In many regression applications both the independent and dependent variables are measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different nonparametric techniques, regression splines and kernel...

In this paper we consider the polynomial regression model in the presence of multiplicative measurement error in the predictor. Consistent parameter estimates and their associated standard errors are derived. Two general methods are considered, with the methods differing in their assumptions...

Stuetzle and Mittal (1979) for ordinary nonparametric kernel regression and Kauermann and Tutz (1996) for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative...

We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator, our proposed estimator removes FE using kernel-based weights. This results a one-step estimator without using the...

This paper proposes and implements a mixture model to account for the unobserved group heterogeneity when modeling repayment behavior in group lending. We discuss the model properties and identification. We estimate the model using a rich dataset from a group lending program in India. The...

Group lending has been widely adopted in the past thirty years by many microfinance institutions as a means to mitigate information asymmetries when delivering credit to the poor. This paper proposes an empirical method to address the potential omitted variable problem resulting from unobserved...