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The '''Chow test''' is a [[statistical]] and [[econometric]] test of whether the coefficients in two [[linear regression]]s on different data sets are equal. The Chow test was invented by economist [[Gregory Chow]] in 1960. In econometrics, the Chow test is most commonly used in [[time series analysis]] to test for the presence of a [[structural break]]. In [[program evaluation]], the Chow test is often used to determine whether the independent variables have different impacts on different subgroups of the population.
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{| class="wikitable"
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!structural break
!program evaluation
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|-
|style="width:450px" valign="top" halign="center" |
[[File:Chow test structural break.png|440px]]
|style="width:450px" valign="top" halign="center"|
[[File:Chow test substructures.png|440px]]
|-
|valign="top"|
At  <math> x=1.7 </math> there is a structural break, regression on the subintervals <math>[0,1.7]</math> and <math>[1.7,4] </math> delivers a better modelling than the combined regression(dashed) over the whole interval.
|valign="top"|
Comparison of 2 different programs (red, green) existing in a common data set, separate regressions for both programs deliver a better modelling than a combined regression (black).
|}
 
Suppose that we model our data as
 
:<math>
y_t=a+bx_{1t} + cx_{2t} + \varepsilon.\,
</math>
 
If we split our data into two groups, then we have
 
:<math>
y_t=a_1+b_1x_{1t} + c_1x_{2t} + \varepsilon. \,</math>
 
and
 
:<math>
y_t=a_2+b_2x_{1t} + c_2x_{2t} + \varepsilon. \,
</math>
 
The [[null hypothesis]] of the Chow test asserts that <math>a_1=a_2</math>, <math>b_1=b_2</math>, and <math>c_1=c_2</math>, and there is the assumption that the [[errors and residuals in statistics|model errors]] <math> \varepsilon </math> are [[independent and identically distributed]] from a [[normal distribution]] with unknown [[variance]].
 
Let <math>S_C</math> be the sum of squared [[errors and residuals in statistics|residual]]s from the combined data, <math>S_1</math> be the sum of squared [[errors and residuals in statistics|residual]]s from the first group, and <math>S_2</math> be the sum of squared [[errors and residuals in statistics|residual]]s from the second group. <math>N_1</math> and <math>N_2</math> are the number of observations in each group and <math>k</math> is the total number of parameters (in this case, 3).  Then the Chow test statistic is
 
:<math>
\frac{(S_C -(S_1+S_2))/(k)}{(S_1+S_2)/(N_1+N_2-2k)}.
</math>
 
The test statistic follows the [[F distribution]] with <math>k</math> and <math>N_1+N_2-2k</math> [[degrees of freedom (statistics)|degrees of freedom]].
 
==References==
*{{cite journal | doi=10.2307/1910133 | first1=Gregory C.|last1=Chow | title=Tests of Equality Between Sets of Coefficients in Two Linear Regressions | jstor=1910133 | journal=Econometrica | year=1960 | volume=28 | pages=591–605 | issue=3}}
*{{cite book |last=Doran |first=Howard E. |year=1989 |title=Applied Regression Analysis in Econometrics |publisher=CRC Press |isbn=0-8247-8049-3 |page=146 }} ([http://books.google.de/books?id=JHzik5hkrIMC&pg=PA146&dq=Chow-Test+regression&lr=&as_brr=3&sig=ACfU3U1T_tmXj-sQbqDVHutF3RRPLFQTqg restricted online version (Google Books)])
*{{cite book |last=Dougherty |first=Christopher |year=2007 |title=Introduction to Econometrics |publisher=Oxford University Press |isbn=0-19-928096-7 |page=194 }} ([http://books.google.de/books?id=h0MTRO_3jYEC&pg=PA194&vq=chow+test&source=gbs_search_r&cad=1_1&sig=ACfU3U2ZXf7-rPaXyqzwpfAaEDtpqoy5MA restricted online version (Google Books)])
 
==External links==
*[http://www.stata.com/support/faqs/stat/chow.html Computing the Chow statistic],  [http://www.stata.com/support/faqs/stat/chow2.html Chow and Wald tests], [http://www.stata.com/support/faqs/stat/chow3.html Chow tests]: Series of FAQ explanations from the [[Stata]] Corporation at http://www.stata.com/support/faqs/
 
[[Category:Econometrics]]
[[Category:Time series analysis]]
[[Category:Statistical tests]]
[[Category:Regression diagnostics]]

Latest revision as of 23:20, 6 October 2014

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