*Bounty: 150*

*Bounty: 150*

I’m trying to fit a negative binomial model to my data because the dependent variable exhibits overdispersion. However, one of my reviewers is insisting that I also test for endogeneity. He or she is worried that two independent variables are potentially endogenous (one of them might potentially be so…). My question is how one goes about testing for overdispersion in a negative binomial model, ideally in R. Can it be done simultaneously for two variables? I already found a potential instrument for the most problematic of these two variables (correlated with the endogenous independent variable but uncorrelated to the dependent variable). I’m just not sure how to go from here… I see papers that implement a two-step Heckman procedure, running the negative binomial regression with the inverse Mills ratio. However, I also read that this might not be appropriate…

My current model looks like this, I’m using R. Basically I’m pooling three years of data from two different countries. I’m primarily interested in the differences between these two countries. I have 2 control variables and 9 independent variables of interest. X1 and X3 are the potential problematic variables. Y is a count of different countries in which firms are present, and independent variables are things like international experience, international education, board independence, etc. Endogeneity arises, for instance, because international firms might hire people with more international experience/education than their local counterparts.

```
negbin <- glm.nb(Y~ Control1 + Contro2 + Year + Country
+ X1*Country
+ X2*Country
+ X3*Country
+ X4*Country
+ X5*Country
+ X6*Country
+ X7*Country
+ X8*Country
+ X9*Country
+ X10*Country, data = mydata)
summary(negbin)
car::vif(negbin)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.04651 -1.16581 -0.56598 0.01105 3.00675
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.588771 1.742045 0.912 0.361761
Control1 0.240602 0.086086 2.795 0.005191 **
Control2 -0.013200 0.003732 -3.537 0.000404 ***
YearThree 0.152904 0.277186 0.552 0.581203
YearTwo 0.085071 0.276648 0.308 0.758459
Country -1.899136 2.604823 -0.729 0.465950
X1 1.609189 0.652992 2.464 0.013727 *
X2 0.146868 0.111476 1.317 0.187674
X3 -4.792707 0.748956 -6.399 1.56e-10 ***
X4 4.352965 0.677561 6.424 1.32e-10 ***
X5 -0.054561 0.015381 -3.547 0.000389 ***
X6 -1.497622 0.374987 -3.994 6.50e-05 ***
X7 -2.689511 0.768235 -3.501 0.000464 ***
X8 -0.078919 0.069243 -1.140 0.254394
X9 4.237630 1.544278 2.744 0.006068 **
X10 3.333337 1.258869 2.648 0.008100 **
Country:X1 0.584704 0.992207 0.589 0.555662
Country:X2 -0.635671 0.332893 -1.910 0.056193 .
Country:X3 4.508881 0.884777 5.096 3.47e-07 ***
Country:X4 -7.823156 1.411851 -5.541 3.01e-08 ***
Country:X5 -0.003909 0.032332 -0.121 0.903779
Country:X6 1.001702 0.570836 1.755 0.079294 .
Country:X7 4.870946 0.991810 4.911 9.05e-07 ***
Country:X8 0.403581 0.100593 4.012 6.02e-05 ***
Country:X9 -2.151496 1.953145 -1.102 0.270655
Country:X10 -21.951529 4.102211 -5.351 8.74e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
```