# pvcm: Variable Coefficients Models for Panel Data In plm: Linear Models for Panel Data

 pvcm R Documentation

## 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 previous model.

### Value

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

 `coefficients` the vector (or the data frame for fixed effects) of coefficients, `residuals` the vector 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, `Delta` the estimation of the covariance matrix of the coefficients (random effect models only), `std.error` a data frame containing standard errors for all coefficients for each individual (within models only).

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

Yves Croissant

\insertRef

SWAM:70plm

### 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 (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"))

## Not run:
# replicate Swamy (1970), 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"))

## End(Not run)

```

plm documentation built on Aug. 16, 2022, 5:15 p.m.