A Comprehensive Spatiotemporal Framework for Hedonic Pricing: Integrating the Comparable Sales Approach and Minimizing Spatial Omitted Variable Bias

2019 ◽  
Author(s):  
Sotirios Thanos ◽  
Jean Dubé ◽  
Diègo Legros
Author(s):  
Dennis Guignet ◽  
Jonathan Lee

Hedonic pricing methods have become a staple in the environmental economist’s toolkit for conducting nonmarket valuation. The hedonic pricing method (HPM) is a revealed preference approach used to indirectly infer the value buyers and sellers place on characteristics of a differentiated product. Environmental applications of the HPM are typically focused on housing and labor markets, where the characteristics of interest are local environmental commodities and health risks. Despite the fact that there have been thousands of hedonic pricing studies published, applications of the methodology still often grapple with issues of omitted variable bias, measurement error, sample selection, choice of functional form, effect heterogeneity, and the recovery of policy-relevant welfare estimates. Advances in empirical methodologies, increased quality and quantity of data, and efforts to link empirical results to economic theory will surely further the use of the HPM as an important nonmarket valuation tool.


2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.


2015 ◽  
Vol 18 (4) ◽  
pp. 376-387
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel

How important is recruiting to a football program’s success? While prior research has attempted to answer this question, we utilize an extensive panel set covering 13 years of games along with a two-stage least squares approach to investigate the effects of recruiting on team success. This article also includes new control variables to account for omitted variable bias that prior work may have missed. We also split our sample to investigate whether recruiting displays heterogeneous effects across schools. Additionally, we find evidence that the benefits of recruiting are driven by team-specific effects, indicating that team success may be more heavily derived from the ability of teams to harness and improve their recruits than their ability to utilize each athlete’s raw abilities. This leads to important revelations regarding future research into both the value of recruits and what drives a football team’s success.


2003 ◽  
Vol 184 ◽  
pp. 99-110 ◽  
Author(s):  
Thomas Zwick

This paper finds substantial effects of ICT investments on productivity for a large and representative German establishment panel data set. In contrast to the bulk of the literature also establishments without ICT capital are included and lagged effects of ICT investments are analysed. In addition, a broad range of establishment and employee characteristics are taken account of in order to avoid omitted variable bias. It is shown that taking into account unobserved heterogeneity of the establishments and endogeneity of ICT investments increases the estimated lagged productivity impact of ICT investments.


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