This paper analyzes wage decomposition methodology in the context of panel data sample selection embedded in a correlated random effects setting. Identification issues unique to panel data are examined for their implications for wage decompositions. As an empirical example, we apply our...

We develop a new class of techniques that takes a copula function and quantifies the dependence properties through a localized coefficient of dependence in the state space. Effectively we develop a numerical procedure to map any copula function to a generalized Gaussian copula function. This...

This paper analyzes wage decomposition methodology in the context of panel data sample selection embedded in a correlated random effects setting. Identification issues unique to panel data are examined for their implications for wage decompositions. As an empirical example, we apply our...

Multilevel models are widely used in education and social science research. However, the effects of omitting levels of the hierarchy on the variance decomposition and the clustering effects have not been well documented. This paper discusses how omitting one level in three-level models affects...

The method of predicting events based on correlation mapping and Fourier decomposition of relevant time series, which is presented in this paper, is intended for the predictive calculation of signs that an event will occur. As input data for calculating the forecast, in addition to the series...

Multilevel models are widely used in education and social science research. However, the effects of omitting levels of the hierarchy on the variance decomposition and the clustering effects have not been well documented. This paper discusses how omitting one level in three-level models affects...