An extended least-squares method is described which allows us to model the location and scale of a process parametrically without specifying any parametric form for the distribution of the errors. The degree of the associated polynomials is chosen using a step-down method on a table of p-values....

A robust gradient statistic under model misspecification is proposed and its asymptotic distribution under the null hypothesis is determined. Several examples are presented and Monte Carlo simulation experiments are carried out.

We consider exact unconditional procedures for testing independence of exchangeable binary data with equal-sized clusters. The approximate unconditional approach is recommended as a basic procedure. The exact unconditional procedure based on estimation followed by maximization is recommended for...

Compatibility testing determines whether two series, say a sub-annual and an annual series, both of which are subject to sampling errors, can be considered suitable for benchmarking. We derive statistical tests and discuss the issues with their implementation. The results are illustrated using...

The small-sample performance of alternatives to the usual likelihood ratio test in mixed linear models is investigated. Specifically, the following tests for fixed effects are considered: (i) a bootstrap-based test, (ii) the Bartlett-corrected usual test, and (iii) an adjusted profile likelihood...

We suggest improved tests for cointegration rank in the vector autoregressive (VAR) model and develop asymptotic distribution theory and local power results. The tests are (quasi-)likelihood ratio tests based on a Gaussian likelihood, but as usual the asymptotic results do not require normally...

This paper considers the estimation and the hypothesis testing on the equality of the ratios of the means to standard deviations of several normal populations with difference sample sizes. We propose an iterative algorithm to find the maximum likelihood estimates (MLEs) of the normal population...

We investigate the behavior of various standard and modified F, likelihood ratio (LR), and Lagrange multiplier (LM) tests in linear homoskedastic regressions, adapting an alternative asymptotic framework in which the number of regressors and possibly restrictions grows proportionately to the...