For example, Hahn, Todd, and van der Klaauw (2001) showed that RDD requires milder assumptions relative to those needed for other non-experimental methods. Among the advantages of RDD are the weaker assumptions required for its validity compared to other non-experimental impact evaluation methods. Thus, a large jump in the outcome variable, observed precisely at the threshold value of the running variable, after program intervention can be attributed to the program itself. In the absence of the program, one would expect that any shifts in outcome variables would happen smoothly alongside minor changes in the running variable. Observations just below the cutoff are deemed similar to, and therefore, compare well to those just above the cutoff.
RDD is a quasi-experimental method for evaluating program impact when observation units (example, households) can be sorted using some continuous metric (example, income) and program assignment is based on a pre-determined threshold or cutoff point of the sorting metric.
In Part 3, validation or falsification tests are discussed. In Part 2, a comparison of user-written Stata estimation packages is provided. Lee and Lemieux (2010), Imbens and Lemieux (2007), and Cook (2008) provide comprehensive reviews of regression discontinuity design and its applications in the social sciences. There has been a growing use of regression discontinuity design (RDD), introduced by Thistlewaite and Campbell (1960), in evaluating the impacts of development programs. 20000 is not a plausible monthly date for most Stata users, as it is for some 1600+ years into the future.
#Xline stata date format code
If this is off-target, you may need to supply real data and code details, not just mention what you used in an incomplete description. The effect of those functions is to change (most) values, after which the appropriate display format can be assigned.Īs you should see, strategic advice here is to try out solutions by hand using display. This is tricky, but it is all well documented at help dates and times, just in more detail than most people want to read, as you have to skim and skip whatever doesn't apply to your immediate need.Īs I understand it, your problem in a nutshell is converting monthly dates to quarterly dates, and for that you don't need to extract month and year as you suppose, although that's one route. On top of that, if you have monthly dates, year() and month() can't help without extra functions, as they extract years and months from daily dates. So, changing the display format without changing the values is often just a recipe for nonsense. If I assign it date format %td, %tm, %tq I get shown different dates, but at most one can be correct, as I changed nothing except the display format. So changing the display format changes the Stata date type.Ībsolutely not! For example, suppose I have a number 42. Still, some diagnosis is possible.Īssigning a particular date display format defines a Stata date Strictly, that renders your question off-topic here, as there is nothing like a reproducible example in your question.
You mention several linked problems here without once giving examples showing your exact code used or your real data.
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