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# 8 Nonlinear Regression Functions

Until now we assumed the regression function to be linear, i.e., we have treated the slope parameter of the regression function as a constant. This implies that the effect on $Y$ of a one unit change in $X$ does not depend on the level of $X$. If, however, the effect of a change in $X$ on $Y$ does depend on the value of $X$, we should use a nonlinear regression function.

Just like for the previous chapter, the packages AER (Kleiber & Zeileis, 2017) and stargazer (Hlavac, 2018) are required for reproduction of the code presented in this chapter. Check whether the code chunk below executes without any error messages.

library(AER)
library(stargazer)

### References

Kleiber, C., & Zeileis, A. (2017). AER: Applied Econometrics with R (Version 1.2-5). Retrieved from https://CRAN.R-project.org/package=AER

Hlavac, M. (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables (Version 5.2.2). Retrieved from https://CRAN.R-project.org/package=stargazer