We study the properties of the multi-period-ahead least-squares forecast for the stationary AR(1) model under a general error distribution. We find that the forecast is unbiased up to O(T^(−1)), where T is the in-sample size, regardless of the error distribution and that the mean squared...

I derive the approximate bias and mean squared error of the least squares estimator of the autoregressive coefficient in a stationary first-order dynamic regression model, with or without an intercept, under a general error distribution. It is shown that the effects of nonnormality on the...

The issue of whether the new one-sided procedures for testing inequalities dominate the classical procedure in terms of power remains unresolved in the literature. The Monte Carlo results in this study suggest a theoretical approach that could provide an analytical resolution

We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the...

We derive the approximate results for the bias and mean squared error of a large class of estimators to orders O(n^(-5/2)) and O(n^(-3)), respectively, given a sample of n observations.The results are built on a stochastic expansion of the moment condition used to identify the econometric...

We derive the exact distribution of the maximum likelihood estimator of the mean reversion parameter (k) in the Ornstein-Uhlenbeck process by employing numerical integration via analytical evaluation of a joint characteristic function. Different scenarios are considered: known or unknown drift...

We develop the analytical second-order bias of a Value-at-Risk estimator based on an ARCH(1) volatility specification when the parameters are estimated by the method of quasi maximum likelihood. We show that the bias results from two sources: assumption on the distribution of the standardized...

We investigate the finite sample properties of the maximum likelihood estimator for the spatial autoregressive model. A stochastic expansion of the score function is used to develop the second-order bias and mean squared error of the maximum likelihood estimator. We show that the results can be...

We develop analytical results on the second-order bias and mean squared error of estimators in time-series models. These results provide a unified approach to developing the properties of a large class of estimators in linear and nonlinear time-series models and they are valid for both normal...

There is widespread concern about differences in the quality of state-run and private schooling. The concerns are especially severe in the numerous developing countries where much of the population has left state-provided schooling for private schooling, including many private schools not...