Meta-Regression Analysis in Economics and Business

By T. Stanley, Hristos Doucouliagos. 2012

735 citations recorded by the source corpus.

Subfields: Information Systems, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/36bd90122702429b3e77d5747b5d620bb6f099dd

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Citations in the corpus

Tourism and Economic Growth: A Meta-regression Analysis (2019, methodology): “Therefore, as recommended by Stanley and Doucouliagos (2012), we use the partial correlation coefficient (PCC) as the standardized effect size to summarize and compare the results from various studies.”
Does economic globalisation affect income inequality? A meta‐analysis (2020, methodology): “In line with standard techniques for meta-regression analysis (e.g. Stanley and Doucouliagos 2012), we make the assumption that the ith estimate of the globalisation-inequality partial correlation coefficient from study j, denoted rij, is influenced by a vector of variables (𝑍𝑍𝑘𝑘𝑘𝑘)…”
META‐ANALYSIS OF ECONOMICS RESEARCH REPORTING GUIDELINES (2013, positive): “Applications of MRA, like their data sources, have grown at a remarkable rate—18% per year over the last couple of decades (Stanley and Doucouliagos, 2012).”
A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias (2015, methodology): “presented in FIGURE 1 are all taken from Stanley and Doucouliagos’s excellent treatise on meta-regression analysis (Stanley and Doucouliagos, 2012).”
Does Government Spending Affect Income Poverty? A Meta-regression Analysis (2018, methodology): “The aim of MRA is to provide a reliable and objective way of summarizing research findings (Stanley & Doucouliagos, 2012).”
Top Management Team Diversity (2013, background): “…employed Hunter and Schmidt procedure have their own advantages, but are unable to control for distorting factors as MRA is able to do (for a detailed introduction to MRA see Stanley & Doucouliagos, 2012; for an application see Carney, Gedajlovic, Heugens, Van Essen, & Van Oosterhout, 2011).”
A meta-analysis of residential water demand studies (2014, methodology): “…the inverse of standard errors as weights and panel data models appear as more reliable methods that take into account many issues in meta-regression estimation such as heteroscedasticity, data heterogeneity and non-independence (Nelson and Kennedy 2009 and Stanley and Doucouliagos 2012, 2013).”
What fiscal policy is most effective? A Meta Regression Analysis (2015, methodology): “Hence, as a second-best proxy for precision (Stanley and Doucouliagos, 2012, p. 73), I rely on the number of observations used for each multiplier estimation.”
The effect of FDI on environmental emissions: Evidence from a meta-analysis (2020, methodology): “According to Stanley and Doucouliagos (2012), meta-analysis involves a systematic review of relevant scientific knowledge in previously published, or reported empirical findings on a given hypothesis.”
R&D and productivity in OECD firms and industries: A hierarchical meta-regression analysis ☆ (2016, neutral): “…be modelled as follows: issue, we estimate a weighted least squares (WLS) version (6), where precision (1/ SE i ) is used as weight (Stanley, 2008; Stanley and Doucouliagos, 2012 Here t i is the t-value associated with the reported estimate and the error term v i is the error term in (5) weighted…”
ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients (2015, background): “√ It is known that the standard error is 1 = N − 2 − g (Olkin and Finn, 1995; Stanley and Doucouliagos, 2012; Sharma, 2012).”
Is There Really Granger Causality between Energy Use and Output? (2014, methodology): “We modify the standard FAT-PET meta-regression model used in economics (Stanley and Doucouliagos, 2012) to meta-analyze Granger causality test statistics.”
Income Elasticities and Global Values of a Statistical Life (2017, background): “The statistical properties of this correction for publication selection effects and related biases are documented in Egger et al. (1997), Stanley (2005, 2008), and Stanley and Doucouliagos (2012).”
Contemporary Guidance for Stated Preference Studies (2017, background): “The predisposition of authors, reviewers, and editors to favor results that pass accepted thresholds for statistical significance is a common source of publication bias in many research literatures, including SP and RP studies (Rosenberger and Johnston 2009; Stanley and Doucouliagos 2012).”