Testing interval forecasts: a GMM-based approach
AbstractThis paper proposes a new evaluation framework for interval forecasts. Our model free test can be used to evaluate intervals forecasts and High Density Regions, potentially discontinuous and/or asymmetric. Using a simple J-statistic, based on the moments de ned by the orthonormal polynomials associated with the Binomial distribution, this new approach presents many advantages. First, its implementation is extremely easy. Second, it allows for a separate test for unconditional coverage, independence and conditional coverage hypotheses. Third, Monte-Carlo simulations show that for realistic sample sizes, our GMM test has good small-sample properties. These results are corroborated by an empirical application on SP500 and Nikkei stock market indexes. It con rms that using this GMM test leads to major consequences for the ex-post evaluation of interval forecasts produced by linear versus nonlinear models.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by HAL in its series Working Papers with number halshs-00618467.
Date of creation: Aug 2011
Date of revision:
Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00618467/en/
Contact details of provider:
Web page: http://hal.archives-ouvertes.fr/
Interval forecasts; High Density Region; GMM.;
Other versions of this item:
- NEP-ALL-2011-10-01 (All new papers)
- NEP-ECM-2011-10-01 (Econometrics)
- NEP-ETS-2011-10-01 (Econometric Time Series)
- NEP-FOR-2011-10-01 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- BONTEMPS, Christian & MEDDAHI, Nour, 2002.
"Testing Normality : A GMM Approach,"
Cahiers de recherche
2002-14, Universite de Montreal, Departement de sciences economiques.
- Bontemps, Christian & Meddahi, Nour, 2005. "Testing Normality: a GMM Approach," Open Access publications from University of Toulouse 1 Capitole http://neeo.univ-tlse1.fr, University of Toulouse 1 Capitole.
- Christian Bontemps & Nour Meddahi, 2002. "Testing Normality: A GMM Approach," CIRANO Working Papers 2002s-63, CIRANO.
- Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
- Christophe Hurlin & Gilbert Colletaz & Sessi Tokpavi & Bertrand Candelon, 2008.
"Backtesting Value-at-Risk: A GMM Duration-Based Test,"
- Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 9(2), pages 314-343, Spring.
- Candelon Bertrand & Colletaz Gilberg & Hurlin Christophe & Tokpavi Sessi, 2009. "Backtesting Value-at-Risk: A GMM Duration-based Test," Research Memoranda 051, Maastricht : METEOR, Maastricht Research School of Economics of Technology and Organization.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2005.
"Evaluating Value-at-Risk models with desk-level data,"
Working Paper Series
010, North Carolina State University, Department of Economics, revised Dec 2006.
- Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
- Peter Christoffersen & Jeremy Berkowitz & Denis Pelletier, 2008. "Evaluating Value-at-Risk Models with Desk-Level Data," CREATES Research Papers 2009-35, School of Economics and Management, University of Aarhus.
- David I. Harvey & Stephen J. Leybourne, 2007. "Testing for time series linearity," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 149-165, 03.
- Wallis, Kenneth F., 2003.
"Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts,"
International Journal of Forecasting,
Elsevier, vol. 19(2), pages 165-175.
- Wallis, Kenneth F., 2001. "Chi-squared tests of interval and density forecasts and the Bank of England's fan charts," Working Paper Series 0083, European Central Bank.
- Wallis, Kenneth F., 2002. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," Royal Economic Society Annual Conference 2002 181, Royal Economic Society.
- Dumitrescu, Elena-Ivona, 2012. "Econometric methods for financial crises," Open Access publications from Maastricht University urn:nbn:nl:ui:27-29274, Maastricht University.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD).
If references are entirely missing, you can add them using this form.