A Markovian Local Resampling Scheme For Nonparametric Estimators In Time Series Analysis
AbstractIn this paper we study the properties of a pth-order Markovian local resampling procedure in approximating the distribution of nonparametric (kernel) estimators of the conditional expectation m(x; ). Under certain regularity conditions, asymptotic validity of the proposed resampling scheme is established for a class of stochastic processes that is broader than the class of stationary Markov processes. Some simulations illustrate the finite sample performance of the proposed resampling procedure.
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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 17 (2001)
Issue (Month): 03 (June)
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- Graflund, Andreas, 2001. "Empirical Probability Distributions of Real Return from Swedish Stock and Bond Portfolios," Working Papers 2001:16, Lund University, Department of Economics, revised 30 Jan 2002.
- Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
- Beare, Brendan K. & Seo, Juwon, 2012. "Time irreversible copula-based Markov Models," University of California at San Diego, Economics Working Paper Series qt31f8500p, Department of Economics, UC San Diego.
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