Model Comparisons in Unstable Environments
AbstractThe goal of this paper is to develop formal techniques for analyzing the relative in-sample performance of two competing, misspeci?ed models in the presence of possible data instability. The central idea of our methodology is to propose a measure of the models? local relative performance: the "local Kullback-Leibler Information Criterion" (KLIC), which measures the relative distance of the two models? (misspeci?ed) likelihoods from the true likelihood at a particular point in time. We discuss estimation and inference about the local relative KLIC; in particular, we propose statistical tests to investigate its stability over time. Compared to previous approaches to model selection, which are based on measures of "global performance", our focus is on the entire time path of the models? relative performance, which may contain useful information that is lost when looking for a globally best model. The empirical application provides insights into the time variation in the performance of a representative DSGE model of the European economy relative to that of VARs. implement IRFMEs.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 09-10.
Length: 44 Pages
Date of creation: 2009
Date of revision:
Contact details of provider:
Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/
Model Selection Tests; Misspeci?cation; Structural Change; Kullback-Leibler Information Criterion;
Other versions of this item:
- Barbara Rossi & Raffaella Giacomini, 2010. "Model Comparisons in Unstable Environments," Working Papers 10-29, Duke University, Department of Economics.
- Raffaella Giacomini & Barbara Rossi, 2012. "Model comparisons in unstable environments," CeMMAP working papers CWP13/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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.:
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- Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
- Rossi, José Luiz Júnior, 2014. "The Usefulness of Financial Variables in Predicting Exchange Rate Movements," Insper Working Papers wpe_332, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
- Leandro M. Magnusson & Sophocles Mavroeidis, 2011. "Identification Using Stability Restrictions," Working Papers 1116, Tulane University, Department of Economics.
- Rossi, José Luiz Júnior, 2013. "Liquidity and Exchange Rates," Insper Working Papers wpe_325, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
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