In econometrics and statistics, a **structural break** is an unexpected change over time in the parameters of regression models, which can lead to huge forecasting errors and unreliability of the model in general.^{[1]}^{[2]}^{[3]} This issue was popularised by David Hendry, who argued that lack of stability of coefficients frequently caused forecast failure, and therefore we must routinely test for structural stability. Structural stability − i.e., the time-invariance of regression coefficients − is a central issue in all applications of linear regression models.^{[4]}

For linear regression models, the Chow test is often used to test for a single break in mean at a known time period *K* for *K* ∈ [1,*T*].^{[5]}^{[6]} This test assesses whether the coefficients in a regression model are the same for periods [1,2, ...,*K*] and [*K* + 1, ...,*T*].^{[6]}

Other challenges occur where there are:

- Case 1: a known number of breaks in mean with unknown break points;
- Case 2: an unknown number of breaks in mean with unknown break points;
- Case 3: breaks in variance.

The Chow test is not applicable in these situations, since it only applies to models with a known breakpoint and where the error variance remains constant before and after the break.^{[7]}^{[5]}^{[6]} Bayesian methods exist to address these difficult cases via Markov chain Monte Carlo inference.^{[8]}^{[9]}

In general, the CUSUM (cumulative sum) and CUSUM-sq (CUSUM squared) tests can be used to test the constancy of the coefficients in a model. The bounds test can also be used.^{[6]}^{[10]} For cases 1 and 2, the **sup-Wald** (i.e., the supremum of a set of Wald statistics), **sup-LM** (i.e., the supremum of a set of Lagrange multiplier statistics), and **sup-LR** (i.e., the supremum of a set of likelihood ratio statistics) tests developed by Andrews (1993, 2003) may be used to test for parameter instability when the number and location of structural breaks are unknown.^{[11]}^{[12]} These tests were shown to be superior to the CUSUM test in terms of statistical power,^{[11]} and are the most commonly used tests for the detection of structural change involving an unknown number of breaks in mean with unknown break points.^{[4]} The sup-Wald, sup-LM, and sup-LR tests are asymptotic in general (i.e., the asymptotic critical values for these tests are applicable for sample size *n* as *n* → ∞),^{[11]} and involve the assumption of homoskedasticity across break points for finite samples;^{[4]} however, an exact test with the sup-Wald statistic may be obtained for a linear regression model with a fixed number of regressors and independent and identically distributed (IID) normal errors.^{[11]} A method developed by Bai and Perron (2003) also allows for the detection of multiple structural breaks from data.^{[13]}

The **MZ test** developed by Maasoumi, Zaman, and Ahmed (2010) allows for the simultaneous detection of one or more breaks in both mean and variance at a *known* break point.^{[4]}^{[14]} The **sup-MZ test** developed by Ahmed, Haider, and Zaman (2016) is a generalization of the MZ test which allows for the detection of breaks in mean and variance at an *unknown* break point.^{[4]}

For a cointegration model, the Gregory–Hansen test (1996) can be used for one unknown structural break,^{[15]} the Hatemi–J test (2006) can be used for two unknown breaks^{[16]} and the Maki (2012) test allows for multiple structural breaks.

There are many statistical packages that can be used to find structural breaks, including R,^{[17]} GAUSS, and Stata, among others. For example, a list of R packages for time series data is summarized at the changepoint detection section of the Time Series Analysis Task View,^{[18]} including both classical and Bayesian methods.^{[19]}^{[9]}