6533b823fe1ef96bd127e045
RESEARCH PRODUCT
Practical Issues on Energy-Growth Nexus Data and Variable Selection With Bayesian Analysis
Anabel ForteAngeliki N. MenegakiGonzalo García-donatoAviral Kumar Tiwarisubject
Bayes estimatorComputer science020209 energyBayesian probabilityFeature selection02 engineering and technologyProduction function01 natural sciencesData scienceField (computer science)010104 statistics & probabilityVariable (computer science)0202 electrical engineering electronic engineering information engineering0101 mathematicsNexus (standard)Selection (genetic algorithm)description
Abstract Given that the energy-growth nexus (EGN) is short of a complete theoretical base, the production function used therein is typically complemented with numerous variables that characterize an economy. Researchers are often puzzled not only with the selection of variables per se, but also with the variable sources and the various data handlings which become apparent and available only after years of experience in this research field. Thus, this chapter is divided into two distinctive parts: The first part contains an overview of the available data sources for the EGN as well as a succinct selection of advice on data handlings, transformations, and interpretations that could come handy to students and practitioners. The second part is more technical and deals with variable selection with Bayesian analysis, which appears as a reasonable solution to the overwhelming problem of variable selection in the EGN. Besides a worked example, the chapter provides with an introduction to Bayesian analysis and the essentials to Bayesian estimation and prediction.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |