We develop a framework to assess how successfully standard times eries models explain low-frequency variability of a data series. The low-frequency information is extracted by computing a finite number of weighted averages of the original data, where the weights are low-frequency trigonometric...

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and...

The purpose of this paper is to help empirical economists think through when and how to weight the data used in estimation. We start by distinguishing two purposes of estimation: to estimate population descriptive statistics and to estimate causal effects. In the former type of research,...

We propose and evaluate a technique for instrumental variables estimation of linear models with conditional heteroskedasticity. The technique uses approximating parametric models for the projection of right hand side variables onto the instrument space, and for conditional heteroskedasticity and...

Movements in the prices of different assets are likely to directly influence one another. This paper develops a model that identifies the contemporaneous interactions between asset prices in U.S. financial markets by relying on the heteroskedasticity in their movements. In particular, we...

In this paper, I develop a new identification method to solve the problem of simultaneous equations, based on heteroskedasticity of the structural shocks. I show that if the heteroskedasticity can be described as a two-regime process, then the system is just identified under relatively weak...

We apply a new estimator to the measurement of the economic returns to education. We control for endogenous education, unobserved ability and measurement error using only the natural heteroscedasticty of wages and education attainment. Our prefered estimate, 6.07%, is closer to the OLS estimate...

There are two broad classes of models used to address the econometric problems caused by skewness in data commonly encountered in health care applications: (1) transformation to deal with skewness (e.g., OLS on ln(y)); and (2) alternative weighting approaches based on exponential conditional...