We discuss the relative advantages and disadvantages of four types of convenient estimators of binary choice models when regressors may be endogenous or mismeasured, or when errors are likely to be heteroskedastic. For example, such models arise when treatment is not randomly assigned and outcomes are binary. The estimators we compare are the two stage least squares linear probability model, ma...

This paper is concerned with the development of a new method for the design of Energy Transfer Filters (ETFs). ETFs are a new class of nonlinear filters recently proposed by the authors, which employ nonlinear effects to transfer signal energy from one frequency band to a different frequency location. The new method uses the powerful Orthogonal Least Squares (OLS) algorithm to solve the Least S...

This longitudinal study examined how academic self-efficacy, intrinsic motivation, and participation in an after-school program contributed to the academic achievement of Latino middle school students over the course of one school year. Participants were 47 Latino students in sixth through eighth grades who attended two public middle schools in which an after-school program was held that was sp...

The standard F test for linear restrictions in regression is relevant as a criterion but fails to capture the notion of tradeoff between bias and variance. Average squared distance criteria yield operational tests that are more appropriate, depending upon objectives. In the present paper two alternative criteria are developed. The first allows testing of the hypothesis that the average squared ...

In this article we model production technology in a state-contingent framework. Our model analyzes production under uncertainty without being explicit about the nature of producer risk preferences. In our model producers’ risk preferences are captured by the risk-neutral probabilities they assign to the different states of nature. Using a state-general state-contingent specification of technolo...

This study is about the development of a robust ridge regression estimator. It is based on weighted ridge MM-estimator (WRMM) and is believed to have potentials in remedying the problems of multicollinearity. The proposed method has been compared with several existing estimators, namely ordinary least squares (OLS), robust regression based on MM estimator, ridge regression (RIDGE), weighted rid...

There has been a wide debate on whether democracy actually has an effect on economic outcomes, and especially on international trade. With a new estimation strategy, we analyze this relationship taking a look at the distribution of countries’ trading activity. Using a panel quantile estimation framework from Powell (2014), we find a stronger relationship at the lower quantiles, especially for t...

Regression via ordinary least squares (OLS) is a commonly applied statistical technique in political science. However, when the dependent variable is dichotomous (0-1) rather than continuous, ordinary least squares becomes an inefficient estimation technique, and the underlying linear probability model (LPM) that is being estimated represents a poor apriori choice of model specification (Aldric...

Recent studies have used quantile regression (QR) techniques to estimate the impact of education on the location, scale and shape of the conditional wage distribution. We conduct a similar investigation of the role of work-related training. We utilise both ordinary least squares and QR techniques to estimate associations between work-related training and wages for private sector men in ten Euro...

Each dot represents one company as the intersection of its output (X) and its costs (Y). The solid line (OLS) represents the “Ordinary Least Squares” regression line, which defines the average or expected value of Y (costs) for each value of X (output). It is defined by the intercept, a, and the gradient, b. Without delving into the details of the technique, it amounts to “drawing a line throug...