Example 1.2 Keynes’s Consumption Function
library(AER)
df <- data.frame(read.csv("data/tablef1_1.csv", fileEncoding="UTF-8-BOM", header=TRUE))
plot(C ~ X, data = df, pch = 19, col="blue", main="FIGURE 1.1. Aggregate U.S. Consumption and Income Data, 2000 - 2009.", xlab="Personal Income", ylab="Personal Consumption",
xlim=c(8500, 12500), ylim=c(6500, 10500))
summary(fm <- lm(C ~ X, data=df))
##
## Call:
## lm(formula = C ~ X, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -137.690 -85.760 -3.589 89.805 150.412
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -587.18549 271.34172 -2.164 0.0624 .
## X 0.88269 0.02594 34.023 6.09e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109.4 on 8 degrees of freedom
## Multiple R-squared: 0.9931, Adjusted R-squared: 0.9923
## F-statistic: 1158 on 1 and 8 DF, p-value: 6.085e-10
abline(fm, col="red")
text(C ~ X, data = df, labels=Year, pch = 19, cex = .9, pos = 3, font=3)
df <- data.frame(read.csv("data/tablef1_1.csv", fileEncoding="UTF-8-BOM", header=TRUE))
APC <- df$C/df$X
plot(APC ~ X, data = df, pch = 19, col="blue", main="Is d(C/X)/dX < 0 ?", xlab="Income", ylab="APC")
summary(fm2 <- lm(APC ~ X, data=df))
##
## Call:
## lm(formula = APC ~ X, data = df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0161764 -0.0084210 0.0002654 0.0094589 0.0153041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.628e-01 2.908e-02 26.231 4.79e-09 ***
## X 5.997e-06 2.781e-06 2.157 0.0631 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01172 on 8 degrees of freedom
## Multiple R-squared: 0.3677, Adjusted R-squared: 0.2887
## F-statistic: 4.652 on 1 and 8 DF, p-value: 0.0631
abline(fm2, col="red")
text(APC ~ X, data = df, labels=Year, pch = 19, cex = .9, pos = 3, font=3)
