SSOAR Logo
    • Deutsch
    • English
  • English 
    • Deutsch
    • English
  • Login
SSOAR ▼
  • Home
  • About SSOAR
  • Guidelines
  • Publishing in SSOAR
  • Cooperating with SSOAR
    • Cooperation models
    • Delivery routes and formats
    • Projects
  • Cooperation partners
    • Information about cooperation partners
  • Information
    • Possibilities of taking the Green Road
    • Grant of Licences
    • Download additional information
  • Operational concept
Browse and search Add new document OAI-PMH interface
JavaScript is disabled for your browser. Some features of this site may not work without it.

Download PDF
Download full text

(386.7Kb)

Citation Suggestion

Please use the following Persistent Identifier (PID) to cite this document:
https://nbn-resolving.org/urn:nbn:de:0168-ssoar-246531

Exports for your reference manager

Bibtex export
Endnote export

Display Statistics
Share
  • Share via E-Mail E-Mail
  • Share via Facebook Facebook
  • Share via Bluesky Bluesky
  • Share via Reddit reddit
  • Share via Linkedin LinkedIn
  • Share via XING XING

A stationary unbiased finite sample ARCH-LM test procedure

[journal article]

Sjölander, Pär

Abstract

Engle's (1982) ARCH-LM test is the standard test to detect autoregressive conditional heteroscedasticity. In this paper, Monte Carlo simulations are used to demonstrate that the test's statistical size is biased in finite samples. Two complementing remedies to the related problems are proposed. One ... view more

Engle's (1982) ARCH-LM test is the standard test to detect autoregressive conditional heteroscedasticity. In this paper, Monte Carlo simulations are used to demonstrate that the test's statistical size is biased in finite samples. Two complementing remedies to the related problems are proposed. One simple solution is to simulate new unbiased critical values for the ARCH-LM test. A second solution is based on the observation that for econometrics practitioners, detection of ARCH is generally followed by remedial modeling of this time-varying heteroscedasticity by the most general and robust model in the ARCH family; the GARCH(1,1) model. If the GARCH model's stationarity constraints are violated, as in fact is very often the case, obviously, we can conclude that ARCH-LM’s detection of conditional heteroscedasticity has no or limited practical value. Therefore, formulated as a function of whether the GARCH model's stationarity constraints are satisfied or not, an unbiased and more relevant two-step ARCH-LM test is specified. If the primary objectives of the study are to detect and remedy the problems of conditional heteroscedasticity, or to interpret GARCH parameters, the use of this paper’s new two-step procedure, 2S-UARCH-LM, is strongly recommended.... view less

Classification
Economic Statistics, Econometrics, Business Informatics

Free Keywords
ARCH-LM; GARCH; Non-negativity Constraints; Stationarity Constraints

Document language
English

Publication Year
2010

Page/Pages
p. 1019-1033

Journal
Applied Economics, 43 (2010) 8

DOI
https://doi.org/10.1080/00036840802600046

Status
Postprint; peer reviewed

Licence
PEER Licence Agreement (applicable only to documents from PEER project)


GESIS LogoDFG LogoOpen Access Logo
Home  |  Legal notices  |  Operational concept  |  Privacy policy
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.
 

 


GESIS LogoDFG LogoOpen Access Logo
Home  |  Legal notices  |  Operational concept  |  Privacy policy
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.