plm (version 2.6-4)

predict.plm: Model Prediction for plm Objects

Description

Predicted values of response based on plm models.

Usage

# S3 method for plm
predict(
  object,
  newdata = NULL,
  na.fill = !inherits(newdata, "pdata.frame"),
  ...
)

Value

A numeric (or a pseries if newdata is a pdata.frame) carrying the predicted values with length equal to the number of rows as the data supplied in newdata and with names the row names of newdata or, if newdata = NULL, the fitted values the original model given in object.

Arguments

object

An object of class "plm",

newdata

An optional pdata.frame in which to look for variables to be used for prediction. If NULL, the fitted values are returned. For fixed effects models, supplying a pdata.frame is recommended.

na.fill

A logical, only relevant if object is a pdata.frame, indicating whether for any supplied out-of-sample indexes (individual, time, combination of both), the missing fixed effect estimate is filled with the weighted mean of the model's present fixed effect estimates or not.

...

further arguments.

Details

predictcalculates predicted values by evaluating the regression function of a plm model for newdata or, if newdata = NULL, it returns the fitted values the plm model.

The fixed effects (within) model is somewhat special in prediction as it has fixed effects estimated per individual, time period (one-way) or both (two-ways model) which should to be respected when predicting values relating to these fixed effects in the model: To do so, it is recommended to supply a pdata.frame (and not a plain data.frame) in newdata as it describes the relationship between the data supplied to the individual. and/or time periods. In case the newdata´'s pdata.frame has out-of-sample data (data contains individuals and/or time periods not contained in the original model), it is not clear how values are to be predicted and the result will contain NA values for these out-of-sample data. Argument na.fill can be set to TRUE to apply the original model's weighted mean of fixed effects for the out-of-sample data to derive a prediction.

If a plain data.frame is given in newdata for a fixed effects model, the weighted mean is used for all fixed effects as newdata for prediction as a plain data.frame cannot describe any relation to individuals/time periods (na.fill is automatically set to TRUE and the function warns).

See also Examples.

Examples

library(plm)
data("Grunfeld", package = "plm")

# fit a fixed effect model
fit.fe <- plm(inv ~ value + capital, data = Grunfeld, model = "within")

# generate 55 new observations of three firms used for prediction:
#  * firm 1 with years 1935:1964 (has out-of-sample years 1955:1964), 
#  * firm 2 with years 1935:1949 (all in sample),
#  * firm 11 with years 1935:1944 (firm 11 is out-of-sample)
set.seed(42L)

new.value2   <- runif(55, min = min(Grunfeld$value),   max = max(Grunfeld$value))
new.capital2 <- runif(55, min = min(Grunfeld$capital), max = max(Grunfeld$capital))

newdata <- data.frame(firm = c(rep(1, 30), rep(2, 15), rep(11, 10)),
                      year = c(1935:(1935+29), 1935:(1935+14), 1935:(1935+9)),
                      value = new.value2, capital = new.capital2)
# make pdata.frame
newdata.p <- pdata.frame(newdata, index = c("firm", "year"))

## predict from fixed effect model with new data as pdata.frame
predict(fit.fe, newdata = newdata.p)

## set na.fill = TRUE to have the weighted mean used to for fixed effects -> no NA values
predict(fit.fe, newdata = newdata.p, na.fill = TRUE)

## predict with plain data.frame from fixed effect model: uses mean fixed effects 
## for prediction and, thus, yields different result with a warning
predict(fit.fe, newdata = newdata)