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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