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, 2018) 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.
Kleiber, C., & Zeileis, A. (2018). AER: Applied Econometrics with R (Version 1.2-6). 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