Trying to use the GMM package in R to estimate the parameters (a-f) of a linear model:
LEV1 = a*Macro + b*Firm + c*Sector + d*qtr + e*fqtr + f*tax
Macro, Firm and Sector are matrices with n number of rows. qtr, fqtr and tax are vectors with n members.
I have one large data frame called unconstrd that has all of the data. First, I break that data into separate matrices:
v_LEV1 <- as.matrix(unconstrd$LEV1)
Macro <- as.matrix(cbind(unconstrd$Agg_Corp_Prof,unconstrd$R1000_TR, unconstrd$CP_Spread))
Firm <- as.matrix(cbind(unconstrd$ppe_ratio, unconstrd$op_inc_ratio_avg, unconstrd$selling_exp_avg,
unconstrd$tax_avg, unconstrd$Mark_to_Bk, unconstrd$mc_ratio))
Sector <- as.matrix(cbind(unconstrd$Sect_Flag03,
unconstrd$Sect_Flag04, unconstrd$Sect_Flag05, unconstrd$Sect_Flag06,
unconstrd$Sect_Flag07, unconstrd$Sect_Flag08, unconstrd$Sect_Flag12,
unconstrd$Sect_Flag13, unconstrd$Sect_Flag14, unconstrd$Sect_Flag15,
unconstrd$Sect_Flag17))
v_qtr <- as.matrix(unconstrd$qtr)
v_fqtr <- as.matrix(unconstrd$fqtr)
v_tax <- as.matrix(unconstrd$tax_dummy)
Then, I bind the data together for the x variable called by gmm:
h=cbind(Macro,Firm,Sector,v_qtr, v_fqtr, v_tax)
Then, I invoke gmm:
gmm1 <- gmm(v_LEV1 ~ Macro + Firm + Sector + v_qtr + v_fqtr + v_tax, x=h)
I get the message:
Error in solve.default(crossprod(hm, xm), crossprod(hm, ym)) :
system is computationally singular: reciprocal condition number = 1.10214e-18
I apologize in advance and admit that I'm a neophyte at R and I've never used GMM before. The GMM function is so general, I've looked at the examples available on the web but nothing seems specific enough to help my situation.