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?