We provide an asymptotic distribution theory for a class of Generalized Method of Moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: the...

We provide a proof of the consistency and asymptotic normality of the estimator suggested by Heckman (1990) for the intercept of a semiparametrically estimated sample selection model. The estimator is based on 'identification at infinity' which leads to non-standard convergence rate. Andrews and...

We analyze empirically the coalition formation of local governments using a novel reduced form econometric procedure that allows for multi-partner mergers. Using Finnish municipal merger data where mergers were decided independently at the local level, we find that merger decisions are largely...

The links between unionisation and job satisfaction remain controversial. In keeping with the existing literature we find strong statistically significant negative correlations between unionisation and overall job satisfaction. However, in contrast to the previous literature we find that once...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...

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...

Inequality measures are often used to summarise information about empirical income distributions. However , the resulting picture of the distribution and of the changes in the distribution can be severely distorted if the data are contaminated. The nature of this distortion will in general...

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...

In this paper, I illustrate the additional information that can be prodivided in estimating the lower bound (Sutton 1991, 1998) by using quantile regression. Quantile regression allows us to invesigate the influence of outliers. Previous lower bound have been performed using the simplex method....