Asymptotic expansions are employed in a dynamic regression model with a unit root in order to find approximations for the bias, the variance and for the mean squared error of the least-squares estimator. For this purpose such expansions are shown to be useful only when the autoregressive model contains at least one non-redundant exogenous explanatory variable. It is found that large sample and small disturbance asymptotic techniques give closely related results in this model, which is not the case in stable dynamic regression models. The results are specialised to the random walk with drift model, where it is seen that the ratio of the standard deviation of the disturbance tot he drift term plays a crucial role. The random walk to the model with drift plus a linear trend is also examined. The accuracy of the approximations are checked in the context of these models making use of a set of Monte Carlo experiments to estimate the true moments.
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Paper provided by University of Exeter, School of Business and Economics in its series Discussion Papers with number
99/09.
Length: 30 pages Date of creation: 1998 Date of revision: Handle: RePEc:fth:exetec:99/09
Contact details of provider: Postal: School of Business and Economics University of Exeter Streatham Court Rennes Drive Exeter EX4 4PU Phone: (01392) 263218 Fax: (01392) 263242 Web page: http://www.exeter.ac.uk/sobe/ More information through EDIRC
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
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