TY  - JOUR
T1  - Parametric and non-parametric data-driven analytics for socioeconomic challenges in a contemporary world
A1  - Nepomuceno, Thyago C C
Y1  - 2023/07//
KW  - analysis
KW  - data analytics
KW  - data envelopment
KW  - econometrics
KW  - natural language
KW  - operations research
KW  - regression models
KW  - time series analysis
JF  - Socioeconomic Analytics
VL  - 1
IS  - 1
SP  - 1
EP  - 4
DO  - 10.51359/2965-4661.2023.259300
UR  - https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/259300
N2  - The rapid advancement of artificial intelligence and data-driven technologies have presented novel opportunities for addressing complex socioeconomic challenges in the contemporary world. By harnessing diverse datasets and employing sophisticated analytical techniques, researchers, policymakers, and practitioners can gain profound insights into the root causes of socioeconomic challenges and devise innovative strategies to promote inclusive development and sustainable growth. In this editorial, I share some perspectives on exploring the application of non-parametric and parametric data-driven analytics methodologies (Data Envelopment Analysis and Econometric Models) as powerful tools for understanding and resolving multifaceted issues that impact societies globally, and what I believe to be the future of these methods. Readers of Socioeconomic Analytics will be able to find those interesting methodologies as empirical applications in the journal's inaugural and forthcoming issues.
ER  -