Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependenceand heterogeneity in explanatory variables and unobservable disturbances are permitted. We discuss...

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well...

Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have a common time trend. The time trend is of unknown form, the model includes additive, unknown, individual-specific components, and we allow for spatial or other cross-sectional dependence...

Many studies have estimated the effect of taxes on taxable income. To account for nonlinear taxes these studies either use instrumental variables approaches that are not fully consistent or impose strong functional form assumptions. None allow for general heterogeneity in preferences. In this...

Given the key role of the taxable income elasticity in designing an optimal tax system there are many studies attempting to estimate this elasticity. To account for nonlinear taxes these studies either use instrumental variables approaches that are not fully consistent, or impose strong...

The taxable income elasticity is a key parameter for predicting the e?ect of tax reform or designing an income tax. Bunching at kinks and notches in a single budget set have been used to estimate the taxable income elasticity. We show that when the heterogeneity distribution is unrestricted the...