tvReg (version 0.5.6)

predict.tvlm: Predict Methods for Objects in tvReg.

Description

Predict methods for objects with class attribute tvlm, tvar, tvvar, tvirf, tvsure and tvplm. This function needs new values of variables y (response), x (regressors), exogen (exogenous variables, when used), and z (smoothing variable).

Usage

# S3 method for tvlm
predict(object, newdata, newz, ...)
# S3 method for tvar
predict(object, newdata, newz, newexogen = NULL, ...)
# S3 method for tvvar
predict(object, newdata, newz, newexogen = NULL, ...)
# S3 method for tvsure
predict(object, newdata, newz, ...)
# S3 method for tvplm
predict(object, newdata, newz, ...)

Arguments

object

An object used to select a method.

newdata

A pdata.frame with new values of all regressors, with the same name and order as they appear in argument 'data' from the 'tvplm' object

newz

A vector with new values of the smoothing variable.

...

Other arguments passed to specific methods.

newexogen

A matrix or vector with the new value of the exogenous variables. Only for predictions of 'tvar' and 'tvvar' objects.

Value

An object of class matrix or vector with the prediction.

See Also

forecast.

Examples

# NOT RUN {
## Example of TVLM prediction with coefficients as 
## functions of the realized quarticity

data("RV")
RV2 <- head(RV, 2001)
z <- RV2$RQ_lag_sqrt
TVHARQ <- tvLM (RV ~ RV_lag + RV_week + RV_month, 
                 z = z, data = RV2, bw = 0.0062)
newdata <- cbind(RV$RV_lag[2002:2004], RV$RV_week[2002:2004],
              RV$RV_month[2002:2004])
newz <- RV$RQ_lag_sqrt[2002:2004]
predict(TVHARQ, newdata, newz)

## Example of TVAR prediction with coefficients as 
## functions of the realized quarticity
data("RV")
RV2 <- head(RV, 2001)
exogen = RV2[, c("RV_week", "RV_month")]
TVHARQ2 <- tvAR (RV2$RV, p = 1, exogen = exogen,  
                      z = RV2[, "RQ_lag_sqrt"], bw = 0.0062)
newylag <- RV$RV[2002:2004]
newz <- RV$RQ_lag_sqrt[2002:2004]
newexogen <- RV[2002:2004, c("RV_week", "RV_month")]
predict(TVHARQ2, newylag,  newz, newexogen = newexogen)
## Example of TVVAR prediction with coefficients as 
## functions of a random ARMA (2,2) process

data(usmacro, package = "bvarsv")
smoothing <- arima.sim(n = NROW(usmacro) + 3, 
list(ar = c(0.8897, -0.4858), ma = c(-0.2279, 0.2488)), 
sd = sqrt(0.1796))
smoothing <- as.numeric(smoothing)
TVVAR.z <- tvVAR(usmacro, p = 6, type = "const", 
               z = smoothing[1:NROW(usmacro)], bw = c(16.3, 16.3, 16.3))
newdata <- data.frame(inf = c(2, 1, 6), une = c(5, 4, 9), tbi = c(1, 2.5, 3))
newz <- c(0, 1.2, -0.2)
predict(TVVAR.z, newdata = newdata, newz = newz)

## Example of TVSURE prediction with coefficients as 
## functions of an ARMA(2,2) process
data("Kmenta", package = "systemfit")
nobs <- NROW (Kmenta)
eqDemand <- consump ~ price + income
eqSupply <- consump ~ price + farmPrice 
system <- list(demand = eqDemand, supply = eqSupply)
smoothing <- arima.sim(n = nobs + 3, 
                       list(ar = c(0.8897, -0.4858), ma = c(-0.2279, 0.2488)), 
                       sd = sqrt(0.1796))
smoothing <- as.numeric(smoothing)
TVOLS.z <- tvSURE(system, data = Kmenta,  
                      z = smoothing[1:nobs],  bw = c(7, 1.8),
                      est = "ll")
newdata <- data.frame(consump = c(95, 100, 102), price = c(90, 100, 103), 
                      farmPrice = c(70, 95, 103), income = c(82, 94, 115))
newz <- tail(smoothing, 3)
predict(TVOLS.z, newdata = newdata, newz = newz)

data(OECD)
z <- runif(length(levels(OECD$year)), 10, 15)
TVPOLS <- tvPLM(lhe~lgdp+pop65+pop14+public, z = z,
index = c("country", "year"), data = OECD,  method ="pooling", bw =  2)
newdata <- cbind(lgdp = c(10, 13), pop65 = c(9, 12), 
pop14 = c(17, 30), public = c(13, 20))  
newz <- runif(2, 10, 15)
predict(TVPOLS, newdata = newdata, newz = newz)
# }