Create lag, lead and diff variables in plm dataframe

Question

I am attempting to do some panel analysis using lagged, leading and differenced variables. However the plm functions do not provide the desired results as it does not loop over individuals. I have looked online, however the following post (Answer_Stack), using pdata.frame() gave the same problematic results. When i group_by(i) in dplyr i get the desired result. Can anyone explain what is going on?

# Variables
i <- c(1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 7, 7, 7)
t <- c(2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 2003, 2001, 2002, 2003)
y <- c(0.047136, 0.044581, 0.040973, 0.045536, 0.043952, 0.038797, 0.049942, 0.047440, 0.042193, 0.048503, 0.046816, 0.040292, 0.056089, 0.052054, 0.047078, 0.044223, 0.041516, 0.036947, 0.045608, 0.042028, 0.037878)
x <- c(0.32691, 0.33013, 0.32888, 0.40301, 0.40337, 0.40326, 0.29692, 0.29982, 0.29790, 0.30380, 0.30698, 0.30668, 0.27942, 0.28696, 0.28616, 0.31218, 0.31424, 0.31382, 0.34592, 0.34738, 0.34782)

# Create plm dataframe
dta <- data.frame(i, t, y, x)
pdta <- plm.data(dta, indexes = c("i", "t"))

# Create lagged variable with plm
pdta$l.x <- lag(pdta$x)             # Does not work

# Create using dplyr
pdta <- pdta %>%
  group_by(i) %>%
  mutate(lag.x = lag(x))

View(pdta)

Note to answer: Even after following the steps suggested, i get this:

> pdta <- pdata.frame(dta, index= c("i", "t"))
    > head(cbind(pdta$i, pdta$y, lag(pdta$y)), 10)
           [,1]     [,2]     [,3]
    1-2001    1 0.047136       NA
    1-2002    1 0.044581 0.047136
    1-2003    1 0.040973 0.044581
    2-2001    2 0.045536 0.040973
    2-2002    2 0.043952 0.045536
    2-2003    2 0.038797 0.043952
    3-2001    3 0.049942 0.038797
    3-2002    3 0.047440 0.049942
    3-2003    3 0.042193 0.047440
    4-2001    4 0.048503 0.042193

As you can see below, it works for me. I'd suggest that you update your packages as a first pass. I just installed plm, so I have the latest version. If you have the latest version, second possibility is that you loaded dplyr after loading plm. dplyr has it's own lag function, which overwrites base R's lag function. plm uses base R's lag function, so loading dplyr destroys this functionality. — lmo
– lmo, Commented Oct 6, 2017 at 12:03

lmo · Accepted Answer · 2018-01-03 13:37:54Z

3

For one thing, you are not using the right function to convert the data.frame into a pdata.frame. plm.data returns a data.frame which can be directly used in the estimator functions, but is not directly amenable to the data transformation functions. Use pdata.frame instead:

pdta <- pdata.frame(dta, index= c("i", "t"))

Then give lag a try:

head(cbind(pdta$i, pdta$y, lag(pdta$y)), 10)
       [,1]     [,2]     [,3]
1-2001    1 0.047136       NA
1-2002    1 0.044581 0.047136
1-2003    1 0.040973 0.044581
2-2001    2 0.045536       NA
2-2002    2 0.043952 0.045536
2-2003    2 0.038797 0.043952
3-2001    3 0.049942       NA
3-2002    3 0.047440 0.049942
3-2003    3 0.042193 0.047440
4-2001    4 0.048503       NA

Alternatively, you can also do

pdta <- pdata.frame(plm.data(dta, indexes= c("i", "t")))

Second, as we figured out in the comments, loading dplyr "overwrites" (masks) a number of base R functions including the ones you mention. plm builds on these base R functions to perform the desired operations on its pdata.frame objects. As helix123 mentions, even with dplyr loaded, you can refer to the plm implementation using plm::<function name>.

edited Jan 3, 2018 at 13:37

answered Oct 6, 2017 at 11:34

lmo

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4 Comments

Gooze Over a year ago

I have edited my question, but even after following these steps i still get the wrong lags

lmo Over a year ago

Try running the code with a fresh R session. Don't load dplyr.

Gooze Over a year ago

Your comment above seems to solve the specific problem, so do not load dplyr and also re-install plm. I think its the issue with dplyr which is probably causing it! Thanks!

Helix123 Over a year ago

If you want to be sure to have the lag function of plm applied, call it by plm::lag.

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