R: PE Test for Linear vs. Log-Linear Specifications
petest
R Documentation
PE Test for Linear vs. Log-Linear Specifications
Description
petest performs the MacKinnon-White-Davidson PE test for comparing
linear vs. log-linear specifications in linear regressions.
Usage
petest(formula1, formula2, data = list(), vcov. = NULL, ...)
Arguments
formula1
either a symbolic description for the first model to be tested,
or a fitted object of class "lm".
formula2
either a symbolic description for the second model to be tested,
or a fitted object of class "lm".
data
an optional data frame containing the variables in the
model. By default the variables are taken from the environment
which petest is called from.
vcov.
a function for estimating the covariance matrix of the regression
coefficients, e.g., vcovHC.
...
further arguments passed to coeftest.
Details
The PE test compares two non-nest models where one has a linear
specification of type y ~ x1 + x2 and the other has a log-linear
specification of type log(y) ~ z1 + z2. Typically, the
regressors in the latter model are logs of the regressors in the
former, i.e., z1 is log(x1) etc.
The idea of the PE test is the following: If the linear specification is
correct then adding an auxiliary regressor with the difference of
the log-fitted values from both models should be non-significant.
Conversely, if the log-linear specification is correct then adding
an auxiliary regressor with the difference of fitted values in levels
should be non-significant. The PE test statistic is simply the marginal
test of the auxiliary variable(s) in the augmented model(s). In petest
this is performed by coeftest.
For further details, see the references.
Value
An object of class "anova" which contains the coefficient estimate
of the auxiliary variables in the augmented regression plus corresponding
standard error, test statistic and p value.
J. MacKinnon, H. White, R. Davidson (1983). Tests for Model Specification in the
Presence of Alternative Hypotheses: Some Further Results.
Journal of Econometrics, 21, 53-70.
M. Verbeek (2004). A Guide to Modern Econometrics, 2nd ed. Chichester, UK: John Wiley.