convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML … estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the … those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the …

convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML … estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the … the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias …

the bias is larger for a more persistent factor. In such a case, bootstrap procedures are effective in reducing the bias … and bootstrap confidence intervals outperform naive asymptotic confidence intervals in terms of controlling the coverage …

for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations … autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on … stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions …

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models … with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also … the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly …

the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on … bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of …