In estimating the effect of an ordered treatment τ on a count response y with an observational data where τ is self-selected (not randomized), observed variables x and unobserved variables ϵ can be unbalanced across the control group (τ = 0) and the treatment groups (τ = 1, …, J). While...

We analyse the dynamic labour participation behaviour of Korean women. State dependence under unobserved heterogeneity is considered, where the heterogeneity may be unrelated, pseudo-related, or arbitrarily related to regressors. Three minor methodological contributions are made: interaction...

The main difficulty in treatment effect analysis with matching is accounting for unobserved differences (i.e., selection problem) between the treatment and control groups, because matching assumes no such differences. The traditional way to tackle the difficulty has been ¡®control function¡¯...

. In addition to these, several interesting practical lessons are noted in doing the two-stage PLR model estimation. First, the cross validation (CV) used in the PLR model literature can fail if the mean-independence is ignored. Second, high order kernels can make the CV criterion function ill...

Difference in differences (DD) relies on the key identifying condition that the untreated response variable would have grown equally across the control and treatment groups; i.e., the ¡®time effects¡¯ across the groups are the same. This condition can be rewritten as the ¡®group...