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Matching LBOs (Julia) (view source)
Revision as of 13:47, 21 September 2020
, 13:47, 21 September 2020no edit summary
{{Project|Has project output=Tool|Has sponsor=McNair ProjectsCenter
|Has title=Matching LBOs (Julia)
|Has owner=James Chen,
|Has keywords=Tool
|Has project status=ActiveComplete
}}
[[Leveraged Buyout Innovation (Academic Paper)]]
==Options==
There are a few options that can be customized in the script before running. Getting this the code into a more user-friendly form is a WIP. In fact, some parts might be difficult, if not impossible, to write in a more accessible way.
Lines 38-48
#Splitting dataset into LBO and non-LBO firms
#Note that we also filter out all firms that were never granted a single patent in the period 1970-2015
LBOs = @from i in df begin
@where i.everlbo == 1 && (i.matchfilter4 == 1 ||i.matchfilter2b == 1) && i.lboentry == 1
@select i
@collect DataFrame
end
nonLBOs = @from i in df begin
@where i.everlbo == 0 && (i.matchfilter4 == 1 ||i.matchfilter2b == 1)
@select i
@collect DataFrame
end
===Specify propensity score type to use for matching===
*Options are: logitp (panel logit), probitp (panel probit), or Cox proportional hazard (hr)
*Alternatively, can use the aboveoptions, with regressions performed using winsorized values of regressors (trimmed at 1st and 99th percentiles): logitpw, probitpw, hrw
Line 58:
mscore = :logitpw;
===Specify whether matching priority should be deterministic or random.===
*If deterministic, priority goes to lower GVKEY
