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Nearest Neighbor Conditional Estimation for Harris Recurrent Markov Chains


Type

Working Paper

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Authors

Sancetta, Alessio 

Abstract

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.

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Keywords

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

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Publisher

Faculty of Economics

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