Why do industrial clusters occur in space? Is it because industries need to stay close together to interact or, conversely, because they concentrate in certain portions of space to exploit favourable conditions like public incentives, proximity to communication networks, to big population...

Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory....

Our study aims to measure the effects of spatial R&D spillovers on firms' patent production at the city level. We use an original method to estimate the spatial dimension of spillovers using count data. The method, based on a generalized cross entropy approach, allows us to test spatial...

The aim of this paper is two-fold: (a) to establish a ‘map’ for describing the wide class of Limited Dependent Variables (LDV) univariate and multivariate models in the econometric literature and (b) to localize typical models in this tradition within the structure, extending typologies of...

The most-noted studies on the impact of microcredit on households are based on a survey fielded in Bangladesh in the 1990s. Contradictions among them have produced lasting controversy and confusion. Pitt and Khandker (PK, 1998) apply a quasi-experimental design to 1991–92 data; they conclude...

This paper presents maximum score type estimators for linear, binomial, tobit and truncated regression models. These estimators estimate the normalized vector of slopes and do not provide the estimator of intercept, although it may appear in the model. Strong consistency is proved. In addition,...

This paper presents maximum score type estimators for linear, binomial, tobit and truncated regression models. These estimators estimate the normalized vector of slopes and do not provide the estimator of intercept, although it may appear in the model. Strong consistency is proved. In addition,...

A new approach to limited-dependent variable count data or other model types is considered. Instead of adopting maximum likelihood estimation based on a full distributional assumption or smoothing techniques and semiparametric estimation, the novel idea is to use an approximation to the...

In this paper we provide semiparametric estimation strategies for a sample selection model with a binary dependent variable. To the best of our knowledge, this has not been done before. We propose a control function approach based on two di erent identifying assumptions. This gives rise to...