Asymptotic expansions are employed in a dynamic regression model with a unit root inorder to find approximations for the bias, the variance and for the mean squared error of theleast-squares estimator of all coefficients. It is found that in this particular context suchexpansions exist only when...

In dynamic regression models conditional maximum likelihood (least-squares) coefficient and variance estimators are biased. Using expansion techniques an approximation is obtained to the bias in variance estimation yielding a bias corrected variance estimator. This is achieved for both the...

The small sample bias of the least-squares coefficient estimator is examined in the dynamic multiple linear regression model with normally distributed whitenoise disturbances and an arbitrary number of regressors which are all exogenous except for the one-period lagged-dependent variable. We...

An approximation to order T−2 is obtained for the bias of the full vector of least-squares estimates obtained from a sample of size T in general stable but not necessarily stationary ARX(1) models with normal disturbances. This yields generalizations, allowing for various forms of initial...

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 of all coefficients. It is found that in this particular context such expansions exist only...

This discussion paper led to a publication in <A href="http://onlinelibrary.wiley.com/doi/10.1111/j.1368-423X.2005.00156.x/abstract;jsessionid=146255EE7C2E41B3C91E06CCA08C53C7.d01t01?systemMessage=Wiley+Online+Library+will+be+disrupted+on+25+August+from+13%3A00-15%3A00+BST+%2808%3A00-10%3A00+EDT%29+for+essential+maintenance">'The Econometrics Journal'</A>.<P>Asymptotic expansions are employed in a dynamic regression model with a unit root inorder to find approximations for the bias, the variance and for the mean squared error of theleast-squares estimator of all coefficients....</p></a>