Lagrange Multiplier Tests in Applied Research

Astaiza-Gómez, José Gabriel

doi:10.2139/ssrn.3669884

Cited by 1 publication

(3 citation statements)

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“…According to a prior study [43], the estimation procedure applied followed a statistical evaluation of the pooled OLS regression, GLS random effects (RE) model, and fixed effects (FE) model using the Breusch-Pagan Lagrangian multiplier test (BPLM) and Hausman test approaches. The BPLM test was applied to the RE model estimates to test whether pooled OLS regression was the appropriate model to apply for analysis.…”

Section: Methodology and Datamentioning

confidence: 99%

“…The BPLM test was applied to the RE model estimates to test whether pooled OLS regression was the appropriate model to apply for analysis. The BPLM test (is used to test for heteroskedasticity in a linear regression model) for random effects results rejected the null hypothesis that the Pooled OLS model was appropriate [43]. The FE model was further run to appropriately selected between the RE and FE.…”

Section: Methodology and Datamentioning

confidence: 99%

“…Having endogenous regressors in a model will cause ordinary least squares estimators to fail, as one of the assumptions of OLS is that there is no correlation between a predictor variable and the error term. Reference [43] stated that the Hausman test was applied to select the appropriate model between RE and FE. Interpreting the result from a Hausman test is fairly straightforward: if the p-value is small (less than 0.05), reject the null hypothesis.…”

Section: Methodology and Datamentioning

confidence: 99%

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Does Social Businesses Development Affect Bioenergy Industry Growth under the Pathway of Sustainable Development?

2021

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The Treaty of European Union (EU) sets out the EU vision for sustainable development of Europe based on balanced economic growth and price stability, a highly competitive social market economy, aiming at full employment and social progress, and a high level of protection and improvement of the quality of the environment. This led us to ask whether or not social business development stimulates the development of the bioenergy sector in the EU28 countries. Given the increasing rates of energy insecurity, environmental pollution, poverty, and unemployment, countries are switching to alternative energy sources that might promote social business development, climate change, and environmental quality. In this scenario, the bioenergy industry has received the attention of scholars and policymakers alike. The role social business development can play in the growth of the bioenergy industry remains uncertain, therefore, further investigation is necessary. This study, therefore, explores the relationships between the bioenergy industry and social business development indicators related to zero emissions, zero poverty, and zero unemployment for EU28 region countries from 1990 to 2018. Empirical evidence is based on the use of a new economic model, dynamic panel co-integration simulations (Fully Modified Ordinary Least Square, Dynamic Ordinary Least Square, and Pooled Ordinary Least Square). The results reveal a negative relationship between EU28′s bioenergy industry growth and carbon dioxide emissions, vulnerable employment, and unemployment rate, suggesting that bioenergy industry growth helps reduce pollution and unemployment. Likewise, bioenergy industry growth increases food supply, economic growth, and female employment and might be the best alternative to fossil fuels. Necessary policy related to bioenergy industry growth can be formulated, especially in achieving the sustainable development goals for social businesses.

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Section: Methodology and Datamentioning

confidence: 99%

Section: Methodology and Datamentioning

confidence: 99%

Section: Methodology and Datamentioning

confidence: 99%

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