Estimating Time Series Regression Using Integrated Likelihood Function
| Autor: | Lin Zhe Jie, 林哲頡 |
|---|---|
| Rok vydání: | 2013 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 101 The time series regression provides an explicit analysis, in which one time series (dependent variable) can be expressed linearly related to other time series variables (covariates), and often errors of the model are possibly correlated or simply white noises. The method of least squares is a naive approach to estimate the regression conditioned on the covariates. When the covariates are non-Gaussian stochastic time series, the least square estimators may not be quite efficient. We propose a new method taking into account the distribution properties. We estimate the parameters by maximizing the unconditional likelihood, which is obtained via convolution. The calculation of multi-fold convolution is insurmountable, so we approximate the unconditional likelihood using Monte Carlo, in which covariates are re-sampled and only selected probability weights are counted into the approximation. The maximum likelihood estimation is obtained applying the Newton-Raphson iterations on the approximated likelihood function. Simulation examples are given and the results are compared to the least squares estimates. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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