tsdiag.TAR: Model diagnostics for a fitted TAR model In TSA: Time Series Analysis

Description

The time series plot and the sample ACF of the standardized residuals are plotted. Also, a portmanteau test for detecting residual correlations in the standardized residuals are carried out.

Usage

 ```1 2``` ```## S3 method for class 'TAR' tsdiag(object, gof.lag, col = "red",xlab = "t", ...) ```

Arguments

 `object` a fitted TAR model output from the tar function `gof.lag` number of lags of ACF to be examined `col` color of the lines flagging outliers, etc. `xlab` x labels for the plots `...` any additional user-supplied options to be passed to the acf function

Value

Side effects: plot the time-series plot of the standardized residuals, their sample ACF and portmanteau test for residual autocorrelations in the standardized errors.

Kung-Sik Chan

References

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

`tar`

Examples

 ```1 2 3``` ```data(prey.eq) prey.tar.1=tar(y=log(prey.eq),p1=4,p2=4,d=3,a=.1,b=.9,print=TRUE) tsdiag(prey.tar.1) ```

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.