pht {plm}

Hausman–Taylor Estimator for Panel Data

Description

The Hausman–Taylor estimator is an instrumental variable estimator without external instruments (function deprecated).

Usage

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,
  ...
)

Arguments

formula

a symbolic description for the model to be estimated,

data

a data.frame,

subset

see lm() for "plm", a character or numeric vector indicating a subset of the table of coefficient to be printed for "print.summary.plm",

na.action

see lm(),

model

one of "ht" for Hausman–Taylor, "am" for Amemiya–MaCurdy and "bms" for Breusch–Mizon–Schmidt,

index

the indexes,

...

further arguments.

object, x

an object of class "plm",

digits

digits,

width

the maximum length of the lines in the print output,

Details

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.

Value

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.

Note

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.

Author(s)

Yves Croissant

References

(Amemiya and MaCurdy 1986)

(Baltagi 2013)

(Breusch et al. 1989)

(Hausman and Taylor 1981)

Examples

## replicates Baltagi (2005, 2013), table 7.4; Baltagi (2021), table 7.5
## 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)