Chow test

Suppose that we model our data as

${\displaystyle y_{t}=a+bx_{1t}+cx_{2t}+\varepsilon .\,}$

If we split our data into two groups, then we have

${\displaystyle y_{t}=a_{1}+b_{1}x_{1t}+c_{1}x_{2t}+\varepsilon \,}$

and

${\displaystyle y_{t}=a_{2}+b_{2}x_{1t}+c_{2}x_{2t}+\varepsilon .\,}$

The null hypothesis of the Chow test asserts that ${\displaystyle a_{1}=a_{2}}$, ${\displaystyle b_{1}=b_{2}}$, and ${\displaystyle c_{1}=c_{2}}$, and there is the assumption that the model errors ${\displaystyle \varepsilon }$ are independent and identically distributed from a normal distribution with unknown variance.

Let ${\displaystyle S_{C}}$ be the sum of squared residuals from the combined data, ${\displaystyle S_{1}}$ be the sum of squared residuals from the first group, and ${\displaystyle S_{2}}$ be the sum of squared residuals from the second group. ${\displaystyle N_{1}}$ and ${\displaystyle N_{2}}$ are the number of observations in each group and ${\displaystyle k}$ is the total number of parameters (in this case 3, i.e. 2 independent variables coefficients + intercept). Then the Chow test statistic is

${\displaystyle {\frac {(S_{C}-(S_{1}+S_{2}))/k}{(S_{1}+S_{2})/(N_{1}+N_{2}-2k)}}.}$

The test statistic follows the F-distribution with ${\displaystyle k}$ and ${\displaystyle N_{1}+N_{2}-2k}$ degrees of freedom.

The same result can be achieved via dummy variables.

Consider the two data sets which are being compared. Firstly there is the 'primary' data set i={1,...,${\displaystyle n_{1}}$} and the 'secondary' data set i={${\displaystyle n_{1}}$+1,...,n}. Then there is the union of these two sets: i={1,...,n}. If there is no structural change between the primary and secondary data sets a regression can be run over the union without the issue of biased estimators arising.

Consider the regression:

${\displaystyle y_{t}=\beta _{0}+\beta _{1}x_{1t}+\beta _{2}x_{2t}+...+\beta _{k}x_{kt}+\gamma _{0}D_{t}+\sum _{i=1}^{k}\gamma _{i}x_{it}D_{t}+\varepsilon _{t}.\,}$

Which is run over i={1,...,n}.

D is a dummy variable taking a value of 1 for i={${\displaystyle n_{1}}$+1,...,n} and 0 otherwise.

If both data sets can be explained fully by ${\displaystyle (\beta _{0},\beta _{1},...,\beta _{k})}$ then there is no use in the dummy variable as the data set is explained fully by the restricted equation. That is, under the assumption of no structural change we have a null and alternative hypothesis of:

${\displaystyle H_{0}:\gamma _{0}=0,\gamma _{1}=0,...,\gamma _{k}=0}$

${\displaystyle H_{1}:{\text{otherwise}}}$

The null hypothesis of joint insignificance of D can be run as an F-test with ${\displaystyle n-2(k+1)}$ degrees of freedom (DoF). That is: ${\displaystyle F={\frac {(RSS^{R}-RSS^{U})/(k+1)}{RSS^{U}/DoF}}}$.

Remarks

• The global sum of squares (SSE) is often called the Restricted Sum of Squares (RSSM) as we basically test a constrained model where we have ${\displaystyle 2k}$ assumptions (with ${\displaystyle k}$ the number of regressors).
• Some software like SAS will use a predictive Chow test when the size of a subsample is less than the number of regressors.

• Chow, Gregory C. (1960). "Tests of Equality Between Sets of Coefficients in Two Linear Regressions" (PDF). Econometrica. 28 (3): 591–605. doi:10.2307/1910133. JSTOR 1910133. S2CID 116311724. Archived from the original (PDF) on 2019-12-28.
• Doran, Howard E. (1989). Applied Regression Analysis in Econometrics. CRC Press. p. 146. ISBN 978-0-8247-8049-4.
• Dougherty, Christopher (2007). Introduction to Econometrics. Oxford University Press. p. 194. ISBN 978-0-19-928096-4.
• Kmenta, Jan (1986). Elements of Econometrics (Second ed.). New York: Macmillan. pp. 412–423. ISBN 978-0-472-10886-2.
• Wooldridge, Jeffrey M. (2009). Introductory Econometrics: A Modern Approach (Fourth ed.). Mason: South-Western. pp. 243–246. ISBN 978-0-324-66054-8.

• Computing the Chow statistic, Chow and Wald tests, Chow tests: Series of FAQ explanations from the Stata Corporation at https://www.stata.com/support/faqs/
• : Series of FAQ explanations from the SAS Corporation