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Least squares estimation of the linear model with autoregressive errors.
| Content Provider | CiteSeerX |
|---|---|
| Author | Nagaraj, Neerchal K. Fuller, Wayne A. |
| Abstract | . A Monte Carlo study of the least squares estimator of the regression model with autocorrelated errors is presented. The model contains a stationary explanatory variable and a random walk explanatory variable. The error model is a first order autoregressive model and the unit root case is included in the simulations. The limiting distribution of the regression pivotals for the basic model are normal, while the statistics for the autoregressive coefficient have a distribution that depends on the true parameter. The agreement between the Monte Carlo results and the asymptotic theory depends upon the autoregressive coefficient and on the nature of the explanatory variable. Key words. Least squares, nonlinear estimation, Monte Carlo, time series. AMS(MOS) subject classifications. Primary 62M10; secondary 62J02, 62F12. 1. Introduction. The regression model with autocorrelated errors is a natural model to use in many situations where the regression variables are observed over time. The ba... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Autoregressive Error Least Square Estimation Linear Model Regression Model Autocorrelated Error Autoregressive Coefficient Regression Variable Monte Carlo Subject Classification Monte Carlo Study Random Walk Explanatory Asymptotic Theory Least Square Stationary Explanatory Basic Model First Order Autoregressive Model True Parameter Many Situation Monte Carlo Result Nonlinear Estimation Unit Root Case Square Estimator Natural Model Time Series Error Model Regression Pivotals |
| Content Type | Text |
