Matching LBOs (Julia)

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Matching LBOs (Julia)
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Project Information
Has title Matching LBOs (Julia)
Has owner James Chen
Has start date
Has deadline date
Has keywords Tool
Has project status Complete
Has sponsor McNair Center
Has project output Tool
Copyright © 2019 All Rights Reserved.

Leveraged Buyout Innovation (Academic Paper)

Inputs and Outputs

  • Input: tab delimited file "E:/McNair/Projects/LBO/Clean/STATApredictLBOclean.txt"
    • This contains list of LBO and nonLBO firms from compustat 1970-2015, propensity scores, patent data, and other variables generated from stata code "" and ""
  • Output: tab delimited file "E:/McNair/Projects/LBO/New matching/matchresults.txt"
    • This is the input file, except with an additional column "matchpair" indicating matched pairs:
    • Positive integers identify pairs matched, negative integers identify matched non-LBOs in years other than the match, -0.1 identifies LBOs that failed to match to any non-LBOs under constraints provided

Running Code

  • Open Julia command line in administrator mode
  • Change directory to E:\McNair\Projects\LBO\New matching\
cd("E:/McNair/Projects/LBO/New\ matching")
  • Run script LBOmatchscript.jl


There are a few options that can be customized in the script before running. Getting 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.

Specify input file (if using different file than default)

Line 12:

df = readtable("E:/McNair/Projects/LBO/Clean/STATApredictLBOclean.txt", separator = '\t');

Specify which observations are valid for matching

  • For now, we filter out all firms that were never granted a single patent in the period 1970-2015
  • For firms that LBO, we also drop their observations in all other years from the list of candidates to match to other LBOs
  • See inline comments in code for detailed description of what matchfilter2, matchfilter4, etc. represent

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
nonLBOs = @from i in df begin
          @where i.everlbo == 0 && (i.matchfilter4 == 1 ||i.matchfilter2b == 1)
          @select i
          @collect DataFrame

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 above options, 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

Line 61:

randoption = 0;

Specify additional constraints on valid matches (modify code within function mcexpr as desired)

  • For example, default code forces matches to be within the same industry group, within the same decade, and with patent stocks within +/- 20% of LBO firm.

Lines 69-81:

function mcexpr(i)
 #note that the below syntax is the simplest way to store a long string over multiple lines
 #(i.e., appending additional characters per line)
 #Also, note that order of operations forces us to put each condition in parentheses
 mcriteria = "nonLBOs[:matchsubset] = (nonLBOs[:industrygroup3].== LBOs[$i,:industrygroup3])"
 mcriteria = mcriteria * " .* (nonLBOs[:decade].==LBOs[$i,:decade])"
 mcriteria = mcriteria * " .* (nonLBOs[:patentstock] .>= (LBOs[$i,:patentstock]*.8))"
 mcriteria = mcriteria * " .* (nonLBOs[:patentstock] .<= (LBOs[$i,:patentstock]*1.2))"
 mcriteria = mcriteria * " .* (nonLBOs[:matchpair] .== 0 )"
 return eval(parse(mcriteria))