![valor do stata 12 valor do stata 12](https://i.ytimg.com/vi/5-tOCLkDW78/maxresdefault.jpg)
However, the specification-searching problem remains relevant even when we do not consider this specification. We show that the specification that uses the average pre-treatment outcome values to estimate the weights performed particularly bad in our simulations. With 230 pre-intervention periods, this probability is still around 10% (18% for a 10% significance test). We find that this probability can be as high as 13% (23% for a 10% significance test) when there are 12 pre-intervention periods and decay slowly with the number of pre-intervention periods. Considering six alternative specifications commonly used in SC applications, we calculate in Monte Carlo simulations the probability of finding a statistically significant result at 5% in at least one specification. We show that such lack of specific guidances provides significant opportunities for the researcher to search for specifications with statistically significant results, undermining one of the main advantages of the method. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. (2015) argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment effects in comparative case studies. Won the Prize of Best Econometric Article presented at the 42nd Meeting of the Brazilian Econometric Society (2020). Presented at the 2019 Bristol Econometrics Study Group and the 42nd Meeting of the Brazilian Econometric Society (2020). We propose estimators for the bounds derived and use data made available by Deb, Munkin, and Trivedi (2006) to empirically illustrate the usefulness of our approach. The results rely on a mixture reformulation of the problem where the mixture weights are identified, extending Lee's (2009) trimming procedure to the MTE context.
![valor do stata 12 valor do stata 12](https://sciexperts.com/wp-content/uploads/2020/07/2.jpg)
Our analysis extends to discrete instruments.
![valor do stata 12 valor do stata 12](https://phantichstata.com/wp-content/uploads/2017/06/rowmean-2.png)
Finally, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. The second set of conditions imposes monotonicity of the sample selection variable with respect to treatment, considerably shrinking the identified set. The first result imposes standard MTE assumptions with an unrestricted sample selection mechanism. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and derive uniformly sharp bounds on this parameter under three increasingly restrictive sets of assumptions. This article presents identification results for the marginal treatment effect (MTE) when there is sample selection.