Journal of applied mathematics & informatics

The Korean Society for Computational and Applied Mathematics (한국전산응용수학회)
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EVALUATION OF PARAMETER ESTIMATION METHODS FOR NONLINEAR TIME SERIES REGRESSION MODELS

  • Kim, Tae-Soo (School of Liberal Arts(Dept. Math), Seoul National University of Technology) ;
  • Ahn, Jung-Ho (School of Computer & Media Engineering, Kangnam University)
  • Published : 2009.01.31
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Abstract

The unknown parameters in regression models are usually estimated by using various existing methods. There are several existing methods, such as the least squares method, which is the most common one, the least absolute deviation method, the regression quantile method, and the asymmetric least squares method. For the nonlinear time series regression models, which do not satisfy the general conditions, we will compare them in two ways: 1) a theoretical comparison in the asymptotic sense and 2) an empirical comparison using Monte Carlo simulation for a small sample size.

Keywords

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