predict.TAR: Prediction based on a fitted TAR model

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/predict.TAR.R

Description

Predictions based on a fitted TAR model. The errors are assumed to be normally distributed. The predictive distributions are approximated by simulation.

Usage

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## S3 method for class 'TAR'
predict(object, n.ahead = 1, n.sim = 1000,...)

Arguments

object

a fitted TAR model from the tar function

n.ahead

number of prediction steps ahead

n.sim

simulation size

...

other arguments; not used here but kept for consistency with the generic method

Value

fit

a vector of medians of the 1-step to n.ahead-step predictive distributions

pred.interval

a matrix whose i-th row consists of the 2.5 and 97.5 precentiles of the i-step predictive distribution

pred.matrix

a matrix whose j-th column consists of all simulated values from the j-step predictive distribution

Author(s)

Kung-Sik Chan

References

"Time Series Analysis, with Applications in R" by J.D. Cryer and K.S. Chan

See Also

tar

Examples

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data(prey.eq)
prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,d=3,a=.1,b=.9,print=TRUE)
set.seed(2357125)
pred.prey=predict(prey.tar.1,n.ahead=60,n.sim=1000)
yy=ts(c(log(prey.eq),pred.prey$fit),frequency=1,start=1)
plot(yy,type='n',ylim=range(c(yy,pred.prey$pred.interval)),ylab='Log Prey',
xlab=expression(t))
lines(log(prey.eq))
lines(window(yy, start=end(prey.eq)[1]+1),lty=2)
lines(ts(pred.prey$pred.interval[2,],start=end(prey.eq)[1]+1),lty=2)
lines(ts(pred.prey$pred.interval[1,],start=end(prey.eq)[1]+1),lty=2)

Example output

Attaching package: 'TSA'

The following objects are masked from 'package:stats':

    acf, arima

The following object is masked from 'package:utils':

    tar

time series included in this analysis is:  log(prey.eq) 
SETAR(2, 1 , 4 ) model delay = 3 
estimated threshold =  4.661  from a Minimum AIC  fit with thresholds 
searched from the  17  percentile to the   81  percentile of all data.
The estimated threshold is the  56.6  percentile of
all data.
lower regime: 
Residual Standard Error=0.2341
R-Square=0.9978
F-statistic (df=2, 28)=6355.76
p-value=0

                       Estimate Std.Err t-value Pr(>|t|)
intercept-log(prey.eq)   0.2621  0.3156  0.8305   0.4133
lag1-log(prey.eq)        1.0175  0.0704 14.4455   0.0000




 (unbiased) RMS 
0.05479 
 with no of data falling in the regime being 
log(prey.eq) 30 


 (max. likelihood) RMS for each series (denominator=sample size in the regime) 
log(prey.eq) 0.05114 


 upper regime: 
Residual Standard Error=0.2676
R-Square=0.9971
F-statistic (df=5, 18)=1253.556
p-value=0

                       Estimate Std.Err t-value Pr(>|t|)
intercept-log(prey.eq)   4.1986  1.2841  3.2697   0.0043
lag1-log(prey.eq)        0.7081  0.2023  3.5005   0.0026
lag2-log(prey.eq)       -0.3009  0.3118 -0.9648   0.3474
lag3-log(prey.eq)        0.2788  0.4063  0.6861   0.5014
lag4-log(prey.eq)       -0.6113  0.2726 -2.2427   0.0377




 (unbiased) RMS 
0.07158 
 with no of data falling in the regime being 
23 


 (max. likelihood) RMS for each series (denominator=sample size in the regime)
0.05602 

 Nominal AIC is  10.92 

TSA documentation built on July 2, 2018, 1:04 a.m.