Some Generalized Estimating Equations Models Based on Causality Tests for Investigation of the Economic Growth of the Country Groups

• Autorzy: Yonar Harun , İyit Neslihan

Identyfikatory

DOI

10.2478/fcds-2021-0019

• Języki publikacji: EN

Abstrakty

EN

In this study, investigation of the economic growth of the Organization for Economic Cooperation and Development (OECD) countries and the countries in different income groups in the World Data Bank is conducted by using causality analyses and Generalized Estimating Equations (GEEs) which is an extension of Generalized Linear Models (GLMs). Eight different macro-economic, energy and environmental variables such as the gross domestic product (GDP) (current US$), CO₂ emission (metric tons per capita), electric power consumption (kWh per capita), energy use (kg of oil equivalent per capita), imports of goods and services (% of GDP), exports of goods and services (% of GDP), foreign direct investment (FDI) and population growth rate (annual %) have been used. These countries have been categorized according to their OECD memberships and income groups. The causes of the economic growth of these countries belonging to their OECD memberships and income groups have been determined by using the Toda-Yamamoto causality test. Furthermore, various GEE models have been established for the economic growth of these countries belonging to their OECD membership and income groups in the aspect of the above variables. These various GEE models for the investigation of the economic growth of these countries have been compared to examine the contribution of the causality analyses to the statistical model establishment. As a result of this study, the highlight is found as the use of causally-related variables in the causality-based GEE models is much more appropriate than in the non-causality based GEE models for determining the economic growth profiles of these countries.

Słowa kluczowe

EN

economic growth; generalized estimating equations; generalized linear model; Toda-Yamamoto; causality test; Organization for Economic Co-Operation and Development; OECD; causality analysis

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2021
• Tom: Vol. 46, No. 3
• Strony: 297--315
• Opis fizyczny: Bibliogr. 58 poz., rys., tab.

Twórcy

autor

Yonar Harun

• Selçuk University, Faculty of Veterinary Medicine, Department of Biostatistics, Konya, Turkey

autor

İyit Neslihan

• Selçuk University, Faculty of Science, Department of Statistics Konya, Turkey

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).