So far, the dependent variable has been modeled as a linear function of the explanatory variables plus an additive error term. In this section, we will look at nonlinear models. It turns out that we have two types of linearity in the linear regression model. First, the dependent variable is linear in the explanatory variables. Second, the dependent variable is linear in the beta parameters. Therefore, we can consider two types of non-linearity. In this section, we will focus mainly on nonlinearity in the explanatory variables retaining linearity in the parameters. We will then look at the most common nonlinear models, the log-log model, the loglinear model and a model where we only log (some of) the x variables. Once we have an nonlinear model, we need to reinterpret the beta parameters. For example, in the log-log model, the beta parameters will be elasticities. Choosing between a linear regression model and a model nonlinear in the explanatory variables can be difficult. To help us in this choice, we introduce Ramsey’s RESET test.