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

This paper develops a new method for estimating a demand function and the welfare consequences of price changes. The method is applied to gasoline demand in the U.S. and is applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a...

This paper develops a new method for estimating the demand function for gasoline and the deadweight loss due to an increase in the gasoline tax. The method is also applicable to other goods. The method uses shape restrictions derived from economic theory to improve the precision of a...

An overview is presented of some parametric and semi-parametric models, estimators, and specification tests that can be used to analyze ordered response variables. In particular, limited dependent variable models that generalize ordered probit are compared to regression models that generalize...

For vectors x and w, let r(x,w) be a function that can be nonparametrically estimated consistently and asymptotically normally. We provide consistent, asymptotically normal estimators for the functions g and h, where r(x,w) = h[g(x),w], g is linearly homogeneous and h is monotonic in g. This...

This paper develops a specification of the credit scoring model with high discriminatory power to analyze data on loans at the retail banking market. Parametric and non- parametric approaches are employed to produce three models using logistic regression (parametric) and one model using...

We propose a nonparametric method to test which characteristics provide independent information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...

We propose a nonparametric method to study which characteristics provide incremental information for the cross section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...