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...

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...

We describe and examine a consistent test for the correct specification of a regression function with dependent data. The test is based on the supremum of the difference between the parametric and nonparametric estimates of the regression model. Rather surprisingly, the behaviour of the test...

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic...

Relative wages vary considerably across regions of the United Kingdom, with skill-abundant regions exhibiting lower skill premia than skill-scarce regions. This paper shows that the location of economic activity is correlated with the variation in relative wages. U.K. regions with low skill...

We estimate semiparametric Engel curves for rural Pakistan using a large household survey. This allows us to obtain consistent estimates of the effects of household size and composition on consumption patterns even when these demographic variables are correlated with an unknown function of...