|
| | | | |
| | | |
| Stat |
Members: 3669
Articles: 2'599'751
Articles rated: 2609
24 March 2025 |
|
| | | | |
|
Article overview
| |
|
|
Bounds on Treatment Effects under Stochastic Monotonicity Assumption in Sample Selection Models | | Yuta Okamoto
; | | Date: |
1 Nov 2023 | | Abstract: | This paper discusses the partial identification of treatment effects in
sample selection models when the exclusion restriction fails and the
monotonicity assumption in the selection effect does not hold exactly, both of
which are key challenges in applying the existing methodologies. Our approach
builds on Lee’s (2009) procedure, who considers partial identification under
the monotonicity assumption, but we assume only a stochastic (and weaker)
version of monotonicity, which depends on a prespecified parameter $vartheta$
that represents researchers’ belief in the plausibility of the monotonicity.
Under this assumption, we show that we can still obtain useful bounds even when
the monotonic behavioral model does not strictly hold. Our procedure is useful
when empirical researchers anticipate that a small fraction of the population
will not behave monotonically in selection; it can also be an effective tool
for performing sensitivity analysis or examining the identification power of
the monotonicity assumption. Our procedure is easily extendable to other
related settings; we also provide the identification result of the marginal
treatment effects setting as an important application. Moreover, we show that
the bounds can still be obtained even in the absence of the knowledge of
$vartheta$ under the semiparametric models that nest the classical probit and
logit selection models. | | Source: | arXiv, 2311.00439 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER