Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data

2019 ◽  
Vol 46 (1) ◽  
pp. 70-99 ◽  
Author(s):  
Paul D. Bliese ◽  
Donald J. Schepker ◽  
Spenser M. Essman ◽  
Robert E. Ployhart
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Theory Development ◽  
Modeling Framework ◽  
Random Effects Models ◽  
Data Support ◽  
Fixed And Random Effects

Both macro- and micro-oriented researchers frequently use panel data where the outcome of interest is measured repeated times. Panel data support at least five different modeling frameworks (within, between, incremental/emergent, cross-level, and growth). Researchers from macro- and micro-oriented domains tend to differentially use the frameworks and also use different analytic tools and terminology when using the same modeling framework. These differences have the potential to inhibit cross-discipline communication. In this review, we explore how macro- and microresearchers approach panel data with a specific emphasis on the theoretical implications of choosing one framework versus another. We illustrate how fixed-effects and random-effects models differ and how they are similar, and we conduct a thorough review of 142 articles that used panel data in leading management journals in 2017. Ultimately, our review identifies ways that researchers can better employ fixed- and random-effects models, model time as a meaningful predictor or ensure unobserved time heterogeneity is controlled, and align hypotheses to analytic choice. In the end, our goal is to help facilitate communication and theory development between macro- and micro-oriented management researchers.

scholarly journals Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Symmetric Matrix ◽  
Panel Data Models ◽  
Random Effects Models ◽  
Panel Data Estimation ◽  
Quadratic Minimization ◽  
Fixed And Random Effects ◽  
Estimated Error ◽  
Data Estimation

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

Skipper skill and panel data in fishing industries

1999 ◽  
Vol 56 (11) ◽  
pp. 2011-2018 ◽  
Author(s):  
Dale Squires ◽  
James Kirkley
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Production Model ◽  
Fixed Effects Model ◽  
Managerial Ability ◽  
Trawl Fishery ◽  
The Pacific ◽  
Fixed And Random Effects ◽  
Catch Rates

Skipper skill or managerial ability plays a central role in the harvesting of fish and fishing power. Examining the influences of managerial ability on catch rates, however, may be complicated, since managerial ability is generally unobservable. Using panel data on production activities in the Pacific Coast trawl fishery, we examine the use of the fixed- and random-effects panel data models to depict managerial skill by intervessel differences, representing differences in technical efficiency. The random-effects production model is selected over the fixed-effects model. We conclude that skipper skill is more related to finding fish, dealing with unforseen events, and handling inclement weather than it is to managing the economic inputs.

scholarly journals Effects of dutasteride on prostate cancer incidence: A systematic review and meta-analysis

2020 ◽  
Vol 11 (4) ◽  
pp. 7266-7270
Author(s):  
Gyeong-Eun Min ◽  
Haesoo Kim ◽  
Da Eun Lee ◽  
Kisok Kim
Keyword(s):  
Prostate Cancer ◽  
Random Effects ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Adult Males ◽  
Fixed Effects Model ◽  
Random Effects Models ◽  
Benign Prostate Hypertrophy ◽  
Fixed And Random Effects ◽  
Meta Analyses

5-Alpha-reductase inhibitors (5-ARIs) are used in the treatment of benign prostate hypertrophy (BPH). 5-ARIs, such as finasteride and dutasteride, suppress the biosynthesis of dihydrotestosterone (DHT), a precursor of androgen, which is closely related to the incidence of prostate cancer (PCa). A previous meta-analysis demonstrated a relationship between finasteride use and the incidence of PCA. However, there have been no meta-analyses on the relationship between PCa and dutasteride alone. This meta-analysis was performed to examine the prevalence of PCa in adult males taking dutasteride. We searched PubMed for reports regarding PCa risk and dutasteride use. The study was conducted according to the PRISMA guidelines for systematic reviews and meta-analyses. The analytic hierarchy process (AHP) method was used to weight the studies. Odds ratios (ORs), 95% confidence intervals (CIs), and P-values were calculated using fixed- and random-effects models. A total of eight articles were included in the meta-analysis. The overall OR for both the fixed- and random-effects models was 0.669 and the 95% CI for the random-effects model (0.526–0.851; P = 0.006) was wider than that for the fixed effects model (0.548–0.817; P < 0.001). This study confirmed that the incidence of PCa was significantly reduced by taking dutasteride.

scholarly journals The Limits of Marginality

2021 ◽  
Author(s):  
Andrew Heathcote ◽  
Dora Matzke
Keyword(s):  
Model Selection ◽  
Random Effects ◽  
Mixed Models ◽  
Fixed Effects ◽  
Order Term ◽  
Test Point ◽  
Linear Regression Models ◽  
Random Effects Models ◽  
Target Article ◽  
Fixed And Random Effects

AbstractThe “marginality principle” for linear regression models states that when a higher order term is included, its constituent terms must also be included. The target article relies on this principle for the fixed-effects part of linear mixed models of ANOVA designs and considers the implication that if extended to combined fixed-and-random-effects models, model selection tests specific to some fixed-effects ANOVA terms are not possible. We review the basis for this principle for fixed-effects models and delineate its limits. We then consider its extension to combined fixed-and-random-effects models. We conclude that we have been unable to find in the literature, including the target article, and have ourselves been unable to construct any satisfactory argument against the use of incomplete ANOVA models. The only basis we could find requires one to assume that it is not possible to test point-null hypotheses, something we disagree with, and which we believe is incompatible with the Bayesian model-selection methods that are the basis of the target article.

scholarly journals Association of bisphenol A with puberty timing: a meta-analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Hui Meng ◽  
Yunping Zhou ◽  
Yunxia Jiang
Keyword(s):  
Bisphenol A ◽  
Confidence Intervals ◽  
Random Effects ◽  
Strong Correlation ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Study Heterogeneity ◽  
The Relationship ◽  
Prevalence Ratios

AbstractObjectivesThe results of existing studies on bisphenol A (BPA) and puberty timing did not reach a consensus. Thereby we performed this meta-analytic study to explore the association between BPA exposure in urine and puberty timing.MethodsMeta-analysis of the pooled odds ratios (OR), prevalence ratios (PR) or hazards ratios (HR) with 95% confidence intervals (CI) were calculated and estimated using fixed-effects or random-effects models based on between-study heterogeneity.ResultsA total of 10 studies involving 5621 subjects were finally included. The meta-analysis showed that BPA exposure was weakly associated with thelarche (PR: 0.96, 95% CI: 0.93–0.99), while no association was found between BPA exposure and menarche (HR: 0.99, 95% CI: 0.89–1.12; OR: 1.02, 95% CI: 0.73–1.43), and pubarche (OR: 1.00, 95% CI: 0.79–1.26; PR: 1.00, 95% CI: 0.95–1.05).ConclusionsThere was no strong correlation between BPA exposure and puberty timing. Further studies with large sample sizes are needed to verify the relationship between BPA and puberty timing.

scholarly journals Sars-Cov-2 in Argentina: Lockdown, Mobility, and Contagion

2020 ◽  
Author(s):  
Juan M.C. Larrosa
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Random Effects ◽  
Social Costs ◽  
Political Discussion ◽  
Fixed And Random Effects ◽  
The World ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Over Time

AbstractThere is a debate in Argentina about the effectiveness of mandatory lockdown measures in containing COVID-19 that lasts five months making it one of the longest in the World. The population effort to comply the lockdown has been decreasing over time given the economic and social costs that it entails. We contributes by analyzing the Argentinian case through information of mobility and contagion given answers to recurrent questions on these topics. This paper aims to fill the gap in the literature by assessing the effects of lockdown measures and the regional relaxation on the numbers of rate of new infections. We also respond to issues of internal political discussion on regional contagion and the effect of marches and unexpected crowd events. We use pool, fixed and random effects panel data modeling and Granger causality tests identifying relations between mobility and contagion. Our results show that lockdown in Argentina has been effective in reducing the mobility but not in way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and by extend it without complementary measures loss effectiveness. Contagion rate seems to be discretely displaced in time and resurging amidst slowly increasing in mobility.

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