This paper reviews the literature on Bartlett and Bartlett-type corrections. It focuses on the corrections to the likelihood ratio, score and Wald test statistics. Three different Bartlett-type corrections which are equivalent to order 1/n, n being the sample size, are compared through...

Under suitable regularity conditions, an improved score test was derived by Cordeiro and Ferrari (1991). The test is based on a corrected score statistic which has a chi-squared distribution to order 1/n under the null hypothesis, where n is the sample size. In this paper we follow their...

In this paper we analyze the asymptotic properties of the popular distribution tail index estimator by B. Hill (1975) for possibly heavy- tailed, heterogenous, dependent processes. We prove the Hill estimator is weakly consistent for processes with extremes that form mixingale sequences, and...

In this paper, we develop a parametric test procedure for multiple horizon "Granger" causality and apply the procedure to the well established problem of determining causal patterns in aggregate monthly U.S. money and output. As opposed to most papers in the parametric causality literature, we...

In this paper we derive a general closed-form expression for the Bartlett correction for the test of H_0: \theta= \theta**(0), where "theta is a scalar parameter of a one-parameter exponential family model. Our results are general enough to cover many important and commonly used distributions....

In this paper we derive second and third order bias-corrected maximum likelihood estimates in general uniparametric models. We compare the corrected estimates and the usual maximum likelihood estimate in terms of their mean squared errors. We also obtain closed-form expressions for...

The universal method for testing linearity against smooth transition autoregressive (STAR) alternatives is the linearization of the STAR model around the null nuisance parameter value, and performing F-tests on polynomial regressions in the spirit of the RESET test. Polynomial regressors,...