predict-tvReg: Predict Methods for Objects in tvReg.

In tvReg: Time-Varying Coefficient for Single and Multi-Equation Regressions

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 class 'tvlm'
predict(object, newdata, newz, ...)

## S3 method for class 'tvar'
predict(object, newdata, newz, newexogen = NULL, ...)

## S3 method for class 'tvvar'
predict(object, newdata, newz, newexogen = NULL, ...)

## S3 method for class 'tvsure'
predict(object, newdata, newz, ...)

## S3 method for class '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

## 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)

tvReg documentation built on Sept. 1, 2023, 5:07 p.m.