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Our data set hsb2 consists of high school student scores on various tests and their demographical information. Let's say our model is regression model of writing scores on math and reading scores. Furthermore we want to test if the regression model will be different for boys and girls. In other words, we want to test if the same regression coefficients apply to both boys and girls in the data set or there are two subsets with different intercepts and slopes. We will use Chow test for this purpose.
Since Chow test is mostly used in time series, SAS has included it with proc autoreg. The way to specify the two subsets is to specify the breakpoint in terms of the position of the observations. In this example, we use proc freq to identify the position for the breakpoint and we then have to sort the data accordingly.
proc freq data = hsb2; tables female; run;The FREQ ProcedureCumulative Cumulative FEMALE Frequency Percent Frequency Percent ----------------------------------------------------------- 0 91 45.50 91 45.50 1 109 54.50 200 100.00proc sort data = hsb2; by female; run; proc autoreg data = hsb2; model write = math read /chow = 91; run;Dependent Variable WRITEOrdinary Least Squares EstimatesSSE 9938.81034 DFE 197 MSE 50.45081 Root MSE 7.10287 SBC 1364.64741 AIC 1354.75246 Regress R-Square 0.4441 Total R-Square 0.4441 Durbin-Watson 1.6662Structural Change TestBreak Test Point Num DF Den DF F Value Pr > FChow 91 3 194 11.84 <.0001Standard Approx Variable DF Estimate Error t Value Pr > |t|Intercept 1 15.5339 3.0180 5.15 <.0001 MATH 1 0.4005 0.0717 5.58 <.0001 READ 1 0.3094 0.0655 4.72 <.0001
The middle section of the output above gives the Chow Test, and the rest is just the regression model for the entire sample including both boys and girls. The Chow test indicates that there is a structural difference for boys and girls. Now let's run the regression models separately.
proc reg data = hsb2; by female; model write = math read ; run; quit;FEMALE=0Parameter Standard Variable DF Estimate Error t Value Pr > |t|Intercept 1 7.33165 4.60342 1.59 0.1148 MATH 1 0.39321 0.10066 3.91 0.0002 READ 1 0.41592 0.09259 4.49 <.0001FEMALE=1Parameter EstimatesParameter Standard Variable DF Estimate Error t Value Pr > |t|Intercept 1 21.07310 3.37071 6.25 <.0001 MATH 1 0.41966 0.08719 4.81 <.0001 READ 1 0.23061 0.07933 2.91 0.0044
Here is the link to a SAS example page on Chow Test. It explains in some detail the assumptions for Chow test and the formula for Chow statistic: SAS Examples: Chow Test for Structural Breaks.
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