Further Results on Forecasting and Model Selection under Asymmetric Loss
AbstractWe make three related contributions. First, we propose a new technique for solving prediction problems under asymmetric loss using piecewise-linear approximations to the loss function, and we establish existence and uniqueness of the optimal predictor. Second, we provide a detailed application to optimal prediction of a conditionally heteroscedastic process under asymmetric loss, the insights gained from which are broadly applicable. Finally, we incorporate our results into a general framework for recursive prediction-based model selection under the relevant loss function. Copyright 1996 by John Wiley & Sons, Ltd.
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Bibliographic InfoArticle provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.
Volume (Year): 11 (1996)
Issue (Month): 5 (Sept.-Oct.)
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Web page: http://www.interscience.wiley.com/jpages/0883-7252/
Other versions of this item:
- Christoffersen & Diebold, . "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Home Pages _059, University of Pennsylvania.
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.:
- McCloskey, Donald N, 1985. "The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests," American Economic Review, American Economic Association, vol. 75(2), pages 201-05, May.
- Zellner, A., 1992. "Statistics, Science and Public Policy," Papers 92-21, California Irvine - School of Social Sciences.
- Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
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- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
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