Multicollinearity in empirical data violates the assumption of independence among the explanatory variables in a linear regression model and by inflating the standard error of estimates of the estimated regression coefficients leads to failure in rejecting a false null hypothesis of...

The Two-Stage Least squares method for obtaining the estimated structural coefficients of a simultaneous linear equations model is a celebrated method that uses OLS at the first stage for estimating the reduced form coefficients and obtaining the expected values in the arrays of current...

In this paper we propose a two-step semiparametric procedure to estimate first-price auction models. In the first-step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second-step, we apply the method of moments...

Universal function approximators, such as artificial neural networks, can learn a large variety of target functions arbitrarily well given sufficient training data. This flexibility comes at the cost of the ability to perform parametric inference. We address this gap by proposing a generic...

This short note proposes working out of the standard errors of estimate of composite indices when they are constructed by using intrinsically derived weights. It illustrates the proposed method, using the jackknife re-sampling technique, by an example that relates to crime of different types in...