scholarly journals The Age–Period–Cohort Problem in Hedonic House Prices Models

Econometrics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Chung-Yim Yiu ◽  
Ka-Shing Cheung
Keyword(s):  
New Zealand ◽  
House Prices ◽  
Identification Problem ◽  
External Information ◽  
Value Assessment ◽  
Ceteris Paribus ◽  
Omitted Variable Bias ◽  
Home Value ◽  
Variable Bias ◽  
Practical Implications

The age–period–cohort problem has been studied for decades but without resolution. There have been many suggested solutions to make the three effects estimable, but these solutions mostly exploit non-linear specifications. Yet, these approaches may suffer from misspecification or omitted variable bias. This paper is a practical-oriented study with an aim to empirically disentangle age–period–cohort effects by providing external information on the actual depreciation of housing structure rather than taking age as a proxy. It is based on appraisals of the improvement values of properties in New Zealand to estimate the age-depreciation effect. This research method provides a novel means of solving the identification problem of the age, period, and cohort trilemma. Based on about half a million housing transactions from 1990 to 2019 in the Auckland Region of New Zealand, the results show that traditional hedonic prices models using age and time dummy variables can result, ceteris paribus, in unreasonable positive depreciation rates. The use of the improvement values model can help improve the accuracy of home value assessment and reduce estimation biases. This method also has important practical implications for property valuations.

scholarly journals The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Peter M. Steiner ◽  
Yongnam Kim
Keyword(s):  
Linear Regression ◽  
Causal Inference ◽  
The Other ◽  
Simple Linear Regression ◽  
Reliable Measure ◽  
Confounding Bias ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
Highly Correlated ◽  
Practical Implications

AbstractCausal inference with observational data frequently requires researchers to estimate treatment effects conditional on a set of observed covariates, hoping that they remove or at least reduce the confounding bias. Using a simple linear (regression) setting with two confounders – one observed (X), the other unobserved (U) – we demonstrate that conditioning on the observed confounder X does not necessarily imply that the confounding bias decreases, even if X is highly correlated with U. That is, adjusting for X may increase instead of reduce the omitted variable bias (OVB). Two phenomena can cause an increasing OVB: (i) bias amplification and (ii) cancellation of offsetting biases. Bias amplification occurs because conditioning on X amplifies any remaining bias due to the omitted confounder U. Cancellation of offsetting biases is an issue whenever X and U induce biases in opposite directions such that they perfectly or partially offset each other, in which case adjusting for X inadvertently cancels the bias-offsetting effect. In this article we discuss the conditions under which adjusting for X increases OVB, and demonstrate that conditioning on X increases the imbalance in U, which turns U into an even stronger confounder. We also show that conditioning on an unreliably measured confounder can remove more bias than the corresponding reliable measure. Practical implications for causal inference will be discussed.

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

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.

scholarly journals Lock-In and Team Effects

2015 ◽  
Vol 18 (4) ◽  
pp. 376-387
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel
Keyword(s):  
Set Covering ◽  
Future Research ◽  
Prior Work ◽  
Omitted Variable Bias ◽  
Specific Effects ◽  
Heterogeneous Effects ◽  
Team Success ◽  
Variable Bias ◽  
Lock In ◽  
Least Squares Approach

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.

The Impact of ICT Investment on Establishment Productivity

2003 ◽  
Vol 184 ◽  
pp. 99-110 ◽  
Author(s):  
Thomas Zwick
Keyword(s):  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Lagged Effects ◽  
Variable Bias ◽  
Employee Characteristics ◽  
The Impact ◽  
Establishment Panel Data

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.

scholarly journals Tracking Underreported Financial Flows: China’s Development Finance and the Aid–Conflict Nexus Revisited

2015 ◽  
Vol 61 (5) ◽  
pp. 935-963 ◽  
Author(s):  
Austin M. Strange ◽  
Axel Dreher ◽  
Andreas Fuchs ◽  
Bradley Parks ◽  
Michael J. Tierney
Keyword(s):  
Open Source ◽  
Reliable Information ◽  
African Countries ◽  
Financial Flows ◽  
Development Finance ◽  
Alternative Funding ◽  
Omitted Variable Bias ◽  
Chinese Official ◽  
Variable Bias ◽  
Project Level

China’s provision of development finance to other countries is sizable but reliable information is scarce. We introduce a new open-source methodology for collecting project-level development finance information and create a database of Chinese official finance (OF) to Africa from 2000 to 2011. We find that China’s commitments amounted to approximately US$73 billion, of which US$15 billion are comparable to Official Development Assistance following Organization for Economic Cooperation and Development definitions. We provide details on 1,511 projects to fifty African countries. We use this database to extend previous research on aid and conflict, which suffers from omitted-variable bias due to the exclusion of Chinese development finance. Our results show that sudden withdrawals of “traditional” aid no longer induce conflict in the presence of sufficient alternative funding from China. Our findings highlight the importance of gathering more complete data on the development activities of “nontraditional donors” to better understand the link between aid and conflict.

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