A new method is proposed for estimating linear triangular models, where identification results from the structural errors following a bivariate and diagonal GARCH(1,1) process. The associated estimator is a GMM estimator shown to have the usual √T-asymptotics. A Monte Carlo study of the...

A particularly useful approach for analyzing pooled cross sectional and time series data is Swamy's random coefficient panel data (RCPD) model. This paper examines the performance of Swamy's estimators and tests associated with this model by using Monte Carlo simulation. The Monte Carlo study...

Grandmont (1985) found that the parameter space of the most classical dynamic models are stratified into an infinite number of subsets supporting an infinite number of different kinds of dynamics, from monotonic stability at one extreme to chaos at the other extreme, and with all forms of...

The hedge fund represents a unique investment opportunity for the institutional and private investors in the diffusion-type financial systems. The main objective of this condensed article is to research the hedge fund’s optimal investment portfolio strategies selection in the global capital...

In a multilingual context, this study investigates the role of language skills on mathematics achievement. It compares characteristics of 5048 Grade-6 learners in 275 Namibian schools. The outcome variable is the standardized SACMEQ mathematics score collected in year 2000. Hierarchical linear...

The diagonal GARCH(1,1) model is shown to support identification of the triangular system and is argued as a second moment analog to traditional exclusion restrictions. Estimators for this result include QML and GMM. The GMM estimator contains many (potential weak) moment conditions that can be...

In this paper we show that the exchange rates of some commodity exporter countries have the ability to predict the price of spot and future contracts of aluminum. This is shown with both in-sample and out-of-sample analyses. The theoretical underpinning of these results relies on the...

We introduce an estimation method that applies to a class of multivariate regression problems. The method can estimate parameters that are subject to multiple reduced-rank conditions and other parameter restrictions and the method allows for a general specifications of the covariance matrix. We...

Modeling policy effects in the context of high-dimensional data requires a balanced consideration of omitted interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing estimates and demonstrates the possible regularization bias...

A correlation between regressors and disturbances presents challenging problems in linear regression. Issues like omitted variables, measurement error and simultaneity render ordinary least squares (OLS) biased and inconsistent. In the context of heteroscedastic linear regression, this note...