Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains
AbstractThis paper is concerned with consistent nearest neighbor time series estimation for data generated by a Harris recurrent Markov chain. The goal is to validate nearest neighbor estimation in this general time series context, using simple and weak conditions. The framework considered covers, in a unified manner, a wide variety of statistical quantities, e.g. autoregression function, conditional quantiles, conditional tail estimators and, more generally, extremum estimators. The focus is theoretical, but examples are given to highlight applications.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0735.
Date of creation: Jul 2007
Date of revision:
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Web page: http://www.econ.cam.ac.uk/index.htm
Nonparametric Estimation; Quantile Estimation; Semiparametric Estimation; Sequential Forecasting; Tail Estimation; Time Series.;
Other versions of this item:
- Sancetta, Alessio, 2009. "Nearest neighbor conditional estimation for Harris recurrent Markov chains," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2224-2236, November.
- NEP-ALL-2007-08-14 (All new papers)
- NEP-ECM-2007-08-14 (Econometrics)
- NEP-FOR-2007-08-14 (Forecasting)
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