Controversial Data Imputation Techniques Unveiled in Green Innovations Study

A recent study on green innovations and patents in 27 countries has come under scrutiny for its questionable data imputation techniques.

A new study on green innovations and patents in 27 countries has raised eyebrows due to the controversial data imputation techniques employed by the authors. The study, conducted by two economics professors in Europe, has come under fire for using Excel’s autofill function to fill in missing data, as well as copying values from adjacent countries in the dataset. These practices have raised concerns among experts, who argue that such methods can invalidate statistical analyses and undermine the integrity of the research. The study, published in the Journal of Cleaner Production, has faced criticism for its lack of transparency and failure to acknowledge the imputation procedures used.

Controversial Data Imputation Methods Unveiled

The study’s first author, Almas Heshmati, a professor of economics at Jönköping University in Sweden, was confronted by a PhD student who discovered the questionable data imputation techniques. Heshmati admitted to using Excel’s autofill function to fill in missing values, a practice that is commonly used in economics but requires careful justification and documentation. The student was shocked to learn that Heshmati had also copied values from adjacent countries in the dataset, leading to thousands of imputed cells in the final dataset.

Experts Weigh In:

Econometrics professors and experts in the field have expressed their concerns about the data imputation methods used in the study. Andrew Harvey, a professor of econometrics at the University of Cambridge, criticized the use of Excel’s autofill function, stating that it can invalidate statistical analyses and associated tests. Søren Johansen, an econometrician and professor emeritus at the University of Copenhagen, went further, describing Heshmati’s actions as “cheating” due to the lack of documentation.

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Controversy Surrounding the Study:

The paper, titled “Green innovations and patents in OECD countries,” was published in the highly regarded Journal of Cleaner Production. However, the paper has only been cited once, according to Clarivate’s Web of Science. The publisher and the journal’s editors have not responded to requests for comment regarding the concerns raised about the study’s data imputation techniques.

Heshmati’s Response:

In response to the criticism, Heshmati initially claimed that his co-author, Mike Tsionas, who recently passed away, was aware of the data imputation methods used. Heshmati argued that imputation was necessary to make the data usable and that the description of the data as “balanced” referred to the final dataset, which included the imputed values. However, he later acknowledged his responsibility for the imputations and the failure to document them properly.

Expert Opinions:

Gary Smith, a professor of economics at Pomona College, expressed serious concerns about the copying of data between countries, calling it “beyond concerning.” He found multiple instances where data points were copied from one country to another. Marco Hafner, a senior economist at the RAND Corporation, also criticized the use of Excel’s autofill function and stressed the importance of reporting assumptions and conducting sensitivity analyses to ensure transparency.

Conclusion:

The controversial data imputation techniques used in the study on green innovations and patents in 27 countries have raised significant concerns within the academic community. The use of Excel’s autofill function and the copying of values from adjacent countries have been criticized for their potential to invalidate statistical analyses and compromise the integrity of the research. The lack of transparency and documentation surrounding these methods has further exacerbated the controversy. As experts call for greater transparency and adherence to best practices in data imputation, the study’s validity and credibility are being called into question.

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