Time Series Analysis of Global Energy Indices: Logarithmic and Normalized Techniques for Developmental Studies

Diverse opinions exist in the time series analysis of energy and related indices, difference methodology, sample size, variation. This paper will make a conscious effort to converge divergent outlooks. To accomplish this essential task, five indices consisting consumption (EC), gross domestic product (GDP), carbon dioxide emission (CDE), human development index (HDI), oil price (OP) were selected. Two analytical methods adopted, namely logarithmic normalized techniques, which are designed complement each other drawing unfalsified statistical inference concerning causality between indices. The subjected four tests analyses: augmented Dickey-Fuller, cointegration, pairwise Granger causality, vector error correction model (VECM). Irrespective prevailing challenges, both techniques unanimously filtered out causalities. consisted neural flow consumption, emission, index, unidirectional price, whereas technique established bidirectional price. Pertinently, research suggests appropriate policies that generate sustainable all causal directions. Assiduously, overwhelming agreement at 0.05 level is recommended for further validation with more modern econometric tests.