The efficient bootstrap methodology is developed for overidentified moment conditions models with weakly dependent observation. The resulting bootstrap procedure is shown to be asymptotically valid and can be used to approximate the distributions of t-statistics, the J-statistic for...

The efficient bootstrap methodology is developed for overidentified moment conditions models with weakly dependent observation. The resulting bootstrap procedure is shown to be asymptotically valid and can be used to approximate the distributions of t-statistics, J statistic for overidentifying...

This paper shows how the generalised empirical likelihood method can be used to obtain valid asymptotic inference for the finite dimensional component of semiparametric models defined by a set of moment conditions. The results of the paper are illustrated using three well-known semiparametric...

In this paper we analyse the higher order asymptotic behaviour of a profiled empirical likelihood ratio which can be used to test a set of linear restrictions in linear regression models. We show that the resulting profiled empirical likelihood ratio admits a Bartlett correction which can be...

In this paper we use the empirical Cressie-Read discrepancy function to obtain a class of nonparametric likelihood statistics for smooth functions of means of [alpha]-mixing processes both in the finite- and infinite-dimensional case.

This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test...

This paper compares the second-order power properties of a broad class of nonparametric likelihood tests recently introduced by Baggerly (1998) as a generalisation of Owen's (1988) empirical likelihood. It is shown that in a multi-parameter setting identity of power up to first order does not...

In this paper we obtain a second order Edgeworth approximation to the density of a likelihood ratio type J test for overidentifying restrictions by embedding the moment conditions into the empirical likelihood framework. The resulting asymptotic expansion can be used to correct to an order o...

This paper develops a new test for a unit root in autoregressive models with serially correlated errors. The test is based on the ``empirical'' Cressie-Read statistic and uses a sieve approximation to eliminate the bias in the asymptotic distribution of the test due to presence of serial...