View source: R/cumsumCPA_test.R
cumsumCPA_test  R Documentation 
The function tests for a change point in parameters of a linear regression model with errors exhibiting a general weakly dependent structure. The approach extends earlier methods based on cumulative sums derived under the assumption of independent errors. The approach applies smoothing when the time series is dominated by high frequencies. To detect multiple changes, it is recommended to employ a binary or wild segmentation \insertCiteGombay_2010funtimes.
cumsumCPA_test(
y,
a.order,
crit.type = c("asymptotic", "bootstrap"),
bootstrap.method = c("nonparametric", "parametric"),
num.bootstrap = 1000
)
y 
a numeric time series vector. Missing values are not allowed. 
a.order 
order of the autoregressive model which must be a nonnegative integer number. 
crit.type 
a string parameter allowing to choose "asymptotic" or "bootstrap" options. 
bootstrap.method 
a string parameter allowing to choose "nonparametric" or "parametric" method of bootstrapping. "nonparametric" – resampling of the estimated residuals (with replacement); "parametric" – sampling innovations from a normal distribution. 
num.bootstrap 
number of bootstrap replications if 
A list with the following components:
index 
time point where the change has occurred. 
stat 
test statistic. 
p.value 

Palina Niamkova, Dorcas OforiBoateng, Yulia R. Gel
mcusum.test
for change point test for regression
## Not run:
#Example 1:
#Simulate some time series:
series_1 = rnorm(157, 2, 1)
series_2 = rnorm(43, 7, 10)
main_val = c(series_1, series_2)
#Now perform a change point detection:
cumsumCPA_test(series_1, 1) # no change
cumsumCPA_test(main_val, 1) # one change, asymptotic critical region
cumsumCPA_test(main_val, 1, "bootstrap", "parametric") # one change, parametric bootstrap
cumsumCPA_test(main_val, 1, "bootstrap", "nonparametric") # one change, nonparametric
#bootstrap
#Example 2:
#Consider time series with ratio of real GDP per family to the median income. This is a
#skewness and income inequality measure for the US families from 1947 till 2012.
e.data = (Ecdat::incomeInequality['mean.median'])
incomeInequality.ts = ts(e.data, start = 1947, end = 2012, frequency = 1)
#Now perform a change point detection:
cumsumCPA_test(incomeInequality.ts, 0)
cumsumCPA_test(incomeInequality.ts, 0, "bootstrap", "parametric")
cumsumCPA_test(incomeInequality.ts, 0, "bootstrap", "nonparametric")
incomeInequality.ts[13] # median income
Ecdat::incomeInequality$Year[13] + 1 # year of change point
#The first change point occurs at the 13th time point, that is 1960, where the ratio of real
#GDP per family to the median income is 1.940126. This ratio shows that in 1960 the national
#wealth was not distributed equally between all the population and that most people earn
#almost twice less than the equal share of the all produced goods and services by the nation.
#Note: To look for the other possible change points, run the same function for the
#segment of time series after value 13.
## End(Not run)
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