Parallelize for loops that subset panel data by industry-year - r

Question

I want to carry out an estimation procedure that uses data on all firms in a given sector, for a rolling window of 5 years.
I can do it easily in a loop, but since the estimation procedure takes quite a while, I would like to parallelize it. Is there any way to do this?
My data looks like this:
sale_log cogs_log ppegt_log m_naics4 naics_2 gvkey year
1 3.9070198 2.5146032 3.192821715 9.290151e-02 72 1001 1983
2 4.1028774 2.7375141 3.517861329 1.067687e-01 72 1001 1984
3 4.5909863 3.2106595 3.975112703 2.511660e-01 72 1001 1985
4 3.2560391 2.7867256 -0.763368555 1.351031e-02 44 1003 1982
5 3.2966287 2.8088799 -0.305698649 1.151525e-02 44 1003 1983
6 3.2636907 2.8330357 0.154036559 8.699394e-03 44 1003 1984
7 3.7916480 3.2346849 0.887916936 1.351803e-02 44 1003 1985
8 4.1778028 3.5364473 1.177985972 1.761273e-02 44 1003 1986
9 4.1819066 3.7297111 1.393016951 1.686331e-02 44 1003 1987
10 4.0174411 3.6050022 1.479584215 1.601205e-02 44 1003 1988
11 3.4466429 2.9633579 1.312863013 8.888067e-03 44 1003 1989
12 3.0667367 2.6128805 0.909779173 2.102674e-02 42 1004 1965
13 3.2362968 2.8140391 1.430690273 2.050934e-02 42 1004 1966
14 3.1981990 2.8822097 1.721614365 1.702929e-02 42 1004 1967
15 3.9265031 3.6159280 2.399823853 2.559074e-02 42 1004 1968
16 4.3343438 4.0116068 2.592692585 3.649313e-02 42 1004 1969
17 4.5869564 4.3059855 2.772196529 4.743631e-02 42 1004 1970
18 4.7015486 4.3995561 2.875267240 5.155589e-02 42 1004 1971
19 5.0564414 4.7539697 3.218686385 6.863808e-02 42 1004 1972
20 5.4323873 5.1711531 3.350849771 8.272720e-02 42 1004 1973
21 5.2979696 5.0033437 3.383504340 6.726429e-02 42 1004 1974
22 5.3958779 5.1475985 3.475121024 1.534230e-01 42 1004 1975
23 5.5442635 5.3195666 3.517557041 1.674937e-01 42 1004 1976
24 5.6260795 5.3909462 3.694842501 1.711362e-01 42 1004 1977
25 5.8039766 5.5455887 3.895724689 1.836405e-01 42 1004 1978
26 5.8198831 5.5665980 3.960153940 1.700499e-01 42 1004 1979
27 5.7474447 5.4697019 3.943733263 1.520660e-01 42 1004 1980
where gvkey is the firm id and naics are the industry codes.
The code I wrote:
theta=matrix(,60,23)
count=1
temp <- dat %>% select(
"sale_log", "cogs_log", "ppegt_log",
"m_naics4", "naics_2", "gvkey", "year"
)
for (i in 1960:2019) { # 5-year rolling sector-year specific production functions
sub <- temp[between(temp$year,i-5,i),] # subset 5 years
jcount <- 1
for (j in sort(unique(sub$naics_2))) { # loop over sectors
temp2 <- sub[sub$naics_2==j,]
mdl <- prodestOP(
Y=temp2$sale_log, fX=temp2$cogs_log, sX=temp2$ppegt_log,
pX=temp2$cogs_log, cX=temp2$m_naics4, idvar=temp2$gvkey,
timevar=temp2$year
)
theta[count,jcount] <- mdl#Model$FSbetas[2]
jcount <- jcount+1
}
count <- count+1
}