SOME IDEAS FROM ESWC05

About identification, we can probably look at works by Chescher and Imbens. These are about Triangular equations with endogenous regressor (we can think of R as an endogenous regressor? and put R in the structural equation? if so, we are in the flamework of them). We may be can say why Y has to be discrete in RS(2003)

Xiaohong Chen wrote a paper with Chunrong Ai about efficient sequential estimation of semi-nonparametric moment models which include Newey and Chamberlain's bounds as special cases.

Floren Jean-Pierre, Uni of Toulouse I wrote a paper about Endogeneity in nonseparable midels: Application to Treatment Models where the Outcomes are durations. Very techniqual paper. There is some regularise of irregular estimators.

Han Hong wrote a paper about confident interval of identification regions "Inference on Identified parameter sets"

Francesca Molinari, Cornell, Partial identification of probability disribution with misclassified data. Very interesting. Use Manski idea. Maybe we can use something to solve identification problem in our exrension of RS(2003)

Sergio Firpo, Puc-Rio and UBC, Inequality treatment effacts. See the effect of treatment on the distribution of the targeted variable. Quite interestin to use this idea to measure the effect of introducing NMW (national minimum wage) on the wage distribution.

What is the relationship between selection (missing data) and random censoring model? (see a paper by tamer and Shakeeb Kahn (Rocheter) for this randomly censored regression)

Thomas Stoker (UCL and MIT) works on missing in X. See his website for more details.

Cheti Nicoletti from Essex ISER works on bounds and imputation. Her paper has some issues about using impuation when Z in the impution regression model is not the same as X in the structural model (X is a proper subset of Z). Since, in Skinner (2002), their X is included in Z, this may be an issue.

Does Bhattacharya's paper allow NMAR? but, although he said that his paper started off from Hirano, Imbens,....'s paper on Attrition with IPW, that paper does not allow NMAR??

Tarozzi Chen..'s paper on Semiparametric Efficiency on moment models are very interesting. But they need refreshment samples. Notice that, for IPW estimators, in order to obtain efficiency, they have to weight with both nonparametric and parametric estimated probabilities ( with correct specification). This may mean that results due to Wooldridge (using estimated prob leads to more efficient estimator) carry over to this. However, since all of these estimators attain semiparametric efficiency bound, we cannot say that using estimated probs is more efficient but we can see that they have to use estimated probs to attain such bounds.

Grant Hiller's paper is very interesting. It is about the foundation of estimation procedure.