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Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists (view source)
Revision as of 16:47, 28 August 2021
, 16:47, 28 August 2021no edit summary
|Has paper status=In development
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==Counterfactual Variables==
Version 2-2 of the dataset includes the following sets of variables:
# firmappmoomirank,firmexitsrank,firmappexitmrank,firmappinvmrank,firmportcosrank,
# firmappmoomiwrank,firmappmoomitruncwrank,firmappexitmwrank,
# firmappmoomirankqtile,firmexitsrankqtile,firmappexitmrankrankqtile,firmappinvmrankqtile,firmportcosrankrankqtile,
# firmappmoomiwrankrankqtile,firmappmoomitruncwrankqtile,firmappexitmwrankrankqtile
The first set ranks the performance measures already in the dataset using only the 3542 VC firms in the dataset: apportioned MOOMI (firmapportionedmoomi), exits (firmexits), apportioned exit value (firmapportionedexitvaluem), apportioned investment (firmtotalapportionedinvm) and the total number of portcos invested in (firmnumportcos).
The second set calculates weighted ranks or truncated weighted ranks (where 1 is the best and 3542 is the worst):
* firmappmoomiwrank ranks firmappmoomi*(firmportcos/sumfirmportcos
* firmappmoomitruncwrank ranks firmappmoomi*((CASE WHEN firmportcos <= 75 THEN firmportcos ELSE 75 END)/sumfirmportcostrunc)
* firmappexitmwrank ranks firmappexitm*(firmportcos/sumfirmportcos)
The third and fourth sets are the ranks above as quartiles (i.e., 1 is best, 4 is worst).
By all means, play with the variables! My guess as to the "best" (i.e., closest approximation to true quality as measured by long-run returns) is (in order):
* '''firmappmoomiwrank''': This puts all the most famous VCs in the top quartile and the bottom quartile truly sucks.
* firmappmoomitruncwrank (harder to justify the truncation, so second place but gives higher rankings to successful small firms who face a pretty stiff size penalty in firmappmoomiwrank)
* firmappexitmwrank: Doesn't take into account investment but still delivers a good result
I also took a look at which types of firms to remove. It turns out that '''firmcat''' was already pretty well put together (i.e., I'd gone down that rabbit hole and excavated it). So, I suggest that we try the following components separately and then, depending on the results, consider the following groupings:
* Corporate
* PE
* Ecosystem + Angel
* Gov + SBIC
==Notes==
This section provides notes on the analysis in Selected_prelim.do. I ran the code and did a basic exploration of variables. These are the results:
* '''I couldn't make the sign on distance change. ''' That's a real finding! I tried CA/MA fixed effects and interactions, Silicon Valley/Boston Cambridge fixed effects and interactions, using quadratic effects, various transformations... nothing undid the results. Also, log distance is the best variable.
* matchinstagebroad outperforms matchinstagenarrow in some contexts, we should use it.
* I tried logging all the vars, even t_pccitydollarsrankm1, to no avail.
* pcexitvaluem is already set to 0 when exit=0. It is missing for undisclosed value acquisitions. '''It's a great RHS variable: Log it and use it!'''
* I used mktid fixed effects and the results look pretty good without the interactions... but less good with them for the probit. There was a slight improvement for the reg, but only 2 vars are sig either way. I think maybe year ind is fine.
* Doing value conditional on exit gave just 1 *** and 1 *. It's the exit that drives the sig in exit value.
* l_pcexpceopres is our PC team quality variable
* t_pccitydollarsrankm1 is our PC environment quality variable
==Dataset Rebuild==
