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About On least-squares bias in the AR(p) models: Bias correction using the bootstrap methods
In the case where the lagged dependent variables are included in the regression model, it is known that the ordinary least squares estimates (OLSE) are biased in small sample and that the bias increases as the number of the irrelevant variables increases. In this paper, based on the bootstrap methods, an attempt is made to obtain the unbiased estimates in autoregressive and non-Gaussian cases. We propose the residual-based bootstrap method in this paper. Some simulation studies are performed to examine whether the proposed estimation procedure works well or not. We obtain the results that it is possible to recover the true parameter values and that the proposed procedure gives us the less biased estimators than OLSE.
Detailed Information
| Author: | Hisashi T., Yoichi M., Shigeyuki H. |
|---|---|
| Publication Year: | 2006 |
| ISBN: | 138108 |
| Pages: | 16 |
| Language: | English |
| File Size: | 0.107 |
| Format: | |
| Price: | FREE |
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