We develop a regression decomposition technique for hazard rate models, where the difference in observed rates is decomposed into components attributable to group differences in characteristics and group differences in effects. The baseline hazard is specified using a piecewise constant...

Authors often add covariates to a base model sequentially either to test a particular coefficient's “robustness” or to account for the “effects” on this coefficient of adding covariates. This is problematic, due to sequence-sensitivity when added covariates are intercorrelated. Using the...

We develop a regression decomposition technique for hazard rate models, where the difference in observed rates is decomposed into components attributable to group differences in characteristics and group differences in effects. The baseline hazard is specified using a piecewise constant...

We provide an axiomatization-based justification for applying the Owen value to decompose R2 in OLS models if prior knowledge can be used to form groups of regressor variables. The assumptions made by the axioms are not only plausible with respect to the variables but also clarify the meaning of...

In this paper, a general Blinder-Oaxaca decomposition is derived that can also be applied to non-linear models, which allows the differences in a non-linear outcome variable between two groups to be decomposed into a part that is explained by differences in observed characteristics and a part...

Volatility is the feature in the India fiber market. By using the granger’s causality test, the study inferred that there was a unidirectional causality between WPI values of jute and cotton and WPI values of silk and cotton at an all India level. By carrying out the co-integration test the...