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 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...

Weights are found for weighted least squares estimates such that a selected coefficient (a) changes by one standard deviation or (b) changes in sign. The length of the vector of weight changes is equal to the usual OLS standard error divided by the White-corrected standard errors. Thus the...

We consider the sensitivity of the Tobit estimator to heteroscedasticity. Our single independent variable is a dummy variable whose coefficient is a difference between group means, and the error variance differs between groups. Heteroscedasticity biases the Tobit estimate of the two means in...