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Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists (view source)
Revision as of 20:01, 18 July 2019
, 20:01, 18 July 2019→Rebuilding Marcos
|Has title=Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists
|Has author=Ed Egan, Jeremy Fox, David Hsu, Chenyu Yang,
|Has RAs=Meghana Gaur, James Chen, Kyran Adams, Marcos Ki Hyung Lee,Wei Wu|Has paper status=DraftIn development
}}
==Current Work==
All work is in:
E:\projects\unobservedcomplementarities
Dataset was buit from '''vcdb3''' (see below). Latest data files are:
*MasterRealC20YearFullPlus.txt
*MasterRealC20YearFullPlus - DataDictionary.txt
Subdirectories:
*Marcos' old work is in .\marcos
*Chenyu's code and datasets are in .\matlab
*.\linearmodel is the current STATA work
Chenyu's box (available until Oct 31st 2019) is here: https://rochester.app.box.com/s/nvtqgpmyygjykes3lcx9s53sfzmu8c27
It contains:
*working folder
*sample batch files
Both folders were cloned to E:\projects\unobservedcomplementarities\Chenyusbox on 17th July 2019.
Notes:
*data_import3.m uses MasterRealC20YearFullPlus.txt, which is the latest dataset
===Linear Model===
The objective is to add city ranking and serials, possibly as well as no. coinvestors, and VC experience x no. coinvestors, to a linear model.
The data for the linear model should include real and synthetic matches. However, to make it comparable to Chenyu's data, we need to exclude some markets.
Marcos' used Z:\VentureXpertDB\vcdb3\MasterRealOneSynth.txt as a base dataset, which contained only a single synth. However, in lpm_full.do, he loads MasterWithSynthcode20.txt instead. Note that some of Marcos' do files were not in the dropbox but were in E:\mcnair\Projects\MatchingEntrepsToVC\Stata\DoFiles
Data notes:
*Exit, exitvalue and related measures are going to be right censored
In data_import2.m, Chenyu has the following restrictions that I clone in STATA (counts are mine):
*He starts with MasterC20YearFull.txt, rather than MasterRealC20YearFullPlus.txt (which suggests he isn't using the latest data)
*Mkts are pccode20,dealminroundyear
*Removes unmatched VCs and startups (shouldn't be in latest dataset?)
*Requires that matched VCs have synthetics with all startups in the market (should be redundant now?)
*Requires there to tbe >=5 and <=10 real matches in a market
**This reduces the number of obs from 445,710 to just 59,205 (13.3%)
*Requires the year to be >=1990 and <=2001
**142,738 out of 445,710 (32%), or 18,055 out of 59,205
*Removes duplicates (should be redundant with revised data?)
*Removes markets with marketid NaN (not clear why this happens)
*In master_dyad.m, Chenyu has year bounds of 2002 and 2016. This upper bound likely has right censoring on exits.
The STATA do file is in:
E:\projects\unobservedcomplementarities\linearmodel
====Rebuilding Marcos====
Marcos starts with a dataset of reals with a single synthetic, and then constructs a dataset of reals with all synthetics (in the same year and code20).
Table 1 gives some LPMs using two sets of variables with and without VC-yearmet fixed effects. These are replicated in the new do file. In order to get something close to Marcos's reported numbers, I create a one-to-one variable so that each real match has only a single synthetic match. This gives about 60k observations as compared with Marcos's 64K (and as opposed to 445k for the full sample). The coefficients are very close to those in Table 1. There are some caveats, however. Marcos is using:
*Amounts in billions (as am I) without taking logs (of 1+x)
*Firmid x year (which he refers to as VC x yearmet) fixed effects, as opposed to year (i.e., dealminroundyear) x pccode20 fixed effects, which correctly define a market
*No restrictions on timing
Table 5 gives some LPMs before and after a Lasso. The hqdist variable was first transformed so that hqdist = hqdist/1000. Note that the matchhqdist variable is bimodal. Matchbodist is also bimodal but not as strongly. The second spike in the distribution is just over 4000km, which is the arc distance from San Francisco to Boston (4335km [https://www.distance.to/Boston/San-Francisco])
Again the data is just a single synthetic for each real. In this analysis, Marcos also clusters the standard errors at the year level, but does not use any fixed effects.
The labels in the pdf are somewhat misleading. The margin command reports only the underlying covariates not the interactions (unless you specifically generate the variables). An analysis of just the underlying variables without the interactions would have produced markedly different margins! The margins in table 6 column 1 of the pdf are coming from the following:
PDF -> source
--------------------------------------
hdqist -> c.hqdist##c.hqdist
sumprevsameindu20 -> c.sumprevsameindu20##c.sumprevsameindu20
serials -> c.serials##c.numprevportco
numprevportcos -> c.patentsprevc##c.numprevportco
firmtenure -> c.serials##c.firmtenure
patentsprevc -> c.patentsprevc##c.firmtenure
Note that STATA uses ## to report both main effects for each variable as well as an interaction, so c.hqdist##c.hqdist reports both hqdist and hqdist^2, while c.serials##c.numprevportco reports serials, numprevportco, and serials*numprevportco. Variables are omitted when duplicated as in c.serials##c.numprevportco and c.patentsprevc##c.numprevportco, which both report numprevportco.
We don't get the same lasso results as Markus:
Variable MarcosLasso NewLasso
-----------------------------
hdqist yes yes
sumprevsameindu20 yes yes
serials yes no
numprevportcos yes no
firmtenure yes yes
patentsprevc no no
But Marcos's spec isn't very grounded. He clusters standard errors at the year level but uses no fixed effects. We want to know what goes on inside markets, implying market-level fixed effects. He believes that "Since non-match specific variables are not used in the structural model, we have to interact VC or Startup specific variables." I'm not sure that this is correct. He goes on to say that "Therefore, the main specification is one which every match-specific variable has a quadratic interaction, and startup and VC variables are interacted with each other. Also, we exclude industry code from the model because it is a discrete variable, and
we transform VC founding year to VC tenure, which subtracts the former with year of match."
Industry certainly won't matter with market fixed effects. Marcos also used numprevportco as if it was purely a VC variable, rather than being closer to a match specific variable.
I tried Marcos's approach using all of the possible variables (old and new) but always and only using firmtenurel as a VC interaction variable (as firmportcosl is used to pick the real from the list of potential reals, and as firmapportione~ml is correlated with firmportcosl). I will also only use pccityoverallr~1l as the PortCo interaction variable, as that's the only PortCo variable that survives to significance.
The result was:
. margins, dydx(*) post
Average marginal effects Number of obs = 381,882
Model VCE : Robust
Expression : Linear prediction, predict()
dy/dx w.r.t. : pccityoverallrankm1l firmtenurel firmportcosl matchprevindu20l matchbodistl
matchinstagenarrow matchcity matchstate
--------------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
---------------------+----------------------------------------------------------------
pccityoverallrankm1l | .0055706 .0002035 27.38 0.000 .0051718 .0059694
firmtenurel | .0059353 .0005165 11.49 0.000 .0049229 .0069477
firmportcosl | .0052155 .0004399 11.86 0.000 .0043532 .0060777
matchprevindu20l | -.0536725 .0007413 -72.41 0.000 -.0551254 -.0522196
matchbodistl | -.0106516 .0003413 -31.21 0.000 -.0113205 -.0099826
matchinstagenarrow | .0057086 .0007494 7.62 0.000 .0042398 .0071774
matchcity | .0684326 .0041129 16.64 0.000 .0603715 .0764937
matchstate | .0436431 .0015343 28.45 0.000 .040636 .0466503
--------------------------------------------------------------------------------------
Finally, collapse the dataset by summing realmatch and produce a histogram and some analysis.
===Notes from Conference Call===
On July 5th at 10am, the co-authors had a conference call. These are the notes from that call:
Ed described the data. Chenyu said that he is running the estimation as two periods: one before and one after dot com crash. He is estimating:
*VCExperience (# prior deals in pccode20) x ranking ($amount or overall, lagged 1yr or 5yr)
*No. of Co-investors
*Distributional params of unobservables
The estimates are quite stable. But when he includes VC Experience x co-investors, estimates come much larger for unobserved hetero. It is possible that this is because of the optimization process. It uses a GA and the objective function is non-smooth because it relies upon Method-of-Moments (and hence the GA might not be finding the global max/min). However, it could also be a with the moments. This is the current sticking point. Possible solutions are:
*Replace the variable with total number of previous startups by VC?
*Scale Co-investors?
Ed is going to reproduce table 5 with new regression evidence for city ranking and serials. He's also going to check what is in dropbox and clear it up.
To Discuss:
*Patents - to many zeros, Serials - not specific to a market
Counterfactual
*No resorting
*Take away different categories
**Mkt vs. Non-mkt
Compute:
*Currently running it on a 120 core cluster. Each job takes 12 cores - 20 seconds for each evaluation, 6 free params.
*Needs Low RAM (3GB per core).
*Rochester - 120hr max war time
===VCDB3 Rebuild===
The dataset was rebuilt using vcdb3 -- See [[VentureXpert Data]] and then fixed by Ed using RevisedDbaseCode.sql in E:\projects\vcdb3 and /bulk/vcdb3.
After that, Ed did the following:
# Rebuild the code so that only matched VCs are used as synthetics
# Add year and industry as variables
# Add variables:
## City rankings over time, lagged by 1 year and by 5 years
## Age
## Various serial measures
## Rebuild data dictionary!
## Definition of industry codes again
Specifically:
E:\projects\vcdb3\MatchingEntrepsV3.sql
Fix PortCoMatchMaster and copeopleaggsimple to make and pass:
E.serialceopres, E.serialfounder, E.ceopres, E.singularceopres, E.founders, E.hasfounders, E.prevs, E.prevceopres, E.prevfounders
Also fix doctors!
E:\projects\vcdb3\RevisedDBaseCode.sql
Fix PortCoSuper and add:
C.serialceopres AS pcserialceopres,
C.serialfounder AS pcserialfounder,
C.ceopres AS pcceopres,
C.singularceopres AS pcsingularceopres,
C.founders AS pcfounders,
C.hasfounders AS pchasfounders,
C.prevs AS pcprevs,
C.prevceopres AS pcprevceopres,
C.prevfounders AS pcprevfounders,
C.serialceopres*C.singularceopres AS pcserialceopressingular,
C.serialfounder*C.hasfounders AS pcserialfounderhas,
C.prevceopres*C.singularceopres AS pcprevceopressingular,
C.prevfounders*C.hasfounders AS pcprevfoundershas
Fix E:\projects\vcdb3\OriginalSQL\Ranking.sql (Note originally fixed in E:\projects\vcdb3\Ranking.sql). Specifically, add the rankingfull queries: city, state, year, dollarsrank, dealsrank, aliverank, overallrank
Finally, in:
E:\projects\vcdb3\MatchingEntreps2VCs
Make:
*MatchingVCEntrepRevisions.sql
*MasterRealC20YearFullPlus.txt
*MasterRealC20YearFullPlus - DataDictionary.txt
====Variables for inclusion====
New potential variables still being considered for inclusion:
*VC historic CAPUM?
*Industry-Year Measures? Needs input from Chenyu. Likely not useful.
Currently Chenyu is using:
*Distance
*Sub-sector specific expertise of VC - could broaden definition
**Currently: most small 10-15 matches using pccode20
**Might end up with more large markets
*Startup specific experience
**patent counts - mostly 0s: 95%.
This will be revised once he has the new data set.
Chenyu is now going to do:
*Monte Carlo with data from empirical distro
*Actual estimation - doesn't take long
*Reduced form estimation: VC investment and outcomes? Logit? outcomes (exit measures). Real match explatory variable, match characteristics, controlling
*Target: May
===Running Chenyu's code on HPCC===
Two Wharton ugrads: Stacey and Kenneth (no account yet) are going to try running Chenyu's code on the HPCC. Chenyu is going to put everything into Box and invite us all to it.
==Reference Papers==
[[Fox Hsu Yang (2015) - Unobserverd Heterogeneity in Matching Games with an Application to Venture Capital]] provides some notes.
==Previous Work (for reference)== ===Matlab Code===
[[Abhijit Brahme (Work Log)]] contains his notes on working with the Matlab code. There is a seperate page here: [[Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists Matlab Code]].
Z:\Projects\MatchingAcceleratorsToVCs
===Data foundations===
The database is '''vcdb2'''
E:\McNair\Projects\MatchingEntrepsToVC\DataWork
===Dataset build===
====Decisions====
Decisions we need to make:
*How to collapse VC rounds (date, amount, etc.): We will use only seed, early, later stage investment and insist on the presence of seed/early for inclusion. We can then have date first, investment duration (to date last), total investment.
====Objective dataset description====
Unit of observation - a startup-fund match.
*maybe stage-match (doesn't make a lot of sense when collapsing rounds) between lead and port co.
====Identifying lead VCs====
Possible methods:
*Participant in earliest round that stayed in for longest with tie-breaker
====Minor Industry====
Across all time and without regard to SEL vs. transaction, here's the minor industry list and counts:
Consumer Related | 3161
===Literature from David===
Literature to "validate" our sample. I think you probably know the papers I reference below (let me know if you need any of them-some for which I am coauthor you can get from my website).
#If we have access to more individual data: VCs prefer to invest in founders with similar demographic characteristics relative to their own characteristics (Gompers et al within the past few years in JFE, Bengtsson and Hsu in JBV within the last few years).
==Work Done in Late November by Dylan & Ed=SBIR and Patent Data===
SBIR Data taken from McNair\Projects\SBIR\Data\Aggregate SBIR\SBIR.txt. -Note! This file needed to be opened in excel to be readable, and took a very long time to open due to its large size. SBIR firm names converted to a pivot table to eliminate exact repeat entries, and then exported to a txt file, NSBIR. NSBIR then matched using The Matcher in mode 2 with the following code:
