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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
*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===
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 key regressions labels in the pdf aresomewhat 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, andwe 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===
