I am a last-year student at university, currently working on my Bachelor's (so still learning R), and I really hope that you would suggest a potential solution (even if to use Python).
So, the main model for my bachelor is as follows: Main model for the Thesis
I need to estimate this model by using the GMM (preferably Arellano and Bond (1991)). I have been searching for the solution for almost 2 months, yet still not succeeded.
The data set could be found here: https://drive.google.com/file/d/15NBcZ7TBfoJ7TnM3BYgtt5JW3T3CmqGJ/view?usp=sharing
My code is the following:
PDataA2 <- pdata.frame(DataA2, index = c("ID","Year"))
z1 <- pgmm(domega ~ lag(domega, 1) + ddebt + ff1 + ff1:ddebt + Age + ta + dsales | lag(domega, 2),
data = PDataA2, effect = "twoways", model = "twosteps", index = c("ID", "Year"), transformation = "ld")
summary(z1, robust = TRUE)
domega is TFP growth, ddebt - debt growth, ta - log of total assets, dsales - sales growth.
ff1, ff2, ff3, ff4, ff5 stands for financial frictions in the model.
After I have created all the needed variables, I am trying to estimate coefficients by using pgmm function in plm package. And receive 2 errors:
In case I use transformation = "d", errors are as follows:
1 type of error
In case I use transformation = "ld", errors are as follows:
2 type of error
In case I delete lag from the model and put it after "|" sign I get: 3 type of error
I would really appreciate any comments and suggestions provided because I do not know how to get away from this dead end. Thank you in advance!)
Please ask for the data and any explanation, since I am really desperate to find the solution.
UPD: here is a correlation matrix:
domegaACF_A ddebt ff1 Age ta dsales lag1_domegaACF_A lag2_domegaACF_A
domegaACF_A 1.000000000 -0.014777102 -0.002600866 -0.019160423 0.02456158 0.256801279 -0.379350157 -0.027687422
ddebt -0.014777102 1.000000000 0.128264730 0.004706522 0.03878795 0.057488971 0.018800962 -0.003222902
ff1 -0.002600866 0.128264730 1.000000000 -0.048072279 -0.02868682 0.008979745 -0.002808377 0.008062733
Age -0.019160423 0.004706522 -0.048072279 1.000000000 0.27884112 -0.021440539 -0.051829392 -0.048162046
ta 0.024561579 0.038787947 -0.028686815 0.278841116 1.00000000 0.078247618 0.015304990 0.015539599
dsales 0.256801279 0.057488971 0.008979745 -0.021440539 0.07824762 1.000000000 -0.049518811 -0.004134281
lag1_domegaACF_A -0.379350157 0.018800962 -0.002808377 -0.051829392 0.01530499 -0.049518811 1.000000000 -0.358671174
lag2_domegaACF_A -0.027687422 -0.003222902 0.008062733 -0.048162046 0.01553960 -0.004134281 -0.358671174 1.000000000