predict {sym.arma}

Forecasts from a fitted SYMARMA model

Description

See Maior and Cysneiros (2018) for details on this function.

Usage

predict(model, h, xreg = NULL)

Arguments

model

a result of a call to elliptical.ts.

h

number of periods for forecasting. If xreg is used, h is ignored and the number of forecast periods is set to the number of rows of xreg.

xreg

future values of an regression variables.

Value

pred

predicted values.

Author(s)

Vinicius Quintas Souto Maior and Francisco Jose A. Cysneiros

Maintainer: Vinicius Quintas Souto Maior <[e-mail]>

References

Brockwell, P. J. and Davis, R. A. (1991). Time Series and Forecasting Methods. Second edition. Springer, New York. Section 11.4.

Brockwell, P. J. and Davis, R. A. (1996). Introduction to Time Series and Forecasting. Springer, New York. Sections 5.1 and 7.6.

Maior, V. Q. S. and Cysneiros, F. J. A. (2018). SYMARMA: a new dynamic model for temporal data on conditional symmetric distribution. Statitical Paper, 59, 75-97. doi: 10.1007/s00362-016-0753-z.

Examples

data(assets)
attach(assets)

# Return in the prices on Microsoft and SP500 index

N = length(msf)
.sp500 = ((sp500[2:N]-sp500[1:(N-1)])/sp500[1:(N-1)])*100
.msf = ((msf[2:N]-msf[1:(N-1)])/msf[1:(N-1)])*100

# The T-bill rates were divided by 253 to convert to a daily rate

.tbill = tbill/253

# Excess return in the d prices on Microsoft and SP500 index

Y = .msf - .tbill[1:(N-1)]
X = .sp500 - .tbill[1:(N-1)]

# Period from April 4, 2002 to October 4, 2002

serie = Y[2122:2240]
aux = cbind(X[2122:2240])

# Fit SYMARMA models

fit.1 = elliptical.ts(serie,order=c(0,0,1),include.mean=FALSE,
 family="Normal")

# Forecasts

predict(fit.1, h=10)