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I wanted to do a sanity check.

I want to remove the effect of structural breaks on my series.

Therefore, I create a dummy with 1s for the period of structural change and 0s for the rest. (There might be several periods). Let's call it D

I then regress it on yt = b0 + b1D + ytStar, where ytStar is the series with no structural break effect.

Do you see any issues with that?

Kiril E. Proykov
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  • There might be a typo, your equation states you're regressing yt on yt. Also, you're using one dummy for all potential structural brakes? Why you believe they can all be modeled by the same variable? – Lucas Farias Feb 02 '19 at 21:20
  • Hi Lucas, thanks for the comment. Somehow the "*" from the equation was not printed, the ytStar is the error term that will contain the filtered series. I would like to model the structural changes in 1 variable to estimate the effect of a change in mean in general instead for a specific period. I have a lot of data (many time series) and I am using the model from Bai & Perron (2003) to detect the structural breaks. Beside the "structural breaks" I am also modelling trend, cycles and holidays. I am using the least square filtering to remove any deterministic effects from my data. – Kiril E. Proykov Feb 02 '19 at 22:33
  • If you post one of your time series I will try and help you form an ARMAX model that incorporates level/shift structure (one of the i'S) while dealing with pulses , time trends , seasonal pulses, arima structure, changes in model error variance. [![enter image description here](https://i.stack.imgur.com/iVFoO.png)](https://i.stack.imgur.com/iVFoO.png) See https://stats.stackexchange.com/questions/376222/advice-on-correcting-for-seasonality-in-data/376243#376243 – IrishStat Feb 03 '19 at 11:56
  • I do see possible flaws with your suggested approach as my answer is that you should consider using ARMAX model which can incorporate X's and their lags , ARIMA and pulses, level shifts,seasonal pulses and time trends. – IrishStat Feb 17 '19 at 22:21

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