In this paper we analyse the higher order asymptotic properties of the empirical likelihood ratio test, by means of the dual likelihood theory. It is shown that when the econometric model is just identified, these tests are accurate to an order o(1/n), and this accuracy can always be improved to...

This paper derives two Bartlett-type adjustments that can be used to obtain higher-order improvements to the distribution of the class of empirical discrepancy test statistics recently introduced by Corcoran (1998) as a generalisation of Owen's (1988)empirical likelihood. The corrections are...

This paper analyses the higher order asymptotic behaviour of a profiled empirical likelihood ratio which can be used as a specification test in linear regression models. Despite the presence of nuisance parameters, a simple Bartlett correction factor is obtained and used to improve to third...

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...

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...