Fixed- and Random-Effects Models

2017 ◽  
pp. 247-258 ◽  
Author(s):  
Michael O’Mahony
Keyword(s):  
Random Effects ◽  
Random Effects Models ◽  
Fixed And Random Effects

scholarly journals Significance of supply chain finance: Insights from Saudi Arabia

2021 ◽  
Vol 9 (3) ◽  
pp. 539-548 ◽  
Author(s):  
Abdul Rahman Shaik
Keyword(s):  
Saudi Arabia ◽  
Supply Chain ◽  
Financial Performance ◽  
Random Effects ◽  
Return On Assets ◽  
Random Effects Models ◽  
Performance Variables ◽  
Operating Profit ◽  
Supply Chain Finance ◽  
Fixed And Random Effects

The study examines the effect of the supply chain finance (SCF) on the corporate financial performance measured in terms of Return on Assets (ROA), Tobin's Q, and Gross Operating Profit (GOP) in the material sector of Saudi Arabia. The study selects a sample of 42 companies from the material sector listed on Tadawul starting in 2008 and ending 2019. A panel regression in terms of pooled OLS, fixed and random effects, and panel GMM is estimated to report the empirical results. The results report a negative and significant effect between the financial performance variables and supply chain finance, specifically with ROA with pooled OLS and fixed and random effects models. The results of panel GMM also show a negative and significant effect between all the financial performance variables and financing supply chain. The results are useful to academicians and the managers in the materials, inventory, and sales sections, and supply chain managers to integrate finance and SCM to achieve corporate benefits.

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.

Meta-analysis in Sociological Research: Power and Heterogeneity

2019 ◽  
pp. 004912411988247 ◽  
Author(s):  
Guangyu Tong ◽  
Guang Guo
Keyword(s):  
Random Effects ◽  
Science Studies ◽  
Meta Analysis ◽  
Sociological Research ◽  
Random Effects Models ◽  
Sociological Inquiry ◽  
Wide Range ◽  
Fixed And Random Effects ◽  
Study Heterogeneity ◽  
Analysis Models

Meta-analysis is a statistical method that combines quantitative findings from previous studies. It has been increasingly used to obtain more credible results in a wide range of scientific fields. Combining the results of relevant studies allows researchers to leverage study similarities while modeling potential sources of between-study heterogeneity. This article provides a review of the core methodologies of meta-analysis that we consider most relevant to sociological research. After developing the foundation of the fixed- and random-effects models of meta-analysis models, this article illustrates the utility of the method with regression coefficients reported from two sets of social science studies. We explain the various steps of the process including constructing the meta-sample from primary studies, estimating the fixed- and random-effects models, analyzing the source of heterogeneity across studies, and assessing publication bias. We conclude with a discussion of steps that could be taken to strengthen the development of meta-analysis in sociological research, which will eventually increase the credibility of sociological inquiry via a knowledge-cumulative process.

Fixed- and random-effects models in meta-analysis.

1998 ◽  
Vol 3 (4) ◽  
pp. 486-504 ◽  
Author(s):  
Larry V. Hedges ◽  
Jack L. Vevea
Keyword(s):  
Random Effects ◽  
Meta Analysis ◽  
Random Effects Models ◽  
Fixed And Random Effects

Detecting and correcting for publication bias in meta-analysis – A truncated normal distribution approach

2016 ◽  
Vol 27 (9) ◽  
pp. 2722-2741 ◽  
Author(s):  
Qiaohao Zhu ◽  
KC Carriere
Keyword(s):  
Normal Distribution ◽  
Publication Bias ◽  
Random Effects ◽  
Effect Size ◽  
Meta Analysis ◽  
Truncated Normal Distribution ◽  
Random Effects Models ◽  
Funnel Plot ◽  
Fixed And Random Effects ◽  
Truncated Normal

Publication bias can significantly limit the validity of meta-analysis when trying to draw conclusion about a research question from independent studies. Most research on detection and correction for publication bias in meta-analysis focus mainly on funnel plot-based methodologies or selection models. In this paper, we formulate publication bias as a truncated distribution problem, and propose new parametric solutions. We develop methodologies of estimating the underlying overall effect size and the severity of publication bias. We distinguish the two major situations, in which publication bias may be induced by: (1) small effect size or (2) large p-value. We consider both fixed and random effects models, and derive estimators for the overall mean and the truncation proportion. These estimators will be obtained using maximum likelihood estimation and method of moments under fixed- and random-effects models, respectively. We carried out extensive simulation studies to evaluate the performance of our methodology, and to compare with the non-parametric Trim and Fill method based on funnel plot. We find that our methods based on truncated normal distribution perform consistently well, both in detecting and correcting publication bias under various situations.

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