Corradi, Valentina and Swanson, Norman R. (Norman Rasmus), 1964- (2004) Predictive density accuracy tests. Working Paper. Warwick Business School, Financial Econometrics Research Centre, Coventry.

Preview

PDF WRAP_Corradi_fwp04-16.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader Download (1064Kb)

Abstract

This paper outlines a testing procedure for assessing the relative out-of-sample predictive accuracy of multiple conditional distribution models, and surveys existing related methods in the area of predictive density evaluation, including methods based on the probability integral transform and the Kullback-Leibler Information Criterion. The procedure is closely related to Andrews’ (1997) conditional Kolmogorov test and to White’s (2000) reality check approach, and involves comparing square (approximation) errors associated with models i, i = 1, ..., n, by constructing weighted averages over U of E !" Fi(u|Zt, !†i ) − F0(u|Zt, !0) #2 $ , where F0(·|·) and Fi(·|·) are true and approximate distributions, u # U, and U is a possibly unbounded set on the real line. Appropriate bootstrap procedures for obtaining critical values for tests constructed using this measure of loss in conjunction with predictions obtained via rolling and recursive estimation schemes are developed. We then apply these bootstrap procedures to the case of obtaining critical values for our predictive accuracy test. A Monte Carlo experiment comparing our bootstrap methods with methods that do not include location bias adjustment terms is provided, and results indicate coverage improvement when our proposed bootstrap procedures are used. Finally, an empirical example comparing alternative predictive densities for U.S. inflation is given.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	H Social Sciences > HB Economic Theory
Divisions:	Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH):	Bootstrap (Statistics), Estimation theory, Distribution (Economic theory), Monte Carlo method
Series Name:	Working papers (Warwick Business School. Financial Econometrics Research Centre)
Publisher:	Warwick Business School, Financial Econometrics Research Centre
Place of Publication:	Coventry
Date:	February 2004
Number:	No.04-
Number of Pages:	54
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Economic and Social Research Council (Great Britain) (ESRC)
Grant number:	RES-000-23-0006 (ESRC)
References:	Andrews, D.W.K., (1997), A Conditional Kolmogorov Test, Econometrica, 65, 1097-1128. Andrews, D.W.K., (2002a), Higher-Order Improvements of a Computationally Attractive k−step Bootstrap for Extremum Estimators, Econometrica, 70, 119-162. Andrews, D.W.K., (2002b), The Block-Block Bootstrap: Improved Asymptotic Refinements, Manuscript, Cowles Foundation in Economics, Yale University. Bai, J., (2003), Testing Parametric Conditional Distributions of Dynamic Models, Review of Economics and Statistics, 85, 531-549. Bontemps, C., and N. Meddahi, (2003a), Testing Normality: a GMM Approach, Journal of Econometrics, forthcoming. Bontemps, C., and N. Meddahi, (2003b), Testing Distributional Assumptions: a GMM Approach, Working Paper, University of Montreal. Chang, Y.S., J.F. Gomes, and F. Schorfheide, (2002), Learning-by-Doing as a Propagation Mechanism, American Economic Review, 92, 1498-1520. Christoffersen, P.F., (1998), Evaluating Interval Forecasts, International Economic Review, 39, 841-862. Christoffersen, P.F., J. Hahn and A. Inoue, (2001), Testing and Comparing Value-at-Risk Measures, Journal of Empirical Finance, 8, 325-342. Clark, T.E. and M.W. McCracken, (2001), Tests of Equal Forecast Accuracy and Encompassing for Nested Models, Journal of Econometrics, 105, 85-110. Clark, T.E. and M.W. McCracken, (2003), Forecast Based Model Selection in the Presence of Structural Breaks, Journal of Econometrics, forthcoming. Corradi, V., (1999), Deciding between I(0) and I(1) via FLIL-Based Bounds, Econometric Theory, 15, 643-663. Corradi, V. and N.R. Swanson, (2002), A Consistent Test for Out of Sample Nonlinear Predictive Ability, Journal of Econometrics, 110, 353-381. Corradi, V. and N.R. Swanson, (2003a), The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation, Working Paper, Queen Mary, University of London and Rutgers University. Corradi, V., N.R. Swanson, (2003b), A Test For Comparing Multiple Misspecified Conditional Distribution Models, Working Paper, Queen Mary, University of London and Rutgers University. Corradi, V. and N.R. Swanson, (2003c), Bootstrap Conditional Distribution Tests in the Presence of Dynamic Misspecification, Journal of Econometrics, forthcoming. Diebold, F.X., T. Gunther and A.S. Tay, (1998), Evaluating Density Forecasts with Applications to Finance and Management, International Economic Review, 39, 863-883. Diebold, F.X., J. Hahn and A.S. Tay, (1999), Multivariate Density Forecast Evaluation and Calibration in Financial Risk Management: High Frequency Returns on Foreign Exchange, Review of Economics and Statistics, 81, 661-673. Diebold, F.X., and R.S. Mariano, (1995), Comparing Predictive Accuracy, Journal of Business and Economic Statistics, 13, 253-263. Duffie, D., and J. Pan, (1997), An Overview of Value at Risk, Journal of Derivatives, 4, 7-49. Eberlain, E., (1986), On Strong Invariance Principles under Dependence Assumptions, Annals of Probability, 14, 260-270. Fernandez-Villaverde, J., and J.F. Rubio-Ramirez, (2001), Comparing Dynamic Equilibrium Models to Data, Working Paper, University of Pennsylvania. Gallant, A.R. and H. White, (1988), A Unified Theory of Estimation and Inference for Nonlinear Dynamic Models, Blackwell, Oxford. Giacomini, R. (2002), Comparing Density Forecasts via Weighted Likelihood Ratio Tests: Asymptotic and Bootstrap Methods, Working Paper, University of California, San Diego. Giacomini, R., and H. White, (2003), Conditional Tests for Predictive Ability, Working Paper, University of California, San Diego. Goncalves, S., and H. White, (2003), Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models, Journal of Econometrics, forthcoming. Hall, P., and J.L. Horowitz, (1996), Bootstrap Critical Values for Tests Based on Generalized Method of Moments Estimators, Econometrica, 64, 891-916. Hansen, P.R., (2001), An Unbiased and Powerful Test for Superior Predictive Ability, Working Paper, Brown University. Hong, Y., (2001), Evaluation of Out of Sample Probability Density Forecasts with Applications to S&P 500 Stock Prices, Working Paper, Cornell University. Hong, Y.M., and H. Li, (2003) Nonparametric Specification Testing for Continuous Time Models with Applications to Term Structure Interest Rates, Review of Financial Studies, forthcoming. Inoue, A., and M. Shintani, (2003), Bootstrapping GMM Estimators for Time Series, Mimeo, Journal of Econometrics, forthcoming. Kitamura, Y., (2002), Econometric Comparisons of Conditional Models, Working Paper, University of Pennsylvania. K¨unsch H.R., (1989), The Jackknife and the Bootstrap for General Stationary Observations, Annals of Statistics, 17, 1217-1241. Linton, O., E. Maasoumi and Y.J. Whang, (2003), Consistent Testing for Stochastic Dominance Under General Sampling Schemes, Manuscript, LSE, Southern Methodist University and Ewha University. Marcellino, M., J. Stock and M. Watson, (2004), A Comparison of Direct and Iterated AR Methods for Forecasting Macroeconomic Series h-Steps Ahead, Working Paper, Princeton University. Pesaran M. H., and A. Timmerman, (2003), How Costly is to Ignore Breaks when Forecasting the Direction of a Time Series? International Journal of Forecasting, forthcoming. Politis, D.N., J.P. Romano and M. Wolf, (1999), Subsampling, Springer and Verlag, New York. Sch¨orfheide, F., (2000), Loss Function Based Evaluation of DSGE Models, Journal of Applied Econometrics, 15, 645-670. West, K.D., (1996), Asymptotic Inference About Predictive Ability, Econometrica, 64, 1067-1084. West, K.D., and M.W. McCracken, (1998), Regression-Based Tests for Predictive Ability, International Economic Review, 39, 817-840. White, H., (2000), A Reality Check for Data Snooping, Econometrica, 68, 1097-1126. Wooldridge, J.M. and H. White, (1988), Some Invariance Principles and Central Limit Theorems for Dependent and Heterogeneous Processes, Econometric Theory, 4, 210-230.
URI:	http://wrap.warwick.ac.uk/id/eprint/1784

Request changes to a record

Actions (login required)

View Item