Panel sample selection model with interactive effects

We consider a two-step estimation procedure to estimate the panel sample selection models with interactive effects. In the first step, we follow the Robinson (1988) procedure to remove the sample selection factors. In the second step, we control the interactive effects. When the cross-section dimension <i>N</i> is large, we propose to use the Pesaran (2006) common correlated effects approach, and when the time series dimension <i>T</i> is large and <i>N</i> is finite we propose to follow the Hsiao, Shi, and Zhou (2022) transformed estimation procedure to eliminate the interactive effects. We show that the resulting estimators are consistent and asymptotically normally distributed. A limited Monte Carlo study is conducted, showing our methods appear to work well in a finite sample. An empirical illustration on female wage rate determination shows that an extra year of work experience could raise the expected log wage rate by 0.1507 under our maintained hypothesis, while neglecting sample selection or interactive effects could lead to seriously biased estimates.