<p>For a multivariate regression:</p>

<p>If you're testing for a break at a known date, say at a historic event or change in policy regime, I would recommend that you use a Chow test (a special case of an F-test). This will give you an idea of whether the coefficients are constant on each side of the proposed break.</p>

<p>On the other hand, if you want to test for a break at an unknown date, you have three options. You could try a Quandt, Mann-Wald, or Andrews-Ploberger statistic. The choice is up to you, but I like Mann-Wald. To me, it's more intuitive than the others because it's basically the Mean Value Theorem evaluated using a Riemann sum of the F-test at each potential break. Of course, the asymptotic distributions necessary to use any of these methods are beyond my knowledge, but you don't need those if you're modeling in a nice software package with pre-loaded diagnostic tests. </p>

<p>Lastly, testing for "breaks" (as in multiple breaks) is a computational nightmare. You probably have deeper issues with your model (like omitted variables or some sort of misspecification) if you need multiple breaks.</p>

CC BY-SA 3.0
2016-09-26T22:51:34.823Z
2016-09-29T00:32:27.040Z
2016-09-29T00:32:27.040Z
Dawson A. Verley
<p>Impulse indicator saturation (IIS) <a href="http://www.sciencedirect.com/science/article/pii/S0165176507001358" rel="nofollow">(Santos, 2008)</a> and step indicator saturation (SIS) <a href="http://www.economics.ox.ac.uk/materials/papers/12760/paper658.pdf" rel="nofollow">(Doornik et al., 2013)</a> are relatively new and powerful methods for change/break detections. They are used, for example, in the <a href="https://www.doornik.com/pcgive/index.html?content=https://www.doornik.com/pcgive/pcgiveup.html" rel="nofollow">newer versions</a> of the automated model selection procedure "Autometrics" by Doornik (et al.?).</p>

<p><strong>References:</strong></p>

<ul>
<li>Santos, Carlos. "Impulse saturation break tests." <em>Economics Letters</em> 98.2 (2008): 136-143.</li>
<li>Doornik, Jurgen A., David F. Hendry, and Felix Pretis. "Step-indicator saturation." <em>Dicussion Paper</em> 658 (2013).</li>
</ul>

CC BY-SA 3.0
2016-10-17T19:26:42.793Z
2016-10-17T19:26:42.793Z
Richard Hardy
<p>Are you testing for just whether or not there are any breaks at all in the data, or are you testing if there is a break/anomaly at a specific point in time that you believe one exists?  If it is the latter you could include a dummy for that point and test its significance...</p>

CC BY-SA 3.0
2016-09-26T12:48:35.943Z
2016-09-26T12:48:35.943Z
bappers2
<p>What tests do you recommend for finding breaks in the following time series: log of inward FDI, log of nominal GDP, log of outward FDI from 1980 to 2012?</p>

<macroeconomics><econometrics>
Recommended tests do detect breaks in time series
MCF
# Recommended tests do detect breaks in time series

(Asked by: username_0 on 2016-09-09)

What tests do you recommend for finding breaks in the following time series: log of inward FDI, log of nominal GDP, log of outward FDI from 1980 to 2012?

**Tags:** macroeconomics, econometrics

---

## 3 answers

### Answer by username_1 on 2016-09-26. Score: 4

For a multivariate regression:

If you're testing for a break at a known date, say at a historic event or change in policy regime, I would recommend that you use a Chow test (a special case of an F-test). This will give you an idea of whether the coefficients are constant on each side of the proposed break.

On the other hand, if you want to test for a break at an unknown date, you have three options. You could try a Quandt, Mann-Wald, or Andrews-Ploberger statistic. The choice is up to you, but I like Mann-Wald. To me, it's more intuitive than the others because it's basically the Mean Value Theorem evaluated using a Riemann sum of the F-test at each potential break. Of course, the asymptotic distributions necessary to use any of these methods are beyond my knowledge, but you don't need those if you're modeling in a nice software package with pre-loaded diagnostic tests.

Lastly, testing for "breaks" (as in multiple breaks) is a computational nightmare. You probably have deeper issues with your model (like omitted variables or some sort of misspecification) if you need multiple breaks.


---

### Answer by username_2 on 2016-10-17. Score: 2

Impulse indicator saturation (IIS) [(Santos, 2008)](http://www.sciencedirect.com/science/article/pii/S0165176507001358) and step indicator saturation (SIS) [(Doornik et al., 2013)](http://www.economics.ox.ac.uk/materials/papers/12760/paper658.pdf) are relatively new and powerful methods for change/break detections. They are used, for example, in the [newer versions](https://www.doornik.com/pcgive/index.html?content=https://www.doornik.com/pcgive/pcgiveup.html) of the automated model selection procedure "Autometrics" by Doornik (et al.?).

**References:**

* Santos, Carlos. "Impulse saturation break tests." *Economics Letters* 98.2 (2008): 136-143.
* Doornik, Jurgen A., David F. Hendry, and Felix Pretis. "Step-indicator saturation." *Dicussion Paper* 658 (2013).


---

### Answer by username_3 on 2016-09-26. Score: 1

Are you testing for just whether or not there are any breaks at all in the data, or are you testing if there is a break/anomaly at a specific point in time that you believe one exists? If it is the latter you could include a dummy for that point and test its significance...


---

