The variable income (yearly) is examined in a regression setting where the predictor variable is lag (1) of income and the following output is produced.

a) Write down the regression equation.

b) Interpret the meaning of the slope.

c) A dummy variable for gender (male=0, female=1) was added to the model. Interpret its coefficient of -0.2.

us_change %>%

model(TSLM(log(Income) ~ log(LagIncome))) %>% report()

#> Series: Consumption

#> Model: TSLM #> #> Residuals:

#> Min 1Q Median 3Q Max

#> -2.5824 -0.2778 0.0186 0.3233 1.4223

#>

#> Coefficients:

#> Estimate Std. Error t value Pr(>|t|)

#> (Intercept) 0.5445 0.0540 10.08 < 2e-16 ***

#> Log(LagIncome) 1.1000 0.0467 5.82 2.4e-08 ***

#> ---

#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#>

#> Residual standard error: 0.591 on 196 degrees of freedom

#> Multiple R-squared: 0.147, Adjusted R-squared: 0.143

#> F-statistic: 33.8 on 1 and 196 DF, p-value: 2.4e-08