Nonparametric estimation in economics: Bayesian and frequentist approaches

© 2017 Wiley Periodicals, Inc. 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. For further resources related to this article, please visit the WIREs website

Estimation and inference under economic restrictions

Estimation of economic relationships often requires imposition of constraints such as positivity or monotonicity on each observation. Methods to impose such constraints, however, vary depending upon the estimation technique employed. We describe a general methodology to impose (observation-specific) constraints for the class of linear regression estimators using a method known as constraint weighted bootstrapping. While this method has received attention in the nonparametric regression literature, we show how it can be applied for both parametric and nonparametric estimators. A benefit of this method is that imposing numerous constraints simultaneously can be performed seamlessly. We apply this method to Norwegian dairy farm data to estimate both unconstrained and constrained parametric and nonparametric models

Is newer always better? A reinvestigation of productivity dynamics using updated PWT data

Understanding the drivers of productivity remains one of the most sought after phenomena in economics. The ability to create produce more from less resources is undoubtedly appealing. Using recently updated Penn World Table data, we investigate to what degree previous results using a popular productivity decomposition are maintained. We find that, contrary to conclusions from earlier work, technical efficiency (catching up) played a more pronounced role in the global increase in productivity over the 1965–1990 period. We also find a larger effect for technical change than earlier work and a far lesser role for capital deepening. This suite of results augurs the coming information age that placed less weight on physical capital to create and sustain wealth. Taken together our findings here suggest that as data collection, its quality and evaluation methods evolve, so too will our understanding of productivity dynamics

A new mathematical model for the efficiency calculation

During the past sixty years, a lot of effort has been made regarding the productive efficiency. Such endeavours provided an extensive bibliography on this subject, culminating in two main methods, named the Stochastic Frontier Analysis (parametric) and Data Envelopment Analysis (non-parametric). The literature states this methodology also as the benchmark approach, since the techniques compare the sample upon a chosen “more-efficient” reference. This article intends to disrupt such premise, suggesting a mathematical model that relies on the optimal input combination, provided by a differential equation system instead of an observable sample. A numerical example is given, illustrating the application of our model’s features.publishe

Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna

Hedonic regression, Submarkets, Multiplicative spatial scaling factors, Semiparametric models, P-splines,