Hello I'm new in this mail list.
Please I have a doubt about the matchIt outcomes. Below you find a
description of my doubt, I will be very grateful if you could help me.
Lastly, how can I save a MatchiT exit like this (match_model) in my hard
disk .
match_model <-
matchit(IO~AGE+SIZE2008+EBTA2008+R_LTEB2008+R_TASH2008+R_EBOP2008+R_CLTA2008,
data = data123.psm_nm, method = "nearest",
distance = "mahalanobis", discard = "both", ratio=1, caliper=0.25)
Thank you so much.
JSarria.
---------- Forwarded message ---------
De: JESUS HERNANDO SARRIA PEDROZA <jsarria(a)ucm.es>
Date: jue., 26 mar. 2020 a las 14:17
Subject: Re: Doubt MatchIt function
To: King, Gary <king(a)harvard.edu>
Thank you Dr. King.
The attached dataset contains information about financial variables like
leverage, size, age, Ebitda Margin, Debt Quality, and the variable IO that
identify Treat=1 and control= 0.
DD_OPRE0812 is the income (diff in diff) variable between (2008-2012) in
percent to measure the impact of treat (soft-loan). And MN_OPRE0608 is the
average of income between 2006 and 2008 to test parallel path to apply Diff
in Diff. The treatment year is 2009.
I Run "lm" model (in the attached R-Script) with IO variable and
covariables used in the matching pre-proces to calculate the impact. The
coefficient of IO will be the impact, like you do in the paper
MatchingFrontier: Automated Matching for Causal Inference in conclusion
section
To check outcomes please run step 2 and 3 twice and compare output. These
are different.
In section 4 there are some outcomes that I ran.
Pd: Please update the path to call the dataset.
Thank you so much Dr. King.
By last, I used Matching Frontier with this dataset but i don't get good
balance for this reason I´m try with mahalanobis distance.
Thanks again Dr. King. I will be attentive to your requirements.
Best regards.
JSarria.
El jue., 26 mar. 2020 a las 12:31, Gary King (<thegaryking(a)gmail.com>)
escribió:
> Hi Jesus, thanks for your note. Why don't you send a note to the matching
> email list with an explanation of exactly what you ran, perhaps with some
> code, and what you are seeing, and we or someone will help you figure
> it out.
> Best,
> Gary
> --
> *Gary King* - Albert J. Weatherhead III University Professor - Director,
> IQSS <http://iq.harvard.edu/> - Harvard University
> GaryKing.org <http://garyking.org/> - King(a)Harvard.edu - @KingGary
> <https://twitter.com/kinggary> - 617-500-7570 - Assistant
> <king-assist(a)iq.harvard.edu>: 617-495-9271
>
>
> On Thu, Mar 26, 2020 at 5:38 AM JESUS HERNANDO SARRIA PEDROZA <
> jsarria(a)ucm.es> wrote:
>
>> Dear Dr. King, I hope you are healthy and managing well despite the
>> situation.
>>
>> I was in contact with you some months ago, Please can you help me with
>> some doubt? Is about Matchit function indeed about Mahalanobis distance
>> option. What is the reason why in every calculate of the matchit function
>> yield different outcomes, I have read the documents about MatchIt, but only
>> said *"We reestimate the matching procedure until we achieve the best
>> balance possible. The running examples here are meant merely to illustrate,
>> not to suggest that we've achieved the best balance*". ¿Why this occur?
>> Because when I use parametric analysis after matching pre-process, the
>> parameters are different. So How can I contrast when I get the best
>> balance. There is some function in the package or maybe i can do that with
>> some loop.
>>
>> By last I have ordered my dataset the first 5000 rows are the control and
>> the last 300 are treated. This has an influence on the outcome or need to
>> be in randomly order
>>
>> Thank you so much.
>>
>> Jesús S.
>>
>
