[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Time series and psmatch2

From   Austin Nichols <>
Subject   Re: st: Time series and psmatch2
Date   Tue, 8 Dec 2009 21:54:09 -0500

Joshua Hawley <> :
Maybe we are not speaking the same language exactly, but no, this is
not "creating issues."  If you want to model the propensity of
receiving some treatment in 1990 and you have the earnings history
from 1971 to 1989 for each control and treatment case, you can
condition on each year of earnings by including 19 variables for
earnings in your model (or a bunch of categorical variables measuring
e.g. membership in deciles in each year).  There is no "repeated
measures" problem per se.

There is very good support for propensity scores in Stata; hard to get
much easier than -predict- after -logit- or -probit- (or -egen- for a
nonparametric model of propensity score).

On Tue, Dec 8, 2009 at 9:23 PM, Joshua Hawley <> wrote:
> Thanks for the response.
> I've done that before, as is mentioned in Becker and Ichino. It works fine.
> However, isn't this simply creating issues because of the repeated measures
> over time?
> I guess I was looking for a time series version of the psmatch2. I'm
> wondering if there is one in R? I'm not sure of the mathematical problems
> that the repeated cross sectional strategy presents, but I don't like it
> from a conceptual basis. If you want to model functions over time, it makes
> no sense to assume that the selection of the proper "control group" comes
> from repeated cross sectional measures.
> I wonder if you could do an approximation of a propensity score in HLM, with
> the times nested in individuals?
> Anyone done anything about this in the past? Why don't we have better
> support for propensity scores in Stata?
> Josh
> On 12/8/09 9:00 PM, "Austin Nichols" <> wrote:
>> One way is to -reshape- to wide format and use e.g. earnings1974,
>> earnings1975, etc.
>> On Tue, Dec 8, 2009 at 2:06 PM, Joshua Hawley <> wrote:
>>> Been wondering about this for some time. With datasets that are set up as
>>> time series, how would you implement a propensity score analysis to use the
>>> time varying information. One example is a variable representing łwages˛ or
>>> łearnings˛ that would have multiple times per individual. I canąt think of a
>>> way to do this with psmatch2.

*   For searches and help try:

© Copyright 1996–2017 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index