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Stochastic Regression Model with Dependent Disturbances
Article first published online: 21 DEC 2001
DOI: 10.1111/1467-9892.00218
Blackwell Publishers Ltd 2001
1467-9892/asset/cover.gif?v=1&s=eec782011cf3959dc4aa59555e8f84f3d4459106 "Journal of Time Series Analysis")
Additional Information
How to Cite
Choy, K. and Taniguchi, M. (2001), Stochastic Regression Model with Dependent Disturbances. Journal of Time Series Analysis, 22: 175–196. doi: 10.1111/1467-9892.00218
Publication History
- Issue published online: 21 DEC 2001
- Article first published online: 21 DEC 2001
- Abstract
- Cited By
Keywords:
- Stochastic regression model;
- short-memory process;
- long-memory process;
- best linear unbiased estimator (BLUE);
- least squares estimator (LSE);
- ratio estimator (RE);
- spectral density;
- stationary linear process
In this paper, we consider the estimation of the coefficient of a stochastic regression model whose explanatory variables and disturbances are permitted to exhibit short-memory or long-memory dependence. Three estimators of the coefficient are proposed. A variety of their asymptotics are illuminated under various assumptions on the explanatory variables and the disturbances. Numerical studies of the theoretical results are given. They show some unexpected aspects of the asymptotics of the three estimators.