@article {192306,
	title = {Identification of Panel Data Models with Endogenous Censoring},
	journal = {Journal of Econometrics},
	volume = {194},
	number = {1},
	year = {2016},
	month = {2016},
	pages = {57-75},
	abstract = {
	We study inference on parameters in censored panel data models, where the censoring\&nbsp;can depend on both observable and unobservable variables in arbitrary ways.\&nbsp;Under some general conditions, we characterize the information the model and data\&nbsp;contain about the parameters of interest by deriving the identified sets: Every parameter\&nbsp;that belongs to these sets is observationally equivalent to the true parameter -\&nbsp;the one that generated the data . We consider two separate sets of assumptions (2\&nbsp;models): the first uses stationarity on the unobserved disturbance terms. The second\&nbsp;is a nonstationary model with a conditional independence restriction. Based on the\&nbsp;characterizations of the identified sets, we provide a valid inference procedure that\&nbsp;is shown to yield correct confidence sets based on inverting stochastic dominance\&nbsp;tests. Also, we also show how our results extend to empirically interesting dynamic\&nbsp;versions of the model with both lagged observed outcomes, and lagged indicators.\&nbsp;We also show extensions to models with factor loads. In addition, and for both\&nbsp;models, we provide sufficient conditions for point identification in terms of support\&nbsp;conditions. The paper then examines sizes of the identified sets, and a Monte Carlo\&nbsp;exercise shows reasonable small sample performance of our procedures.



	\&nbsp;

},
	url = {http://www.sciencedirect.com/science/article/pii/S0304407616300446},
	author = {Shakeeb Khan and Maria Ponomareva and Elie Tamer}
}
