A semiparametric bivariate fractionally cointegrated system is considered, integrationorders possibly being unknown and I (0) unobservable inputs having nonparametricspectral density. Two kinds of estimate of the cointegrating parameter ? are considered,one involving inverse spectral weighting...

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and marginal density estimates. We point out the different implications of leading...

This paper develops methods of investigating the existence and extent of cointegration in fractionally integrated systems. We focus on stationary series, with some discussion of extension to nonstationarity. The setting is semiparametric, so that modelling is effectively confined to a...

Smoothed nonparametric kernel spectral density estimates areconsidered for stationary data observed on a d-dimensional lattice.The implications for edge effect bias of the choice of kernel andbandwidth are considered. Under some circumstances the bias canbe dominated by the edge effect. We show...

We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be relevant in small or moderate-sized samples, especially as (depending on...

We consider statistical inference in the presence of serial dependence. The main focus is on use of statistics that are constructed as if no dependence were believed present, and are asymptotically normal in the presence of dependence. Typically the variance in the limit distribution is affected...

Recently proposed tests for unit root and other nonstationarity of Robinson (1994a) are applied to an extended version of the data set used by Nelson and Plosser (1982). Unusually, the tests are efficient (against appropriate parametric alternatives), the null can be any member of the I(d)...

We consider cross-sectional data that exhibit no spatial correla-tion, but are feared to be spatially dependent. We demonstrate that a spatialversion of the stochastic volatility model of financial econometrics, entailing aform of spatial autoregression, can explain such behaviour. The...

We provide a general class of tests for correlation in time series, spatial, spatiotemporaland cross-sectional data. We motivate our focus by reviewing howcomputational and theoretical difficulties of point estimation mount as one movesfrom regularly-spaced time series data, through forms of...

Disregarding spatial dependence can invalidate methods for analyzingcross-sectional and panel data. We discuss ongoing work on developingmethods that allow for, test for, or estimate, spatial dependence. Muchof the stress is on nonparametric and semiparametric methods.