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Nonparametric estimation in economics: Bayesian and frequentist approaches

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We review Bayesian and classical approaches to nonparametric density and regression estimation and illustrate how these techniques can be used in economic applications. On the Bayesian side, density estimation is illustrated via finite Gaussian mixtures and a Dirichlet Process Mixture Model, while nonparametric regression is handled using priors that impose smoothness. From the frequentist perspective, kernel‐based nonparametric regression techniques are presented for both density and regression problems. Both approaches are illustrated using a wage dataset from the Current Population Survey. WIREs Comput Stat 2017, 9:e1406. doi: 10.1002/wics.1406

This article is categorized under:

  • Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
  • Statistical and Graphical Methods of Data Analysis > Density Estimation
  • Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Kernel estimated conditional mean function relating age to log hourly earnings (left panel); kernel estimated marginal effect (gradient) of age on earnings (right panel).
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Density estimates of log hourly wages by gender—finite mixture and Dirichlet process mixture model results.
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Kernel density estimates of log hourly wages by gender—using a Gaussian kernel and bandwidths equal to 1.06σxn−1/5.
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Posterior mean of the regression function relating age to log hourly earnings (left panel); posterior mean of the marginal effect (gradient) of age on earnings (right panel).
[ Normal View | Magnified View ]

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Statistical and Graphical Methods of Data Analysis > Nonparametric Methods
Statistical and Graphical Methods of Data Analysis > Density Estimation
Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory

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