># car ライブラリを用いる場合
> mdata <-read.table("ch.csv", header=T, sep=",")
> year<-mdata[,1]
> ch  <-mdata[,2]
> ydh <-mdata[,3]
> wh  <-mdata[,4]
> d   <-as.numeric(year>=2001)
> dydh = d*ydh
> dwh  = d*wh
> m1 <-lm(ch~ydh+wh+d+dydh+dwh)
> summary(m1)

Call:
lm(formula = ch ~ ydh + wh + d + dydh + dwh)

Residuals:
    Min      1Q  Median      3Q     Max 
-2689.3 -1425.1  -887.8   792.0  6183.0 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  2.866e+04  1.424e+05   0.201    0.843
ydh          6.070e-01  6.010e-01   1.010    0.328
wh           4.440e-02  2.715e-02   1.635    0.122
d            3.197e+04  1.519e+05   0.211    0.836
dydh        -7.909e-02  6.400e-01  -0.124    0.903
dwh         -1.002e-03  2.859e-02  -0.035    0.972

Residual standard error: 2686 on 16 degrees of freedom
Multiple R-squared:  0.9751,    Adjusted R-squared:  0.9674 
F-statistic: 125.5 on 5 and 16 DF,  p-value: 2.993e-12

> library(car)
> m2 <-lm(ch~ydh+wh)
> anova(m1,m2)
Analysis of Variance Table

Model 1: ch ~ ydh + wh + d + dydh + dwh
Model 2: ch ~ ydh + wh
  Res.Df       RSS Df Sum of Sq     F  Pr(>F)  
1     16 115403795                             
2     19 208533635 -3 -93129840 4.304 0.02091 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

 



># strucchange ライブラリを用いる場合

> mdata <-read.table("ch.csv", header=T, sep=",")
> library(strucchange)
> ch <-mdata[,2]
> ydh <-mdata[,3]
> wh <-mdata[,4]
> sctest(ch~ydh+wh, type = "Chow", point = 7)

        Chow test

data:  ch ~ ydh + wh
F = 4.304, p-value = 0.02091