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Dichotomous/polychotomous dependent variable #14
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The method "works" whenever the first-stage model is specified correctly for the outcome variable. For example, if you have an indicator variable as an outcome and you think you've correctly specified the (linear) propensity score model. In general, though, the first-stage model is unlikely to hold |
RIght. So the first stage is estimated usig OLS and not GMM, correct? I assumed the first stage is also estimated using GMM. Then, no distributional assumption is imposed on errors. But it's not the case. Thanks for your response |
Well OLS imposes the assumption of normal dist. on error terms. It helps us to make statistical inference. In OLS, if the errors are normally distributed with mean zero and constant variance, then the OLS estimator is consistent (and efficient). |
hi Saharnaz
I think you have concepts here confused.
1. OLS consistency does not depend on normality of the errors. It does
depend on the zero conditional mean.
2. OLS does not assume variables are continuous. that is why we can use it
for almost all kind of models. Standard errors can always be corrected if
one believes they are not homoskedastic.(robust standard erros for once)
3. MLE Does impose distributional assumptions. Otherwise you cannot use it.
GMM , as OLS, does not impose distributional assumptions. Just conditions
on the Moments.
4. All the commands you mention actually can handle binary variables as
dependent variables, but you need to acknowledge that they use LPM (or
something similar to it).
5. If you want to use something that handles binary variables explicitly
you can use jwdid
jwdid y x1 x2 x3, .... method(logit)
Here, however, the parallel test assumption is not on the observed
probability, but on the latent variable.
F
…On Thu, Apr 13, 2023 at 2:43 PM Saharnaz Babaei-Balderlou < ***@***.***> wrote:
Well OLS imposes the assumption of normal dist. on error terms. It helps
us to make statistical inference. In OLS, if the errors are normally
distributed with mean zero and constant variance, then the OLS estimator is
consistent (and efficient).
Estimating a specification with binary dep. var. leads to predicted values
less than 0 and more than 1. OLS assumes that the outcome var is continuous
and normally distributed. Binary variables are inherently dichotomous and
take only two values. Also, with binary outcome, the variance of errors
will depend on the value of the independent variables, resulting in a
violation of the constant variance assumption. So, the logtistic regression
is suggested. But MLE and GMM do not impose normal dist. assumption.
I am sorry if my question was confusing. I am searching a way to test
pretrends and do an event study for my case where outcome is polychotomous,
data is repeated cross-sections, and treatment is staggered. That is why, I
am searching the literature of DID to be consistent with my scenario. I am
more of an applied economist and have not been successful to master DID
literature yet. After looking at csdid, jwdid, and did2s commands in
stata, I am trying to find out which could be the best for me. csdid is
not suitable for binary outcome.
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Thank you F for your time. |
if jwdid is not converging, is probably because
a) not enough data. You have very few observations per cohort per year
b) too many controls. This will indirectly affect a)
I would definitely need more information to say something about why
jwdid is not working for you.
…On Thu, Apr 13, 2023 at 3:38 PM Saharnaz Babaei-Balderlou < ***@***.***> wrote:
Thank you F for your time.
Maybe I am making a mistake. I will refer to my textbooks regarding how
violation of normality assumption could relate to the inference, hypothesis
testing, consistency and efficiency of the estimators. Probably, I am
confused. Thank you for your explanations. I will check on details.
But binary variable does not have a continuous distribution. We can only
assume it as being continuous for LPM.
jwdid for some reason is not converging and I could not find the reason
for the error yet (possibly something in the way I set it up). In the
meantime working on the error, I tried to check out if there are other
options available.
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Thank you. I am not sure if this (issue of another code) is the right place to send details. Can I email you? |
Agreed on all fronts with @friosavila! |
Can this method (either in stata or R) be applied when the dependent variable is a factor variable (dichotomous/polychotomous)?
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