Description Usage Arguments Value Author(s) References See Also Examples
View source: R/AuePolyReg_test.R
The function uses a nonlinear polynomial regression model in which it tests for the null hypothesis of structural stability in the regression parameters against the alternative of a break at an unknown time. The method is based on the extreme value distribution of a maximumtype test statistic which is asymptotically equivalent to the maximally selected likelihood ratio. The resulting testing approach is easily tractable and delivers accurate size and power of the test, even in small samples \insertCiteAue_etal_2008funtimes.
1 2 3 4 5 6 7 8  AuePolyReg_test(
y,
a.order,
alpha = 0.05,
crit.type = c("asymptotic", "bootstrap"),
bootstrap.method = c("nonparametric", "parametric"),
num.bootstrap = 1000
)

y 
a vector that contains univariate time series observations. Missing values are not allowed. 
a.order 
order of the autoregressive model which must be a nonnegative integer number. 
alpha 
significance level for testing hypothesis of no change point. Default value is 0.05. 
crit.type 
method of obtaining critical values: "asymptotic" (default) or "bootstrap". 
bootstrap.method 
type of bootstrap if 
num.bootstrap 
number of bootstrap replications if 
A list with the following components:
index 
time point where the change point has occurred. 
stat 
test statistic. 
crit.val 
critical region value (CV(alpha, n)). 
p.value 

Palina Niamkova, Dorcas OforiBoateng, Yulia R. Gel
mcusum.test
change point test for regression
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32  ## Not run:
#Example 1:
#Simulate some time series:
set.seed(23450)
series_1 = rnorm(137, 3, 5)
series_2 = rnorm(213, 0, 1)
series_val = c(series_1, series_2)
AuePolyReg_test(series_1, 1) # no change (asymptotic)
AuePolyReg_test(series_val,1) # one change (asymptotic)
#Example 2:
#Consider a time series with annual number of world terrorism incidents from 1970 till 2016:
c.data = Ecdat::terrorism["incidents"]
incidents.ts < ts(c.data, start = 1970, end = 2016)
#Run a test for change points:
AuePolyReg_test(incidents.ts, 2) # one change (asymptotic)
AuePolyReg_test(incidents.ts, 2, 0.05,"bootstrap", "parametric", 200)
# one change (bootstrap)
incidents.ts[44] #number of victims at the value of change point
year < 197 + 44  1 # year when the change point occurred
plot(incidents.ts) # see the visualized data
#The structural change point occurred at the 44th value which corresponds to 2013,
#with 11,990 identified incidents in that year. These findings can be explained with
#a recent rise of nationalism and extremism due to appearance of the social media,
#Fisher (2019): White Terrorism Shows 'Stunning' Parallels to Islamic State's Rise.
#The New York Times.
## End(Not run)

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