scholarly journals The Effects of Omitted Variable on Multicollinearity in Hierarchical Linear Modelling

2021 ◽  
pp. 1-13
Author(s):  
V. G. Jemilohun
Keyword(s):  
Linear Model ◽  
Hierarchical Linear Model ◽  
Variance Inflation Factor ◽  
Variable Model ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Correct Form ◽  
Inflation Factor ◽  
Variable Bias ◽  
The Impact

This study investigates the impact of violation of the assumption of the hierarchical linear model where covariate of level – 1 collinear with the correct functional and omitted variable model. This was carried out via Monte Carlo simulation. In an attempt to achieve this omitted variable bias was introduced. The study considers the multicollinearity effects when the models are in the correct form and when they are not in the correct form.  Also, multicollinearity test was carried out on the data set to find out whether there is presence of multicollinearity among the data set using Variance Inflation Factor (VIF).  At the end of the study, the result shows that, omitted variable has tremendous impact on hierarchical linear model.

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.

An introduction to Monte Carlo simulations in criminal psychology: applications in evaluating biased estimators for recidivism

2015 ◽  
Vol 5 (2) ◽  
pp. 149-156 ◽  
Author(s):  
Priscillia Hunt ◽  
Jeremy N.V Miles
Keyword(s):  
Criminal Psychology ◽  
Statistical Tests ◽  
Actual Data ◽  
Content Type ◽  
Criminal Convictions ◽  
Omitted Variable Bias ◽  
Mc Simulation ◽  
Biased Estimators ◽  
Variable Bias ◽  
The Impact

Purpose – Studies in criminal psychology are inevitably undertaken in a context of uncertainty. One class of methods addressing such uncertainties is Monte Carlo (MC) simulation. The purpose of this paper is to provide an introduction to MC simulation for representing uncertainty and focusses on likely uses in studies of criminology and psychology. In addition to describing the method and providing a step-by-step guide to implementing a MC simulation, this paper provides examples using the Fragile Families and Child Wellbeing Survey data. Results show MC simulations can be a useful technique to test biased estimators and to evaluate the effect of bias on power for statistical tests. Design/methodology/approach – After describing MC simulation methods in detail, this paper provides a step-by-step guide to conducting a simulation. Then, a series of examples are provided. First, the authors present a brief example of how to generate data using MC simulation and the implications of alternative probability distribution assumptions. The second example uses actual data to evaluate the impact that omitted variable bias can have on least squares estimators. A third example evaluates the impact this form of heteroskedasticity can have on the power of statistical tests. Findings – This study shows MC simulated variable means are very similar to the actual data, but the standard deviations are considerably less in MC simulation-generated data. Using actual data on criminal convictions and income of fathers, the authors demonstrate the impact of omitted variable bias on the standard errors of the least squares estimator. Lastly, the authors show the p-values are systematically larger and the rejection frequencies correspondingly smaller in heteroskedastic error models compared to a model with homoskedastic errors. Originality/value – The aim of this paper is to provide a better understanding of what MC simulation methods are and what can be achieved with them. A key value of this paper is that the authors focus on understanding the concepts of MC simulation for researchers of statistics and psychology in particular. Furthermore, the authors provide a step-by-step description of the MC simulation approach and provide examples using real survey data on criminal convictions and economic characteristics of fathers in large US cities.

Endogenous democracy: causal evidence from the potato productivity shock in the old world

2020 ◽  
pp. 1-8
Author(s):  
Joan Barceló ◽  
Guillermo Rosas
Keyword(s):  
Economic Development ◽  
Causal Effect ◽  
Transition To Democracy ◽  
Sensitivity Analyses ◽  
Old World ◽  
Omitted Variable Bias ◽  
Productivity Shock ◽  
Variable Bias ◽  
The Impact ◽  
New Evidence

Abstract Despite a high cross-country correlation between development and democracy, it is difficult to gauge the impact of economic development on the probability that autocracies will transition to democracy because of endogeneity, especially due to reverse causation and omitted variable bias. Hence, whether development causes democracy remains a contested issue. We exploit exogeneity in the regional variation of potato cultivation along with the timing of the introduction of potatoes to the Old World (i.e., a potato productivity shock) to identify a causal effect of urbanization, a proxy for economic development, on democratization. Our results, which hold under sensitivity analyses that question the validity of the exclusion restriction, present new evidence of the existence of a causal effect of economic development on democracy.

scholarly journals Can Panel Data Methodologies Determine the Impact of Climate Change on Economic Growth?

2021 ◽  
Author(s):  
Richard A. Rosen ◽  
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Panel Data ◽  
Regression Equations ◽  
Annual Variations ◽  
Omitted Variable Bias ◽  
The World ◽  
Variable Bias ◽  
One Year ◽  
The Impact

Several major papers have been published over the last ten years claiming to have detected the impact of either annual variations in weather or climate change on the GDPs of most countries in the world using panel data-based statistical methodologies. These papers rely on various multivariate regression equations which include the annual average temperatures for most countries in the world as one or more of the independent variables, where the usual dependent variable is the change in annual GDP for each country from one year to the next year over 30-50 year time periods. Unfortunately, the quantitative estimates derived in these papers are misleading because the equations from which they are calculated are wrong. The major reason the resulting regression equations are wrong is because they do not include any of the appropriate and usual economic factors or variables which are likely to be able to explain changes in GDP/economic growth whether or not climate change has already impacted each country’s economy. These equations, in short, exhibit suffer from “omitted variable bias,” to use statistical terminology.

Firm-specific corporate governance and analysts’ earnings forecast characteristics

2018 ◽  
Vol 26 (3) ◽  
pp. 335-361 ◽  
Author(s):  
Minna Yu ◽  
Yanming Wang
Keyword(s):  
Corporate Governance ◽  
Stock Markets ◽  
Earnings Forecasts ◽  
Corporate Management ◽  
Forecast Errors ◽  
Content Type ◽  
Future Performance ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
The Impact

Purpose The purpose of this paper is to examine the impact of corporate governance on the capital market participants’ abilities to forecast future performance, as measured by the properties of analysts’ earnings forecasts in Asian stock markets. Design/methodology/approach This paper hypothesizes that higher corporate governance is associated with lower forecast errors, lower forecast dispersion and lower forecast revision volatility. Findings These predictions are supported with a sample of companies across eleven Asian economies over 2004-2012. The results of this paper suggest that corporate governance plays a significant role in the predictability of firm’s future performance and, therefore, improves the financial environment in Asian stock markets. Furthermore, the impact of corporate governance on analysts’ forecast properties is more pronounced in countries with strong investor protection. Research/limitations/implications The authors acknowledge the following limitations of this paper. First, the results of this paper may be subject to omitted-variable bias and endogeneity issue. The authors have used control variables in the regressions to reduce the omitted variable bias. The authors have run lead-lag regressions to address causality issue. Second, CLSA corporate governance scores are collected for largest companies in each jurisdiction. Therefore, the sample is biased towards the largest companies in those jurisdictions and may not be representative of the average firm in the Asia. Originality/value The results of this paper speak to the benefit of having strong corporate governance in terms of reducing the information asymmetry between investors and corporate management.

scholarly journals The Impact of Lagging-Region Status on District Poverty in Indonesia

2016 ◽  
Vol 62 (1) ◽  
pp. 30
Author(s):  
Rus’an Nasrudin
Keyword(s):  
Panel Data ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Selection Bias ◽  
Unobserved Heterogeneity ◽  
Poverty Rate ◽  
Fixed Effect ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
The Impact

Reducing subnational imbalances of development progress is unquestionable policy for heterogeneous Indonesia. This paper examines the impact of policy that assigns a lagging-region status namely status daerah tertinggal (DT) on poverty rate and poverty gap among districts in Indonesia in the two period of SBY presidency. The panel data fixed effect combined with propensity score matching is used to tackle the selection bias due to the nature of the policy, unobserved heterogeneity and omitted variable bias. The results show that the lagging-region status that was aimed to mainstream central and district’s budget toward lagging regions statistically significant reduces poverty rate and poverty gap in the period. The DT status, on average is associated with 0.75 percentage point of reduction in the poverty rate and 7% reduction in the poverty gap index. AbstrakMenurunkan ketimpangan antar-daerah adalah sebuah agenda kebijakan yang niscaya untuk Indonesia yang majemuk dalam kemajuan ekonomi. Artikel ini berusaha mengukur dampak dari sebuah kebijakan penetapan daerah tertinggal terhadap dua ukuran kemiskinan, yaitu tingkat kemiskinan dan kedalaman kemiskinan pada dua periode masa jabatan Presiden SBY. Metode yang dipergunakan adalah panel data fixed-effect dikombinasikan dengan propensity score matching untuk mengatasi permasalah endogen pada variabel utama yaitu bias dalam seleksi terhadap kebijakan, keragaman daerah yang tidak dapat diukur, dan potensi bias karena ketiadaan variabel-variabel yang berpengaruh terhadap dua ukuran kemiskinan. Hasil pendugaan regresi tersebut menunjukkan bahwa penetapan daerah tertinggal yang ditujukan untuk mengarusutamakan dana pembangunan secara statistik signifikan dan menyebabkan penurunan tingkat kemiskinan dan kedalaman kemiskinan di masa tersebut. Daerah tertinggal secara rata-rata memiliki tingkat kemiskinan lebih rendah sebesar 0.75 (persentase) dan memiliki indeks kedalaman kemiskinan 7% lebih rendah.Kata kunci: Daerah Tertinggal; Kemiskinan; IndonesiaJEL classifications: I32, P48

Housing features and rent: estimating the microstructures of rental housing

2019 ◽  
Vol 12 (2) ◽  
pp. 210-225 ◽  
Author(s):  
Hayato Nishi ◽  
Yasushi Asami ◽  
Chihiro Shimizu
Keyword(s):  
Design Methodology ◽  
Rental Housing ◽  
Hedonic Model ◽  
Traditional Media ◽  
Data Set ◽  
Content Type ◽  
Unique Data ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
Hedonic Approach

Purpose While consumers did not previously have information on detailed housing features via traditional media, such as magazines, nowadays, due to the progress in information technology, they can access detailed information on various housing features via housing information websites. Therefore, detailed housing features may affect current rents to some extent. This paper aims to identify the effects of detailed housing features on rent and on omitted variable bias in Tokyo, Japan. Design/methodology/approach This paper applies the hedonic approach. To identify the effects of features which are not observed previously, we use a unique data set that contains various housing features and over 200,000 housing units. This data set enables to simulate the situations when the researcher cannot get some variables, and this simulation shows which variables cause omitted variable bias. Findings The analysis shows that housing features significantly influence housing rent. If significant housing feature variables are not included in the hedonic model, the estimated coefficients show omitted variable bias. Additionally, unit-specific features such auto-locking door can cause omitted variable bias on location-specific features such accessibility to downtown. Originality/values This paper shows empirical evidence that detailed housing features can cause omitted variable bias on other features including variables which are often used in previous searches. The result from our unique data set can be a guide for variable selection to reduce omitted variable bias.

Effects of personality on the efficacy of judgmental adjustments of statistical forecasts

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cuneyt Eroglu ◽  
Nada R. Sanders
Keyword(s):  
Locus Of Control ◽  
Forecast Accuracy ◽  
Hierarchical Linear Model ◽  
Large Data ◽  
Openness To Experience ◽  
Organizational Setting ◽  
Data Set ◽  
Content Type ◽  
Judgmental Adjustments ◽  
The Impact

PurposeThe purpose of this paper is to investigate the effects of personality dimensions (conscientiousness, neuroticism, extraversion, agreeableness, openness to experience, locus of control) on the efficacy of judgmental adjustments of statistical forecasts.Design/methodology/approachThis paper uses a two-level hierarchical linear model to analyze a large data set obtained from an organizational setting (a quick service restaurant chain) that includes 3,812 judgmental adjustments of sales forecasts made by 112 store managers.FindingsThe results indicate that the average forecast accuracy improves as a result of judgmental adjustments, but performance of individual forecasters varies considerably based on their personality. Specifically, the trait of openness to experience tends to improve forecast accuracy while extraversion and external locus of control have negative effects.Originality/valueIntegration of human judgment with analytics algorithms is a major challenge for organizations. Documenting the impact of these traits on forecast accuracy opens the door for forecasting support system design, training, personnel selection and correction strategies that can be applied to judgmental adjustments.

scholarly journals A Reassessment of the Relationship Between Inequality and Growth

2000 ◽  
Vol 90 (4) ◽  
pp. 869-887 ◽  
Author(s):  
Kristin J Forbes
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Negative Relationship ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Specific Effects ◽  
Variable Bias ◽  
Time Invariant ◽  
The Relationship ◽  
Country Specific

This paper challenges the current belief that income inequality has a negative relationship with economic growth. It uses an improved data set on income inequality which not only reduces measurement error, but also allows estimation via a panel technique. Panel estimation makes it possible to control for time-invariant country-specific effects, therefore eliminating a potential source of omitted-variable bias. Results suggest that in the short and medium term, an increase in a country's level of income inequality has a significant positive relationship with subsequent economic growth. This relationship is highly robust across samples, variable definitions, and model specifications. (JEL O40, O15, E25)

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