Inference about predictive ability when there are many predictors
AbstractWe enhance the theory of asymptotic inference about predictive ability by considering the case when a set of variables used to construct predictions is sizable. To this end, we consider an alternative asymptotic framework where the number of predictors tends to in nity with the sample size, although more slowly. Depending on the situation the asymptotic normal distribution of an average prediction criterion either gains additional variance as in the few predictors case, or gains non-zero bias which has no analogs in the few predictors case. By properly modifying conventional test statistics it is possible to remove most size distortions when there are many predictors, and improve test sizes even when there are few of them.
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 Center for Economic and Financial Research (CEFIR) in its series Working Papers with number w0096.
Length: 43 pages
Date of creation: Jan 2007
Date of revision:
Contact details of provider:
Postal: 117418 Russia, Moscow, Nakhimovsky pr., 47, office 720
Phone: +7 (495) 105 50 02
Fax: +7 (495) 105 50 03
Web page: http://www.cefir.ru
More information through EDIRC
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-28 (All new papers)
- NEP-ECM-2007-01-28 (Econometrics)
- NEP-ETS-2007-01-28 (Econometric Time Series)
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.:
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 20(1), pages 134-44, January.
- Kenneth D. West, 1994.
"Asymptotic Inference About Predictive Ability,"
- Kenneth D. West & Todd Clark, 2006.
"Approximately Normal Tests for Equal Predictive Accuracy in Nested Models,"
NBER Technical Working Papers
0326, National Bureau of Economic Research, Inc.
- Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Koenker, Roger, 1988. "Asymptotic Theory and Econometric Practice," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 3(2), pages 139-47, April.
- Todd E. Clark & Michael W. McCracken, 1999.
"Tests of equal forecast accuracy and encompassing for nested models,"
Research Working Paper
99-11, Federal Reserve Bank of Kansas City.
- Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
- Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
- Todd E. Clark & Michael McCracken, 1999. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Computing in Economics and Finance 1999 1241, Society for Computational Economics.
- John Chao & Norman Swanson, 2004.
"Consistent Estimation with a Large Number of Weak Instruments,"
Departmental Working Papers
200421, Rutgers University, Department of Economics.
- John C. Chao & Norman R. Swanson, 2005. "Consistent Estimation with a Large Number of Weak Instruments," Econometrica, Econometric Society, vol. 73(5), pages 1673-1692, 09.
- John C. Chao & Norman Rasmus Swanson, 2004. "Consistent Estimation with a Large Number of Weak Instruments," Yale School of Management Working Papers ysm374, Yale School of Management.
- Chao, John Chao & Norman R. Swanson, 2003. "Consistent Estimation with a Large Number of Weak Instruments," Cowles Foundation Discussion Papers 1417, Cowles Foundation for Research in Economics, Yale University.
- McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
- repec:att:wimass:9710 is not listed on IDEAS
- Koenker, Roger & Machado, Jose A. F., 1999. "GMM inference when the number of moment conditions is large," Journal of Econometrics, Elsevier, vol. 93(2), pages 327-344, December.
- Bekker, Paul A, 1994. "Alternative Approximations to the Distributions of Instrumental Variable Estimators," Econometrica, Econometric Society, vol. 62(3), pages 657-81, May.
- Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
- West, Kenneth D & McCracken, Michael W, 1998.
"Regression-Based Tests of Predictive Ability,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-40, November.
- Graham Elliott & Allan Timmermann, 2008.
Journal of Economic Literature,
American Economic Association, vol. 46(1), pages 3-56, March.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- John Galbraith & Victoria Zinde-Walsh, 2006. "Reduced-Dimension Control Regression," Departmental Working Papers 2006-17, McGill University, Department of Economics.
- Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011.
"Forecasting the price of oil,"
International Finance Discussion Papers
1022, Board of Governors of the Federal Reserve System (U.S.).
- Todd Clark & Michael W. McCracken, 2011.
"Advances in forecast evaluation,"
1120, Federal Reserve Bank of Cleveland.
- Calhoun, Gray, 2014. "Out-Of-Sample Comparisons of Overfit Models," Staff General Research Papers 32462, Iowa State University, Department of Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Julia Babich).
If references are entirely missing, you can add them using this form.