Using small-disturbance expansions, we derive analytic expressions for the bias of the OLS estimator an elasticity in a linear model, both at an individual sample point and at the sample mean. The magnitudes of these biases are illustrated with Australian expenditure data.

We examine the finite sample properties of the maximum likelihood estimator for the binary logit model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error function for the maximum likelihood estimator in this model are derived, and we...

We examine the finite sample properties of the MLE for the Logit model with random covariates. We derive the second order bias and MSE function for the MLE in this model, and undertake some numerical evaluations to illustrate the analytic results. From these numerical results we find, for...

We derive saddlepoint approximations for the density and distribution functions of the half-life estimated by OLS from an AR(1) or AR(p) model. Our analytic results are used to prove that none of the integer-order moments of these half-life estimators exist. This provides an explanation for the...

We examine the small-sample behaviour of the maximum likelihood estimator for the Poisson regression model with random covariates. Analytic expressions for the first-order bias and second-order mean squared error for this estimator are derived, and we undertake some numerical evaluations to...

We consider the relative merits of various saddlepoint approximations for the c.d.f. of a statistic with a possibly non-normal limit distribution. In addition to the usual Lugannani-Rice approximation we also consider approximations based on higher-order expansions, including the case where the...