue C, 54-59

Abstract: We derive a stochastic expansion of the error variance–covariance matrix estimator for the linear regression model under Gaussian AR(1) errors. The higher order accuracy terms of the refined formula are not directly derived from formal Edgeworth-type expansions but instead, the paper adopts Magadalinos’ (1992) stochastic order of ω which is a convenient device to obtain the equivalent relation between the stochastic expansion and the asymptotic approximation of corresponding distribution functions. A Monte Carlo experiment compares tests based on the new estimator with others in the literature and shows that the new tests perform well.

Keywords: Linear regression; AR(1) disturbances; Stochastic expansions; Asymptotic approximations; Autocorrelation robust inference (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul

More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-10-15
Handle: RePEc:eee:stapro:v:135:y:2018:i:c:p:54-59