[R-sig-ME] pgmm errors in R

[Vadym Ilchuk]

Dear All,

I am a last-year student, currently working on my Bachelor's Thesis (so,
still learning R).

It has been a while I tried to find the solution to my problem, yet still
unsuccessfully. Thus, I decided to ask for help from people who know more
than me and potentially could suggest some solutions and ideas.

This is my main model:
[image: image.png]

Here is a dataset:
https://drive.google.com/file/d/1EDPnH2xRXZiPbUrD-sCUuIVdKUtWjojL/view?usp=sharing


So far I have created all the variables needed, and while *plm* works
perfectly, *pgmm *gives errors constantly.

This is my code (NB dataset already contains all the variables created),
furthermore, I tried to clean the needed variables from NAs, still, the
errors are present.
*******************************************************************************

#filtering dataset with NACE = A, and creating variables needed
LVData_A <- LVwin %>%
  filter(NACE == 'A') %>%
  filter(VAw > 0, FAw > 0, COGSw > 0, TURNw > 0, TFAw > 0) %>%
  mutate(ID = ID,
         Year = Year,
         va = log(VAw),
         tfa = log(TFAw),
         fa = log(FAw),
         cogs = log(COGSw),
         turn = log(TURNw),
         ta = log(TAw),
         ff1 = ((LTD + LOAN)/TAw)/median((LTD + LOAN)/TAw, na.rm = TRUE),
         ff2 = (CASHw/TAw)/median(CASHw/TAw, na.rm = TRUE),
         ff3 = (INT/dplyr::lag(LTD + LOAN))/median(INT/lag(LTD + LOAN),
na.rm = TRUE),
         ff4 = median(FAw)/SALEw,
         ff5 = TFAw/TAw)

summary(LVData_A)


##########################################################################################################################

#Estimation of the TFP by using ACF method
LV_ACF_A <-  prodest::prodestACF(LVData_A$turn, fX = LVData_A$cogs, sX =
LVData_A$tfa, pX = LVData_A$cogs, idvar = LVData_A$ID, timevar =
LVData_A$Year,
                                R = 100, cX = NULL, opt = 'DEoptim', theta0
= NULL, cluster = NULL)
summary(LV_ACF_A)

LVomegaACF_A <- prodest::omega(LV_ACF_A)
summary(LVomegaACF_A)

LVData_A$LVomegaACF_A <- prodest::omega(LV_ACF_A)

#########################################################################################################################

 #Estimation of the TFP by using WRDG method
LV_WRDG_A <-  prodest::prodestWRDG(LVData_A$turn, fX = LVData_A$cogs, sX =
LVData_A$tfa, pX = LVData_A$m, idvar = LVData_A$ID, timevar = LVData_A$Year,
                               cX = NULL)
summary(LV_WRDG_A)

LVomegaWRDG_A <- prodest::omega(LV_WRDG_A)
summary(LVomegaWRDG_A)

LVData_A$LVomegaWRDG_A <- as.xts(prodest::omega(LV_WRDG_A))

#########################################################################################################################

#Creating growth variables
LVData_A <- LVData_A %>%
  arrange(ID, Year) %>%
  group_by(ID) %>%
  mutate(domegaACF_A = LVomegaACF_A - dplyr::lag(LVomegaACF_A),
         #domegaWRDG_A = LVomegaWRDG_A - dplyr::lag(LVomegaWRDG_A),
         debt = LTD + LOAN,
         ddebt = debt - dplyr::lag(debt),
         dsales = SALEw - dplyr::lag(SALEw)) %>%
  ungroup

#Panel dataframe creation
PLVData_A <- pdata.frame(LVData_A, index = c("ID","Year"))

#GMM
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) + ddebt + ff1 + log(Age)
+ ta + dsales | plm::lag(domegaACF_A, 2),
           data = PLVData_A, effect = "twoways", model = "twosteps",
transformation = "d", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

# | plm::lag(domegaACF_A, 2:3)
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) + ddebt + ff1 + log(Age)
+ ta + dsales | plm::lag(domegaACF_A, 2:3),
           data = PLVData_A, effect = "twoways", model = "twosteps",
transformation = "d", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

# plm::lag(domegaACF_A, 1:2) AND plm::lag(domegaACF_A, 3)
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1:2) + ddebt + ff1 +
log(Age) + ta + dsales | plm::lag(domegaACF_A, 3),
           data = PLVData_A, effect = "twoways", model = "twosteps",
transformation = "d", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

#| plm::lag(domegaACF_A, 1:2)
z1 <- pgmm(domegaACF_A ~ ddebt + ff1 + log(Age) + ta + dsales |
plm::lag(domegaACF_A, 1:2),
           data = PLVData_A, effect = "twoways", model = "twosteps",
transformation = "d", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

# effect = "individual"
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) + ddebt + ff1 + log(Age)
+ ta + dsales | plm::lag(domegaACF_A, 2:3),
           data = PLVData_A, effect = "individual", model = "twosteps",
transformation = "ld", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

#effect = "twoways", model = "onestep", transformation = "d"
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) + ddebt + ff1 + log(Age)
+ ta + dsales | plm::lag(domegaACF_A, 1),
           data = PLVData_A, effect = "twoways", model = "onestep",
transformation = "d", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

#effect = "individual", model = "onestep", transformation = "ld"
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) + ddebt + ff1 + log(Age)
+ ta + dsales | plm::lag(domegaACF_A, 1),
           data = PLVData_A, effect = "individual", model = "onestep",
transformation = "ld", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

#
z1 <- pgmm(domegaACF_A ~ plm::lag(domegaACF_A, 1) | plm::lag(domegaACF_A,
2:3),
           data = PLVData_A, effect = "twoways", model = "onestep",
transformation = "ld", index = c("ID", "Year"), collapse = FALSE, lost.ts =
NULL)
summary(z1, robust = TRUE)

#Check of the correlation
PLVData_A %>%
  select(domegaACF_A, ddebt, ff1, Age, ta, dsales) %>%
  cor(use = "complete.obs")

*******************************************************************************


Each GMM model specification yields different types of errors.

Thank you in advance for your devoted time.

Best,
Vadym

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