This paper deals with properties of three indirect estimators that are known to be (first order) asymptotically equivalent. Specifically, we examine a) the issue of validity of the formal Edgeworth expansion of an arbitrary order. b) Given a), we are concerned with valid moment approximations...

Extending the results in Sargan (1976) and Tanaka (1984), we derive the asymptotic expansions, of the Edgeworth and Nagar type, of the MM and QML estimators of the 1^{st} order autocorrelation and the MA parameter for the MA(1) model. It turns out that the asymptotic properties of the estimators...

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".

In this paper we are concerned with the issue of the existence of locally uniform Edgeworth expansions for the distributions of random vectors. Our motivation resides on the fact that this could enable subsequent uniform approximations of analogous moments and their derivatives. We derive...

This is an extended appendix for the revision of the paper Stochastic Expansions and Moment Approximations for Three Indirect Estimators.

In this paper we are concerned with the issue of the existence of locally uniform Edgeworth expansions for the distributions of parameterized random vectors. Our motivation resides on the fact that this could enable subsequent polynomial asymptotic expansions of moments. These could be useful...

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...

In this paper we define a set of Indirect Inference estimators based on moment approximations of the auxiliary ones. Their introduction is motivated by reasons of analytical and computational facilitation. Their definition provides an indirect inference framework for some "classical" bias...

n this paper we derive the bias approximations of the Maximum Likelihood (ML) and Quasi-Maximum Likelihood (QML) Estimators of the EGARCH(1,1) parameters and we check our theoretical results through simulations. With the approximate bias expressions up to O(1/T), we are then able to correct the...