<p>We provide a general class of tests for correlation in time series, spatial, spatio-temporal and cross-sectional data. We motivate our focus by reviewing how computational and theoretical difficulties of point estimation mount as one moves from regularly-spaced time series data, through forms of...</p>

This teaching note reports and evaluates an initiative to enhance first-year accounting students' written communication skills in a large class setting. The procedures adopted are outlined and empirical evidence of the effectiveness of these procedures in enhancing writing skills is provided....

Africa needs power - to grow its economies and enhance the welfare of its people. Power for all is still a long distance away - two thirds of the population remains without electricity and enterprises rank electricity as a top constraint to doing business. This sub-optimal situation coexists...

In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate...

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes additive, unknown, individual-specific components and allows also for cross-sectional and temporal dependence and conditional heteroscedasticity. A simple nonparametric...

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, or when parametric modelling arises in part from a common factor or other...

For testing lack of correlation against spatial autoregressive alternatives, Lagrange multiplier tests enjoy their usual computational advantages, but the (X2) first-order asymptotic approximation to critical values can be poor in small samples. We develop refined tests for lack of spatial error...

Disregarding spatial dependence can invalidate methods for analyzing cross-sectional and panel data. We discuss ongoing work on developing methods that allow for, test for, or estimate, spatial dependence. Much of the stress is on nonparametric and semiparametric methods.