## Nonparametric regression: Like parametric regression, but not

**Initial thoughts**

Nonparametric regression is similar to linear regression, Poisson regression, and logit or probit regression; it predicts a mean of an outcome for a set of covariates. If you work with the parametric models mentioned above or other models that predict means, you already understand nonparametric regression and can work with it.

The main difference between parametric and nonparametric models is the assumptions about the functional form of the mean conditional on the covariates. Parametric models assume the mean is a known function of \(\mathbf{x}\beta\). Nonparametric regression makes no assumptions about the functional form.

In practice, this means that nonparametric regression yields consistent estimates of the mean function that are robust to functional form misspecification. But we do not need to stop there. With **npregress**, introduced in Stata 15, we may obtain estimates of how the mean changes when we change discrete or continuous covariates, and we can use **margins** to answer other questions about the mean function.

Below I illustrate how to use **npregress** and how to interpret its results. As you will see, the results are interpreted in the same way you would interpret the results of a parametric model using **margins**. Read more…