Model components for fitted models with plm

library("knitr")
opts_chunk$set(message = FALSE, warning = FALSE)

plm tries to follow as close as possible the way models are fitted using lm. This relies on the following steps, using the formula-data with some modifications:

Panel data has a special structure which is described by an index argument. This argument can be used in the pdata.frame function which returns a pdata.frame object. A pdata.frame can be used as input to the data argument of plm. If the data argument of plm is an ordinary data.frame, the index argument can also be supplied as an argument of plm. In this case, the pdata.frame function is called internally to transform the data.

Next, the formula, which is the first and mandatory argument of plm is coerced to a Formula object.

model.frame is then called, but with the data argument in the first position (a pdata.frame object) and the formula in the second position. This unusual order of the arguments enables to use a specific model.frame.pdata.frame method defined in plm.

As for the model.frame.formula method, a data.frame is returned, with a terms attribute.

Next, the X matrix is extracted using model.matrix. The usual way to do so is to feed the function with two arguments, a formula or a terms object and a data.frame created with model.frame. lm uses something like model.matrix(terms(mf), mf) where mf is a data.frame created with model.frame. Therefore, model.matrix needs actually one argument and not two and we therefore wrote a model.matrix.pdata.frame which does the job ; the method first checks that the argument has a term attribute, extracts the terms (actually the formula) and then computes the model's matrix X.

The response y is usually extracted using model.response, with a data.frame created with model.frame as first argument, but it is not generic. We therefore created a generic called pmodel.response and provide a pmodel.response.pdata.frame method. We illustrate these features using a simplified (in terms of covariates) example with the SeatBelt data set:

library("plm")
data("SeatBelt", package = "pder")
SeatBelt$occfat <- with(SeatBelt, log(farsocc / (vmtrural + vmturban)))
pSB <- pdata.frame(SeatBelt)

We start with an OLS (pooling) specification:

formols <- occfat ~ log(usage) + log(percapin)
mfols <- model.frame(pSB, formols)
Xols <- model.matrix(mfols)
y <- pmodel.response(mfols)
coef(lm.fit(Xols, y))

which is equivalent to:

coef(plm(formols, SeatBelt, model = "pooling"))

Next, we use an instrumental variables specification. Variable usage is endogenous and instrumented by three variables indicating the law context: ds, dp, and dsp.

The model is described using a two-parts formula, the first part of the RHS describing the covariates and the second part the instruments. The following two formulations can be used:

formiv1 <- occfat ~ log(usage) + log(percapin) | log(percapin) + ds + dp + dsp
formiv2 <- occfat ~ log(usage) + log(percapin) | . - log(usage) + ds + dp + dsp

The second formulation has two advantages:

The formula is coerced to a Formula, using the Formula package. model.matrix.pdata.frame then internally calls model.matrix.Formula in order to extract the covariates and instruments model matrices:

mfSB1 <- model.frame(pSB, formiv1)
X1 <- model.matrix(mfSB1, rhs = 1)
W1 <- model.matrix(mfSB1, rhs = 2)
head(X1, 3) ; head(W1, 3)

For the second (and preferred formulation), the dot argument should be set and is passed to the Formula methods. . has actually two meanings:

which correspond respectively to dot = "seperate" (the default) and dot = "previous". See the difference between the following two examples:

library("Formula")
head(model.frame(Formula(formiv2), SeatBelt), 3)
head(model.frame(Formula(formiv2), SeatBelt, dot = "previous"), 3)

In the first case, all the covariates are returned by model.frame as the . is understood by default as "everything".

In plm, the dot argument is internally set to previous so that the end-user doesn't have to worry about these subtleties.

mfSB2 <- model.frame(pSB, formiv2)
X2 <- model.matrix(mfSB2, rhs = 1)
W2 <- model.matrix(mfSB2, rhs = 2)
head(X2, 3) ; head(W2, 3)

The IV estimator can then be obtained as a 2SLS estimator: First, regress the covariates on the instruments and get the fitted values:

HX1 <- lm.fit(W1, X1)$fitted.values
head(HX1, 3)

Next, regress the response on these fitted values:

coef(lm.fit(HX1, y))

The same can be achieved in one command by using the formula-data interface with plm:

coef(plm(formiv1, SeatBelt, model = "pooling"))

or with the ivreg function from package AER (or with the newer function ivreg in package ivreg superseding AER::ivreg()):

coef(AER::ivreg(formiv1, data = SeatBelt))
X2 <- model.matrix(Formula(form1), mfSB, rhs = 2, dot = "previous")

formols <- occfat ~ log(usage) + log(percapin)  | . - log(usage) +  ds + dp + dsp

form1 <- occfat ~ log(usage) + log(percapin) + log(unemp) + log(meanage) + 
    log(precentb) + log(precenth) + log(densrur) + log(densurb) + 
    log(viopcap) + log(proppcap) + log(vmtrural) + log(vmturban) + 
    log(fueltax) + lim65 + lim70p + mlda21 + bac08
form2 <- . ~ . |  . - log(usage) + ds + dp +dsp

jorm1 <- occfat ~ log(usage) + log(percapin) + log(unemp) + log(meanage) + 
    log(precentb) + log(precenth) + log(densrur) + log(densurb) + 
    log(viopcap) + log(proppcap) + log(vmtrural) + log(vmturban) + 
    log(fueltax) + lim65 + lim70p + mlda21 + bac08 | . - log(usage) + 
    ds + dp + dsp
jorm2 <- noccfat ~ . | .


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plm documentation built on March 18, 2022, 6:15 p.m.