Relationship between Economic Growth and Energy Consumption in Iran (Analysis of Linear and Non-linear Causality Models)

• Main Authors: teimor mohammdi, hamid nazemian, mohsen nosratian nasab
• Format: Article
• Language: fas
• Published: Allameh Tabataba'i University Press 2013-01-01
• Series: Pizhūhishnāmah-i Iqtiṣād-i Inirzhī-i Īrān
• Subjects: Energy Consumption; Economic Growth; linear causality; nonlinear causality
• Online Access: http://jiee.atu.ac.ir/article_777_8f8ad1b28e59e736e10158947c92d481.pdf

The causal relation between energy consumption and economic growth, as an imperative issue in energy economics, has been a well-studied topic. Previous studies in Iran have ignored the nonlinear behavior which could be caused by structural breaks. In this study, both linear and nonlinear causality test are applied to examine the causal relationship between energy consumption and economic growth in Iran. The nonlinear causality test, applied here, is based on the conception of correlation integral (an estimator of spatial probability across time). In this study, we find evidence on a unidirectional linear and nonlinear causality running from energy consumption to economic growth. environmental pollution. On the basis of panel integration and co-integration tests, Stern (2004) and Perman and Stern (1999, 2003) have presented evidence and forcefully stated that the EKC hypothesis does not exist. In this paper by using fractional co-integration test, EKC is evaluated for 27 low middle income countries. The conclusions show according to classical co-integration test there is no co-integrated EKC based on HADRI statistics. Using fractional co-integration, evidences support a common EKC for countries: El Salvador, Nicaragua, Iran, Pakistan, Paraguay, Tunisia but our data does not give useful information about EKC existence. fareast-font-family:Calibri; mso-bidi-font-family:"Times New Roman";color:#333333;mso-no-proof:no'>FARIMA) were applied using the daily oil price in order to forecast oil prices. To compare the forecast accuracy of the model, the prediction error criteria was used. The results showed that the performance of FARIMA is much better than the other two models.