Least Squares Model Averaging by Prediction Criterion
This paper proposes a new estimator for least squares model averaging. A model average estimator is a weighted average of common estimates obtained from a set of models. We propose computing weights by minimizing a model average prediction criterion (MAPC). We prove that the MAPC estimator is asymptotically optimal in the sense of achieving the lowest possible mean squared error. For statistical inference, we derive asymptotic tests for single hypotheses and joint hypotheses on the average coefficients for the "core" regressors. These regressors are of primary interest to us and are included in every approximation model. To improve the finite sample performance, we also consider bootstrap tests. In simulation experiments the MAPC estimator is shown to have significant efficiency gains over existing model selection and model averaging methods. We also show that the bootstrap tests have more reasonable rejection frequency than the asymptotic tests in small samples. As an empirical illustration, we apply the MAPC estimator to cross-country economic growth models.
|Date of creation:||Nov 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Kingston, Ontario, K7L 3N6|
Phone: (613) 533-2250
Fax: (613) 533-6668
Web page: http://qed.econ.queensu.ca/
More information through EDIRC
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.:
- Barro, R.J., 1989.
"Economic Growth In A Cross Section Of Countries,"
RCER Working Papers
201, University of Rochester - Center for Economic Research (RCER).
- Chun Liu & John M. Maheu, 2009.
"Forecasting realized volatility: a Bayesian model-averaging approach,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 24(5), pages 709-733.
- Chun Liu & John M Maheu, 2008. "Forecasting Realized Volatility: A Bayesian Model Averaging Approach," Working Papers tecipa-313, University of Toronto, Department of Economics.
- Douglas Staiger & James H. Stock, 1997.
"Instrumental Variables Regression with Weak Instruments,"
Econometric Society, vol. 65(3), pages 557-586, May.
- Douglas Staiger & James H. Stock, 1994. "Instrumental Variables Regression with Weak Instruments," NBER Technical Working Papers 0151, National Bureau of Economic Research, Inc.
- Pagan, Adrian, 1987. " Three Econometric Methodologies: A Critical Appraisal," Journal of Economic Surveys, Wiley Blackwell, vol. 1(1), pages 3-24.
- Russell Davidson & James MacKinnon, 2000.
"Bootstrap tests: how many bootstraps?,"
Taylor & Francis Journals, vol. 19(1), pages 55-68.
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Wan, Alan T.K. & Zhang, Xinyu & Zou, Guohua, 2010. "Least squares model averaging by Mallows criterion," Journal of Econometrics, Elsevier, vol. 156(2), pages 277-283, June.
- Hendry, David F., 1976. "The structure of simultaneous equations estimators," Journal of Econometrics, Elsevier, vol. 4(1), pages 51-88, February.
- Hansen, Bruce E. & Racine, Jeffrey S., 2012. "Jackknife model averaging," Journal of Econometrics, Elsevier, vol. 167(1), pages 38-46.
- David F. Hendry & Bent Nielsen, 2007. "Preface to Econometric Modeling: A Likelihood Approach," Introductory Chapters, in: Econometric Modeling: A Likelihood Approach Princeton University Press.
When requesting a correction, please mention this item's handle: RePEc:qed:wpaper:1299. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Babcock)
If references are entirely missing, you can add them using this form.