pvcm: Variable Coefficients Models for Panel Data

In ycroissant/plm: Linear Models for Panel Data

Variable Coefficients Models for Panel Data

Description

Estimators for random and fixed effects models with variable coefficients.

Usage

pvcm(
  formula,
  data,
  subset,
  na.action,
  effect = c("individual", "time"),
  model = c("within", "random"),
  index = NULL,
  ...
)

## S3 method for class 'pvcm'
summary(object, ...)

## S3 method for class 'summary.pvcm'
print(
  x,
  digits = max(3, getOption("digits") - 2),
  width = getOption("width"),
  ...
)

Arguments

formula	a symbolic description for the model to be estimated,
data	a data.frame,
subset	see lm,
na.action	see lm,
effect	the effects introduced in the model: one of "individual", "time",
model	one of "within", "random",
index	the indexes, see pdata.frame(),
...	further arguments.
object, x	an object of class "pvcm",
digits	digits,
width	the maximum length of the lines in the print output,

Details

pvcm estimates variable coefficients models. Individual or time effects are introduced, respectively, if effect = "individual" (default) or effect = "time".

Coefficients are assumed to be fixed if model = "within", i.e., separate pooled OLS models are estimated per individual (effect = "individual") or per time period (effect = "time"). Coefficients are assumed to be random if model = "random" and the model by \insertCiteSWAM:70;textualplm is estimated; it is a generalized least squares model which uses the results of the OLS models estimated per individual/time dimension (coefficient estimates are weighted averages of the single OLS estimates with weights inversely proportional to the variance-covariance matrices). The corresponding unbiased single coefficients, variance-covariance matrices, and standard errors of the random coefficients model are available in the returned object (see Value).

A test for parameter stability (homogeneous coefficients) of the random coefficients model is printed in the model's summary and is available in the returned object (see Value).

pvcm objects have print, summary and print.summary methods.

Value

An object of class c("pvcm", "panelmodel"), which has the following elements:

coefficients	the vector (numeric) of coefficients (or data frame for fixed effects),
residuals	the vector (numeric) of residuals,
fitted.values	the vector of fitted values,
vcov	the covariance matrix of the coefficients (a list for fixed effects model (model = "within")),
df.residual	degrees of freedom of the residuals,
model	a data frame containing the variables used for the estimation,
call	the call,
args	the arguments of the call,

random coefficients model only (model = "random"):

Delta	the estimation of the covariance matrix of the coefficients,
single.coefs	matrix of unbiased coefficients of single estimations,
single.vcov	list of variance-covariance matrices for single.coefs,
single.std.error	matrix of standard errors of single.coefs,
chisq.test	htest object: parameter stability test (homogeneous coefficients),

separate OLS estimations only (model = "within"):

std.error

a data frame containing standard errors for all coefficients for each single regression.

Author(s)

Yves Croissant, Kevin Tappe

References

\insertAllCited \insertRef

SWAM:71plm

\insertRef

GREE:18plm

\insertRef

POI:03plm

\insertRef

KLEI:ZEIL:10plm

Examples

data("Produc", package = "plm")
zw <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within")
zr <- pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random")

## replicate Greene (2018), p. 452, table 11.22/(2012), p. 419, table 11.14
summary(pvcm(log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp, 
             data = Produc, model = "random"))
             
## replicate Poi (2003) (need data adjustment, remaining tiny diffs are due 
## Poi's data set having more digits, not justified by the original Grunfeld data)
data(Grunfeld) # need firm = 1, 4, 3, 8, 2
Gr.Poi.2003 <- Grunfeld[c(1:20, 61:80, 41:60, 141:160, 21:40), ]
Gr.Poi.2003$firm <- rep(1:5, each = 20)
Gr.Poi.2003[c(86, 98), "inv"] <- c(261.6, 645.2)
Gr.Poi.2003[c(92), "capital"] <- c(232.6)

mod.poi <- pvcm(inv ~ value + capital, data = Gr.Poi.2003, model = "random")
summary(mod.poi)
print(mod.poi$single.coefs)
print(mod.poi$single.std.err)

## Not run: 
# replicate Swamy (1971), p. 166, table 5.2
data(Grunfeld, package = "AER") # 11 firm Grunfeld data needed from package AER
gw <- pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year"))
# close replication of Swamy (1970), (7.4) [remaining diffs likely due to less 
# precise numerical methods in the 1970, as supposed in Kleiber/Zeileis (2010), p. 9]
gr <- pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year"), model = "random")

## End(Not run)

ycroissant/plm documentation built on July 8, 2024, 3:59 a.m.