Conditional minimum volume predictive regions for stochastic processes

Wolfgang Polonik and Qiwei Yao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Motivated by interval/region prediction in nonlinear time series, we propose a minimum volume predictor (MV-predictor) for a strictly stationary process. The MV-predictor varies with respect to the current position in the state space and has the minimum Lebesgue measure among all regions with the nominal coverage probability. We have established consistency, convergence rates, and asymptotic normality for both coverage probability and Lebesgue measure of the estimated MV-predictor under the assumption that the observations are taken from a strong mixing process. Applications with both real and simulated data sets illustrate the proposed methods.

Keywords: Conditional distribution; level set; minimum volume predictor; Nadaraya-Watson estimator; nonlinear time series; predictor; strong mixing. (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2000-06
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Citations: View citations in EconPapers (22)

Published in Journal of the American Statistical Association, June, 2000, 95(450), pp. 509-519. ISSN: 0162-1459

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