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Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains


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Sancetta, Alessio 


This 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.



Nonparametric Estimation, Quantile Estimation, Semiparametric Estimation, Sequential Forecasting, Tail Estimation, Time Series

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Faculty of Economics

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