This paper deals with higher order asymptotic properties for three indirect inference estimators. We provide conditions that ensure the validity of locally uniform Edgeworth approximations. When these are of sufficiently high order they also form integrability conditions that validate locally...

This extended appendix contains detailed proofs for the results in the paper "On the Validity of Edgeworth Expansions and Moment Approximations for Three Indirect Estimators".

Purpose The purpose of this paper is to investigate the impact of bias error resulted from using Monte Carlo simulation in evaluating the American-style option value. Design/methodology/approach The authors develop an analytical approximation formula to quantify the bias error under the...

This article describes a new Stata routine, xtlsdvc, that computes bias-corrected least-squares dummy variable (LSDV) estimators and their boot- strap variance-covariance matrix for dynamic (possibly) unbalanced panel-data models with strictly exogenous regressors. A Monte Carlo analysis is...

This study describes a new Stata routine that computes bias-corrected LSDV estimators and thier bootstrap variance-covariance matrix for dynamic (possibly) unbalanced panel data models. A Monte Carlo analysis is carried out to evaluate the finite-sample performance of the bias corrected LSDV...

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...

This paper extends the LSDV bias approximations in Bun and Kiviet (2003) to unbalanced panels. The approximations are obtained by modify-ing the within operator to accommodate the dynamic selection rule. They are accurate, with higher order terms bringing only decreasing improve-ments to the...