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Posts Tagged ‘nonparametric regression’

## Exploring results of nonparametric regression models

In his blog post, Enrique Pinzon discussed how to perform regression when we don’t want to make any assumptions about functional form—use the npregress command. He concluded by asking and answering a few questions about the results using the margins and marginsplot commands.

Recently, I have been thinking about all the different types of questions that we could answer using margins after nonparametric regression, or really after any type of regression. margins and marginsplot are powerful tools for exploring the results of a model and drawing many kinds of inferences. In this post, I will show you how to ask and answer very specific questions and how to explore the entire response surface based on the results of your nonparametric regression.

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## 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…