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Nonparametric Regression Techniques in Economics
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In this article, a brief overview of the class of models under study and central theoretical issues such as the curse of dimensionality, the bias-variance trade-off and rates of convergence are discussed.Abstract:
This introduction to nonparametric regression emphasizes techniques that might be most accessible and useful to the applied economist. The paper begins with a brief overview of the class of models under study and central theoretical issues such as the curse of dimensionality, the bias-variance trade-off and rates of convergence. The paper then focuses on kernel and nonparametric least squares estimation of the nonparametric regression model, and optimal differencing estimation of the partial linear model. Constrained estimation and hypothesis testing is also discussed. Empirical examples include returns to scale in electricity distribution and hedonic pricing of housing attributes.read more
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Causal Inference with General Treatment Regimes: Generalizing the Propensity Score
Kosuke Imai,David A. van Dyk +1 more
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Evaluating anti-poverty programs
TL;DR: This article reviewed the methods available for the ex-post counterfactual analysis of programs that are assigned exclusively to individuals, households or locations, and showed that no single method dominates; rigorous, policy-relevant evaluations should be open-minded about methodology.
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Simple and Bias-Corrected Matching Estimators for Average Treatment Effects
TL;DR: In this article, the authors develop a new framework to analyze the properties of matching estimators and establish a number of new results, such as the conditional bias term may not vanish at a rate faster than root-N when more than one continuous variable is used for matching.
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Chapter 16 Property Value Models
TL;DR: In this paper, the authors present several techniques that can be used to study the effects of environmental quality on property values and infer willingness to pay for improvements, including the hedonic model and the discrete choice models.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Book
Spline models for observational data
TL;DR: In this paper, a theory and practice for the estimation of functions from noisy data on functionals is developed, where convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a number of problems within this framework.