Detecting for structural changes in a time series near the end My interest is to check for structural changes in a time series. I know the time point which I wish to check for structural break. This point happens to be near the end of the series. Also I am doing a univariate modeling on the series (so no other regressors other that it's own lags). 
Following is what I understood after browsing through the net:


*

*There are many tests to check for Structural breaks: Chow test, sup-Wald test, sup-LM test, sup-LR (Andrews test), etc. 

*The classical chow test can handle near the end problem but requires linearity of the model, exogenous regressors and normality of errors. Since auto.arima in R gives a (1,0,0)(1,0,0) model, two of these three requirements are not met.

*Some websites suggest to use Andrews test as this test can handle non-linearity and own-lagged regressors (Q1: would be great if someone can confirm this). Further, this test allows for automatic detection of break points. Q2: Can it detect only one breakpoint or many?

*Then I discovered that this test may not be good as it assumes large number of observations before and after the breakpoint. 

*Latest information that I have is that Andrews developed another test in 2003, which can handle all these problems. 
Q3: Is there a R package which has this latest test?
Q4: Can either of sup-Wald test or sup-LM test work for me?
Q5: Can someone give a brief idea of the tests above, particularly their assumptions, applicability and possibly a source to study theory behind them?
 A: "I know the time point which I wish to check for structural break."
A structural break can be due to 
1) an observation that is significantly different from the expected thus a pulse indicator would be approriate ....to identify this pulse follow http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html 
2) a sequence of recent errant observations can be suggested by the need for a level/step shift using the above reference . 
3) a sequence of recent errant observations can be suggested by a change in the model form testable by the 1960 CHOW test https://de.wikipedia.org/wiki/Chow-Test 
 for constant PARAMETERS across groups. This is different from the "typically available Chow test for a level shift in the expected value" which is dealt with 2)
4) a sequence of errant observations can be suggested by detecting deterministic change points in the model error variance using the same reference
Precisely which structural break are you after ?
Finally you say you know the break point thus this is de jure  . Often time there is a de facto break point which can differ from the presumed one requiring it to be found via analytics from the aforementioned reference. 
