The optimal minimum distance (OMD) estimator for models of covariance structure is asymptotically efficient but has much worse finite-sample properties than does the equally-weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample...

This note points out to applied researchers what adjustments are needed to the coefficient estimates in a random effects probit model in order to make valid comparisons in terms of coefficient estimates and marginal effects across different specifications. These adjustments are necessary because...

Extremum estimation is typically an ad hoc semi-parametric estimation procedure which is only justified on the basis of the asymptotic properties of the estimators. For a fixed finite data set, consider a large number of investigations using different extremum estimators to estimate the same...

In this paper we examine educational data whch has a cross-classified structure. A cross-classified value-added multilevel model is proposed for these data and the problem of estimation are discussed in relation to the probllem of an endogenous regressor.

It is well known that the usual techniques for estimating random and fixed effects panel data models are inconsistent in the dynamic setting. As a consequence, numerous consistent estimators have been proposed in the literature. However, all such estimators rely on certain well defined...

The paper derives and tests maximum likelihood parameter estimators for symmetrically correlated Weiner processes observed at discrete intervals. Such processes are arise when pricing and determining Value-at-Risk for portfolio derivatives. Cases of driftless and mean-reverting state variables...