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Discuss the structural estimation results in Table 3:
Two coefficient estimates in Model 6 have unexpected sign
Model 3 *Model 6 has two unexpected signs*rhos are unobserved complementarity correlations and sigma is its std. dev. *Why not the main effects? Interpretation of interactions is problematic.
6
7 Correlation between (two) firm-specific variables in the interaction term in realized matches
8 average market complementarity
9 average observed market production
10 average observed market complementarity
Estimating Unobserved Complementarities between Entrepreneurs and Venture Capitalists (view source)
Revision as of 14:25, 23 April 2021
, 14:25, 23 April 2021→Current Work
*We may want to redefine PC Rank so that the higher its value the higher the rank of the city
===General===
Discuss normalization:
See Table 1 for summary statistics for realized matches before data transformation. See
Table 2 for summary statistics for realized matches after data transformation.
Discuss:
To be checked:
*VC Exp is “matchprevindu20” in the codebook, which is defined as “Matched VCs prior experience investing in companies (i.e., count of companies) in the same pccode20 as the PortCo”. There might be a mistake in constructing this variable. The VC Exp for counterfactual matches are greater than or equal to the value for realized matches.
===Tables===
Tables:
1& 2 Summary stats (reg, norm)*Distance normalization is strange. Use logs? *PC RANK should be non-linear?*PC investor can be standardized - first round (of interest), or av. per round?2
3 Structural Estimation Results
4 Change in production with respect to one standard deviation of the (interaction) variable
5 Change in production with respect to one standard deviation of one variable in the interaction term while setting the other variable at 0.5
6 Number of zeros in matches (all vs. realized). Seems to indicate data issues???
7 Correlation between (two) firm-specific variables and (three) portco-specific variables in realized matches
8 Counterfactual average market complementarity, 9 Counterfactual average observed market production, 10 Counterfactual average observed market complementarity
*Exitvalue vs. Investment?
*Currently removing by tenure and experience (in thirds)
*Should do: GSVC, CVC, non-market VC?
===Other Notes===
The estimation strategy is two-step simulated method of moments. The solver is "adaptive_de_rand_1 bin_radius_limited" in the
Julia package "BlackBoxOptim.jl", which executes Differential Evolution algorithm.
Julia code:
*https://github.com/robertfeldt/BlackBoxOptim.jl (support parallel CPU)
==Previous Work==
