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2001•Communications for Statistical Applications and MethodsRequires access

Asymmetric Least Squares Estimation for A Nonlinear Time Series Regression Model

Tae Soo Kim, Hae Kyoung Kim, Jin Hee Yoon

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Abstract

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

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What this paper is about

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

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Available abstract

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

Key concepts: Mathematics, Estimator, Nonlinear regression, Statistics, Non-linear least squares, Generalized least squares, Series (stratigraphy), Simple linear regression

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