pht {plm} | R Documentation |
The Hausman–Taylor estimator is an instrumental variable estimator without external instruments (function deprecated).
pht(formula, data, subset, na.action, model = c("ht", "am", "bms"), index = NULL, ...) ## S3 method for class 'pht' summary(object, ...) ## S3 method for class 'summary.pht' print(x, digits = max(3, getOption("digits") - 2), width = getOption("width"), subset = NULL, ...)
formula |
a symbolic description for the model to be estimated, |
object,x |
an object of class |
data |
a |
subset |
see |
na.action |
see |
model |
one of |
index |
the indexes, |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
... |
further arguments. |
pht
estimates panels models using the Hausman–Taylor
estimator, Amemiya–MaCurdy estimator, or Breusch–Mizon–Schmidt estimator,
depending on the argument model
. The model is specified as a two–part
formula, the second part containing the exogenous variables.
An object of class c("pht", "plm", "panelmodel")
.
A "pht"
object contains the same elements as plm
object, with a
further argument called varlist
which describes the typology of
the variables. It has summary
and print.summary
methods.
The function pht
is deprecated. Please use function plm
to estimate
Taylor–Hausman models like this with a three-part formula as shown in the example:
plm(<formula>, random.method = "ht", model = "random", inst.method = "baltagi")
.
The Amemiya–MaCurdy estimator and the Breusch–Mizon–Schmidt estimator is computed
likewise with plm
.
Yves Croissant
Amemiya, T. and MaCurdy, T.E. (1986) Instrumental–variable estimation of an error components model, Econometrica, 54(4), pp. 869–880.
Baltagi, Badi H. (2013) Econometric Analysis of Panel Data, 5th ed., John Wiley and Sons.
Breusch, T.S., Mizon, G.E. and Schmidt, P. (1989) Efficient estimation using panel data, Econometrica, 57(3), pp. 695–700.
Hausman, J.A. and Taylor W.E. (1981) Panel data and unobservable individual effects, Econometrica, 49(6), pp. 1377–1398.
## replicates Baltagi (2005, 2013), table 7.4 ## preferred way with plm() data("Wages", package = "plm") ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "baltagi") summary(ht) am <- plm(lwage ~ wks + south + smsa + married + exp + I(exp ^ 2) + bluecol + ind + union + sex + black + ed | bluecol + south + smsa + ind + sex + black | wks + married + union + exp + I(exp ^ 2), data = Wages, index = 595, random.method = "ht", model = "random", inst.method = "am") summary(am) ## deprecated way with pht() for HT #ht <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + # bluecol + ind + union + sex + black + ed | # sex + black + bluecol + south + smsa + ind, # data = Wages, model = "ht", index = 595) #summary(ht) # deprecated way with pht() for AM #am <- pht(lwage ~ wks + south + smsa + married + exp + I(exp^2) + # bluecol + ind + union + sex + black + ed | # sex + black + bluecol + south + smsa + ind, # data = Wages, model = "am", index = 595) #summary(am)