elasticities of energy, environment, and economy in long...
TRANSCRIPT
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2155 www.globalbizresearch.org
Elasticities of Energy, Environment, and Economy in Long and Short
Run: Using Simultaneous Equations, Error Correction and
Cointegration Models
Vahid Mohamad Taghvaee,
Customs Administration of Iran, Iran.
Parviz Hajiani
Alireza Seifi Aloo
Email: [email protected]
___________________________________________________________________________
Abstract
All quarters of the globe worries about the globe including environmentalists, literati,
politicians, and religious people. This paper tries to estimate the environment, energy, and
economy elasticities of Iran, both the long run and short ones. We employ error correction
model and cointegration technique to regress simultaneous equations system in a 38-year
period during 1974-2012 in Iran. The regressions are estimated with two distinctive
methodologies including Limited and Full Information. The results show that the long run
elasticities are greater than the corresponding short run ones. Moreover, the elasticities of
economic growth are greater than those of environmental pollution implying that the
environmental pollution provides a flimsy pretext to impose a limit on economic activities.
However, it is urbanization which presents the most profound impacts on the energy
consumption, representing consumerism as an inveterate concomitant of urbanization. Thus,
the governors are advised 1) to make a compromise between economy and environment by
improving the economic infrastructure and developing more effective environmental- and
cultural-policies; 2) to encourage the green economic sectors by formulating the long run
environmental policies; and 3) to implement the cultural and energetic programs to increase
the environmental quality without stopping the economic growth. Finally, The Simultaneous
Error Correction Model does not work well.
___________________________________________________________________________
Key Words: Environmental Pollution; Economic Growth; Energy Consumption;
Simultaneous Equations
JEL Classification: Q56; C32
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2156 www.globalbizresearch.org
1. Introduction
All quarters of the globe worries about the globe including environmentalists, literati,
politicians, and religious people. “Nobody on the planet will be untouched by climate change”
said Rajendra K. Pachauri, the chairman of the Intergovernmental Panel on Climate Change
(IPCC) in a press conference in Japan in 2014 where and when a meeting was held on the IPCC
report in 2014. In the next year, Carol Ann Duffy composed the subsequent poem “What have
you done; with what was given you; what have you done with; the blue, beautiful world?” At
that year and two days before the Earth day, Barak Obama, the US president said “Today, there
is no greater threat to our planet than climate change.” Again at that year, Pope Francis said
“This sister now cries out to us because of the harm we have inflicted on her by our irresponsible
use and abuse of the goods with which God has endowed her.” Also, he referred to the global
warming as “a seedbed of collective selfishness”. One of the guiltiest economies at climate
change is Iran.
Iran is a good candidate for the economic-environmental studies. In 2000s, the GDP of Iran
made up averagely less than 0.005% of that of the world, whilst it is more than thirty times
higher for CO2 emissions, exceeding 0.15% (Taghvaee and Parsa, 2015; World Development
Indicator). Furthermore, this country is 164th (out of 230) in the real growth rate of GDP
ranking, 71th in the percentage term (CIA factsheet, 2015) whereas it is 83th (out of 178) in the
environmental quality ranking, 46th in the percentage term (Yale University Environmental
Database, 2015). They can be for a simple reason; the base of Iran economy is only on the
production and export of a large amount of fossil fuel energies and the derivatives; more than
70 million people produce solely one product to live with.
The diverse economic activities in various fields can create an assortment of effects on the
sustainable development. On the one hand, they, undoubtedly, play a vital role in the economic
development of the developing countries such as Iran. Take oil industry for example which is
a hive of activity in Iran, the more it grows up, the more the economy flourishes. Moreover,
based on the Engine of Growth Hypothesis, there is an emprical correlation between
industrialization degree and per capita income level in these countries (Kaldor, 1966, 1967;
Rodrik and May, 2009; Szirmai, 2012; Szirmai and Verspagen, 2015). On the other hand, they
play a fatal role in the environmental development owing to the environmentally harmful
emissions (Taghvaee and Hajiani, 2015). Based on the Kaya identity, economic growth gives
the major impetus to the environmental pollution (Duro, 2013; Kaya, 1989; Kaya and Yokobori,
1997; Ruijven et al., 2015); and regarding the Pollution Haven Hypothesis (PHH), the
environmental pollution in the developing countries like Iran lies at the heart of trade openness
(as a service subsector of economy) and foreign direct investment (as a financial subsector of
economy) (Al-mulali and Tang, 2013). It is consistent with the increasing phase of the
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2157 www.globalbizresearch.org
Environmental Kuznets Curve before the turning point since the developing countries,
normally, follows the first half of the curve wherein the environment and economic growth are
in a direct conflict (Grossman and Krueger, 1991; Taghvaee and Shirazi, 2014; Baek, 2015).
Thus, the burning environmental issues have brought the environmentalists into irreconcilable
conflict with the economists whether the economy is worth growing whilst the environment has
fallen victim into the economic snare.
To grow, or not to grow, that is the question. How economy and environment can profit
from joint developments, with reaching a compromise between economists and
environmentalists. The main question of the study is which economic variables should be
inflated leading to neither environmental degradation nor deceleration in the rate of economic
growth, depending on the length of time period. Among those variables, which one can give
more mutually economic and environmental benefits in comparison with the others? The
variables encompass trade openness, financial development, urbanization, labour, capital, and
energy consumption; and the time period includes short term and long term. Answering the
above-mentioned questions give the policy makers some guidelines to stimulate those
economic sectors which are environmentally-friendly and to reconstruct those which are
environmentally-unfriendly.
The main purpose of the study is to estimate the long term and short term environmental,
energetic, and economic elasticities to find the most effective factors on the environment,
energy, and economic growth. It is worth mentioning that the elasticities represent the efficacies
of the peer variables on the endogenous variables. Positive elasticities of economic growth and
negative elasticities of environmental pollution show the economically- and environmentally-
friendly variables, respectively, which should be boosted; otherwise, they are the economically-
and environmentally-friendly variables which should be reconstructed. There is no doubt that
the long term plans would be more ingenious than the short term ones if the long term
elasticities are bigger than their short term counterparts, and vice versa. Furthermore, this study
tests Pollution Haven Hypothesis for trade openness in Iran. The algorithm of this article is as
follows: section 2 is about methodology and data, section 3 shows and analyzes the results,
section 4 represents the discussion, and section 5 explains conclusion in two separate parts.
2. Methodology and Data
Following Omri, 2013; Omri, 2014; Omri et al., 2015, this study utilizes a simultaneous
equations system to estimate the long run and short run economic elasticities of environmental
pollution in Iran within 1974-2012. The simultaneous equations system is come from a Cobb-
Douglas production function (Cobb and Douglas, 1928) and the prerequisite tests are
implemented before estimating the parameters. They subsume the tests of stationarity,
cointegration, identification, and exogeniety. Then the parameters of the cointegrated model
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2158 www.globalbizresearch.org
(long run elasticities) are estimated using two various methodologies and six different methods.
Furthermore, the parameters of the Simultaneous Error Correction Model (SECM), or short run
elasticities, are estimated using two various methodologies and six different methods. These
six methods are employed to test the both the simultaneous model of Omri, 2013; Omri, 2014;
Omri et al., 2015 in the case of Iran and to investigate the Simultaneous Error Correction Model
whether it works well as in the single or Vector Error Correction Models or not. Before the
estimation of the elasticities, the preliminary tests are implemented including stationarity,
cointegration, identification, and exogeniety (Gujarati, 2004; Greene, 2012). All the tests can
be seen in the appendices.
2.1 Cointegrated model (long run elasticities)
The preliminary tests are preparatory to the estimation of the parameters, which include tests
of stationarity, cointegration, identification, and exogeniety (Gujarati, 2004; Greene, 2012).
Augmented Dickey Fuller (Dickey and Fuller, 1979) and Phillips Perron (Phillips and Perron,
1988), and Zviot Andrews tests are the unit root tests which are employed to examine the
stationarity of the variables. Although the non-stationary variables create spurious regressions,
those with the identical integration degree can make, at least, one stationary linear combination,
so-called cointegrated regression. The residuals of the regression must be stationary in level to
develop such a cointegrated regression whose variables are correlated in long run. So the
variables with identical integration degree are put to the Engle and Granger (EG) and
Augmented Engle and Granger (AEG) tests, subject to the approval of the cointegrated equation
residuals stationarity in level (Engle and Granger, 1987). In addition to the EG and AGE, the
Durbin Watson Cointegration Regression (DWCR) is another approach to assess the
cointegration of a model, based on which a regression with the Durbin Watson statistics greater
than 0.511 is cointegrated in 99% confidence distance (Sargan and Bhargava, 1983). Then the
identification problem is analyzed using rank and order condition as the necessary and sufficient
conditions, respectively. Based on the rank condition in a simultaneous equations system, an
equation is over-identifiable if the number of predetermined variables included in the system
but excluded from the equation is greater than the number of endogenous variables in the
equation minus one; and it is just-identifiable if they are equal; otherwise it is unidentifiable.
On the basis of order condition, the equation identification depends on the matrices of the
variables coefficients excluded from an equation but included in other equations, which has
non-zero determinants. At least one such a matrix is the irrefutable proof that the equation is
identifiable. Lastly, the exogeniety test determines whether the exogenous variables are
exogenous either fallaciously or accurately. Using the reduced forms of the equations, the
suspect endogenous variables are estimated and they are added to the other right hand-side
variables of the original equation. The coefficients of the estimated variables are put to the Wald
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2159 www.globalbizresearch.org
test to examine the null hypothesis of whether they are equal to zero or not. It is rejected if the
variables are accurately endogenous and the equations are simultaneous (Gujarati, 2004;
Greene, 2012).
2.2 Cointegrated model (long run elasticities)
The Cobb-Douglas production function is a good candidate to measure the efficacy of the
variables on the economic growth which is as follows:
(1) 𝑦𝑡 = ∑ 𝛽𝑚. 𝑥𝑚𝑡
𝑛
𝑚=1
where y is production output, x is production inputs, 𝛽 is the elasticity, t is time, m denotes
the input and there are n inputs. It is transformed into the regression below to estimate the
elasticities (Omri, 2013; Omri, 2014; Omri et al., 2015).
(2) 𝐿𝐺𝐷𝑃𝑡 = 𝛽02 + 𝛽12𝐿𝐶𝑂𝑡 + 𝛽22𝐿𝐸𝑡 + 𝛾12𝐿𝑂𝑃𝑡 + 𝛾22𝐿𝐿𝐴𝐵𝑡 + 𝛾32𝐿𝐶𝐴𝑃𝑡 + 𝛾42𝑑𝑟+ 𝛾52𝑑𝑤 + 𝜀2𝑡
where CO is the carbon dioxide emissions (per capita metric ton), GDP is per capita gross
domestic production (constant Iranian Rial prices in 2004), E is energy consumption (per capita
Kilogram oil equivalent), OP is trade openness (trade volume as a percentage of GDP), DR (is
zero for the years before the Islamic revolution in 1979 and is one for the rest of the years) and
DW (is one for the war years within 1980-1987 and is zero for the rest of the years) are dummy
variables, 𝜀 is the residuals, t is the year, L is the natural logarithm (meaning that all the
variables are in the form of natural logarithm), 𝛽 and 𝛾 are the parameters, and the remaining
symbols were explained in the previous model. Since the variables are in the natural logarithmic
form, the parameters can be considered as the elasticities and as the long run elasticities in a
cointegrated model. In addition to the elastisities of economic growth, this research is aiming
to estimate the environmental and energetic elasticities. So it develops the single regression into
the simultaneous three-equation system beneath to consider the economic growth,
environmental pollution, and energy consumption not only as explanatory variables but also as
independent variables. It paves the way for assessing the interrelationship between them (Omri,
2013; Omri, 2014; Omri et al., 2015).
(3) 𝐿𝐶𝑂𝑡 = 𝛽01 + 𝛽11𝐿𝐺𝐷𝑃𝑡 + 𝛽21𝐿𝐸𝑡 + 𝛾11𝐿𝑂𝑃𝑡 + 𝛾21𝐿𝐹𝐷𝑡 + 𝛾31𝑑𝑟 + 𝜀1𝑡
(4) 𝐿𝐺𝐷𝑃𝑡 = 𝛽02 + 𝛽12𝐿𝐶𝑂𝑡 + 𝛽22𝐿𝐸𝑡 + 𝛾12𝐿𝑂𝑃𝑡 + 𝛾22𝐿𝐿𝐴𝐵𝑡 + 𝛾32𝐿𝐶𝐴𝑃𝑡 + 𝛾42𝑑𝑟+ 𝛾52𝑑𝑤 + 𝜀2𝑡
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2160 www.globalbizresearch.org
(5) 𝐿𝐸𝑡 = 𝛽03 + 𝛽13𝐿𝐶𝑂𝑡 + 𝛽23𝐿𝐺𝐷𝑃𝑡 + 𝛾13𝐿𝐹𝐷𝑡 + 𝛾23𝐿𝑈𝑡 + 𝛾33𝐿𝐿𝐴𝐵𝑡 + 𝛾43𝐿𝐶𝐴𝑃𝑡+ 𝛾53𝑑𝑟 + 𝜀3𝑡
where FD is financial development (domestic credit to private sector as a percentage of
GDP), LAB is the labour force (active population as a percentage of the total population), CAP
is capital (per capita constant Iranian Rial prices in 2004), U is urban population (urban
population as a percentage of the total population), and the remaining symbols were explained
in the previous model. In this study, all the variables derived from World Development
Indicator other than per capita GDP, labour force, and capital which are come from the Central
Bank of Islamic Republic of Iran.
Estimation of the system parameters relies on both Limited Information Methodology
(single-equation) and Full Information Methodology (system of equations). Limited
Information Methodology (single-equation) estimates the parameters equation-by-equation
rather than estimating the equations system as a whole. It covers three methods: 2-Stage Least
Squares (2SLS), Weighted 2-Stage Least Squares (W2SLS), and Limited Information
Maximum Likelihood (LIML); and Full Information Methodology (system of equations)
estimates the parameters, all the equations considered entirely. It subsumes three methods: 3-
Stage Least Squares (3SLS), General Method of Moments (GMM), and Full Information
Maximum Likelihood (FIML). Clearly, applying the six distinct methods provides us with the
capability of juxtaposing the comparative results to have a preponderance of evidence, firm
conclusion, and detailed discussion (Gujarati, 2004; Greene, 2012).
2.3 Simultaneous Error Correction Model (SECM) for short run elasticities
The Simultaneous Error Correction Model (SECM) can be constructed as below, on
condition that the variables are cointegrated (Greene, 2012).
(6) 𝑑𝐿𝐶𝑂𝑡 = 𝜗01 + 𝜗11𝑑𝐿𝐺𝐷𝑃𝑡 + 𝜗21𝑑𝐿𝐸𝑡 + 𝜃11𝑑𝐿𝑂𝑃𝑡 + 𝜃21𝑑𝐿𝐹𝐷𝑡 + 𝜃31𝑑𝑟+ 𝜃41𝜀1̂𝑡−1 + 𝑒1𝑡
(7) 𝑑𝐿𝐺𝐷𝑃𝑡 = 𝜗02 + 𝜗12𝑑𝐿𝐶𝑂𝑡 + 𝜗22𝑑𝐿𝐸𝑡 + 𝜃12𝑑𝐿𝑂𝑃𝑡 + 𝜃22𝑑𝐿𝐿𝐴𝐵𝑡 + 𝜃32𝑑𝐿𝐶𝐴𝑃𝑡+ 𝜃42𝑑𝑟 + 𝜃52𝑑𝑤 + 𝜃62𝜀2̂𝑡−1 + 𝑒2𝑡
(8) 𝑑𝐿𝐸𝑡 = 𝜗03 + 𝜗13𝑑𝐿𝐶𝑂𝑡 + 𝜗23𝑑𝐿𝐺𝐷𝑃𝑡 + 𝜃13𝑑𝐿𝐹𝐷𝑡 + 𝜃23𝑑𝐿𝑈𝑡 + 𝜃33𝑑𝐿𝐿𝐴𝐵𝑡
+ 𝜃43𝑑𝐿𝐶𝐴𝑃𝑡 + 𝜃53𝑑𝑟 + 𝜃63𝜀3̂𝑡−1 + 𝑒3𝑡
where d is one degree differentiation, 𝜀̂ is the estimated residuals in the cointegrated
regression, e is the residual term, 𝜃 and 𝜗 are the short run elasticity, and the remaining indices
are as mentioned before. Parameter 𝜃63 is expected to be less than one and negative in sign
showing the adjustment velocity. In this model the parameters are interpreted as the short run
elasticities (Engle and Granger, 1987; Ramanathan, 1999; Alves and Bueno, 2003; Taghvaee
and Hajiani, 2014). Despite using both the Limited and Full Information methodologies in the
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2161 www.globalbizresearch.org
estimation of this model, only three methods are employed to estimate the short run elasticities
including 2-Stage Least Squares (2SLS) and Weighted 2-Stage Least Squares (W2SLS) as the
Limited Information methods; and 3-Stage Least Squares (3SLS) as the Full Information
method.
3. Results
This study estimates the environmental, energetic, and economic elasticities for long run
and short run in Iran during 1974-2012 using a simultaneous model with two different
methodologies: 1) Limited Information or single-equation methodology including 2-Stage
Least Squares (2SLS), Weighted 2-Stage Least Squares (W2SLS), and Limited Information
Maximum Likelihood (LIML); and 2) Full Information or system of equations methodology
including 3-Stage Least Squares (3SLS), General Method of Moments (GMM), and Full
Information Maximum Likelihood (FIML). Prior to using the methodologies, the variables
(which are in the natural logarithm form) are put to the stationarity tests. The results of the
above-mentioned tests support the reliability of our estimations which can be seen in the
appendices.
3.1 Results of cointegrated model (long run elasticities)
Table 8 indicates the long term elasticities, statistics, and explanatory strength of the
equation 3 in the cointegrated simultaneous model using Limited Information methodology
(2SLS, W2SLS, and LIML) and Full Information methodology (3SLS, GMM, and FIML).
2SLS, W2SLS, and LIML presents the precisely-equal elasticities in value and sign, in spite of
the relatively-diverse ones in the other methods. GDP and energy consumption have not only
the highest elasticities but also they propose the most statistically-significant effects in
comparison with the other variables in this equation. Notwithstanding the disparate values of
elasticities, they are similar in sign, which is positive, other than the financial development,
which is negative. In addition to this similarity, all the elasticities are less than one, symbolizing
the inelasticity of environmental pollution in response to all the explanatory variables of the
equation. The results of the most effective variables in the long run are reported in the next
paragraph and then those of the other variables are explained on the eve of the explanatory
strength analysis of the equation.
Based on table 8, energy consumption and GDP are the most powerful impetus behind
environmental pollution in long run. Energy consumption not only has the highest elasticities
ranging from 0.7359 in 2SLS, W2SLS, and 3SLS and 0.7785 in GMM with positive sign but
also it shows the most statistically-significant effect exceeding 99% in confidence level in all
the six methods. It implies that energy consumption is the absolute acme of the environmental
pollution prior to GDP whose elasticities range among 0.3313 in GMM and 0.4469 in FIML.
However, the statistical significance of GDP is over 99% in 2SLS, W2SLS, LIML, and 3SLS
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2162 www.globalbizresearch.org
comparable to the energy consumption but it is over 95% in GMM and 90% in FIML. It
signifies that economic growth intensifies the environmental pollution and also Iran is in the
ascending phase of the Environmental Kuznets Curve (EKC).
With regard to the results of equation 3 with the great explanatory strength, trade openness
and financial development, also play a positive role, albeit lesser than energy consumption and
GDP, in environmental pollution. Owing to the emerging economy of Iran, it is a corroborative
evidence of the Pollution Haven Hypothesis (PHH), suggesting this country as a resort to the
pollutant activities of the more developed countries. Supporting PHH is, in turn, another
affirmation for locating Iran in the first and ascending phase of the EKC. The trade openness
elasticities of environmental pollution are within 0.0074 in FIML and 0.0295 in 2SLS, W2SLS,
and 3SLS and they are statistically insignificant in all the six methods. Contrary to all the above-
mentioned elasticities, those of financial development are negative ranging from 0.0166 in
GMM to 0.0344 in FIML, despite the statistical-insignificance of confidence level in all the
methods. These resulted elasticities are dependable due to the tremendous explanatory power
of the equation in the system which is validated by the Durbin Watson statistics, determination
coefficients and adjusted determination coefficients, all of which are close to each other except
the results of FIML. The minimum and maximum values are 0.9386 and 0.9545, respectively,
for the adjusted determination coefficient in LIML and determination coefficient in 2SLS, other
than those of FIML which are 0.7809 and 0.8212. Furthermore, DW statistics are in the
indecisive zone, ranging between 1.3636 in GMM and 1.4105 in LIML, which neither
confirming nor rejecting autocorrelation in the residuals of the equation 3 in the system.
On the whole, energy consumption and economic growth play the most important and
positive role in the environmental pollution owing to the high energetic and economic
elasticities of environmental pollution. Although trade openness role is the same, it is lesser
than them. In contrast, financial development reduces the environmental pollution but with an
almost negligible effect. The results support the PHH and the location of Iran in the ascending
phase of EKC. In spite of some differences in the results of the various methods, they are
consistent with one another, leading us to hinting the similar implication.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2163 www.globalbizresearch.org
Table 8: Estimation results of equation 3 in the cointegrated model (long run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C -10.8442 -5.7984***
(0.00)
-10.8442 -6.3035***
(0.00)
-10.5960 -5.6151***
(0.00)
-10.8442 -6.3035***
(0.00)
-11.1375 -
2.6857***
(0.00)
-9.5317 -4.4707***
(0.00)
LGDP 0.4221 3.3407***
(0.00)
0.4221
3.6317***
(0.00)
0.4004 3.1401***
(0.00)
0.4221 3.6317***
(0.00)
0.4469 1.6555*
(0.09)
0.3313 2.2124**
(0.02)
LE 0.7359 11.8521***
(0.00)
0.7359 12.8846***
(0.00)
0.7551 12.0516***
(0.00)
0.7359 12.8846***
(0.00)
0.7369 5.4398***
(0.00)
0.7785
9.7996***
(0.00)
LOP 0.0295 0.7964
(0.42)
0.0295 0.8658
(0.38)
0.0293 0.7840
(0.43)
0.0295 0.8658
(0.38)
0.0074 0.0768
(0.95)
0.0211 0.9959
(0.32)
LFD -0.0184 -0.3437
(0.73)
-0.0184 -0.3736
(0.70)
-0.0185 -0.3407
(0.73)
-0.0184 -0.3736
(0.70)
-0.0344 -0.2216
(0.79)
-0.0166
-0.4835
(0.62)
DR -0.1885 -1.8144*
(0.07)
-0.1885 -1.9725*
(0.05)
-0.2112 -2.0151*
(0.05)
-0.1885 -1.9725*
(0.05)
-0.1908 -0.8139
(0.36)
-0.2714
-2.1374**
(0.03)
R2 0.9545 0.9477 0.9467 0.9477 0.8212 0.9510
Adjusted R2 0.9397 0.9397 0.9386 0.9397 0.7809 0.9431
D.W. 1.3695 1.3695 1.4105 1.3695 1.4042 1.3636
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2164 www.globalbizresearch.org
Table 9 shows the long term elasticities, statistics, and explanatory strength of the equation
4 in the cointegrated simultaneous model using Limited Information methodology (2SLS,
W2SLS, and LIML) and Full Information methodology (3SLS, GMM, and FIML). All the
methods in the Limited Information Methodology (2SLS, W2SLS, and LIML) present the
precisely-equal elasticities in value and sign, despite the relatively-diverse ones in those of the
Full Information Methodology. Contrary to the equation 4, all the elasticities in equation 4 are
greater than one, symbolizing that the environmental pollution responds elastically to all the
explanatory variables of the equation, except to the trade openness and capital with the less-
than-one elasticities, reaching at most to 0.3409 and 0.0860, respectively. CO2 emissions,
labour, and energy consumption have not only the highest elasticities but also they propose the
most statistically-significant effects in comparison with the other variables in this equation
(other than the labour with statistically-insignificant effect in all the methods). The all the CO2
emissions, trade openness, and capital elasticities are totally positive in sign and energy
consumption elasticities are mainly positive while energy consumption elasticities are totally
negative. The results of the most effective variables in the long run are reported in the next
paragraph and then those of the other variables are explained on the eve of the explanatory
strength analysis of the equation.
Based on table 9, CO2 emissions, labour, and energy consumption show the largest absolute
values of the coefficients in equation 4. CO2 emissions have not only the highest absolute
values of coefficients ranging from 1.9755 in 2SLS, W2SLS, and LIML to 1.2998 in FIML
wholly with positive signs, but also its effects are, in all the methods, over 95% statistical-
significance (even over 99% in GMM yet in FIML with statistically-insignificant effects). After
CO2 emissions, labour takes the second place in the absolute values of the elasticities between
0.3604 in 3SLS and 1.5771 in all the methods in the Limited Information Methodology (2SLS,
W2SLS, and LIML) with negative signs, except for FIML with the positive one. However, it is
statistically-significant in all the methods whereas the energy consumption proposes the most
statistically-significant effects (over 99% statistical-significance in all the methods except for
LIML with over 95% statistical-significance and FIML with statistically-insignificant effect).
It takes the third place in the absolute value of elasticities in equation 4, after CO2 emissions
and labour, from 0.9268 in all the methods in the Limited Information Methodology (2SLS,
W2SLS, and LIML) and 1.2427 in 3SLS. Thus, GDP responds mainly to CO2 emissions,
labour, and energy consumption.
With regard to the results of equation 4 with the rather great explanatory strength, trade
openness and capital also play a positive role, albeit lesser than CO2 emissions, labour, and
energy consumption, in economic growth. Capital elasticities are between 0.2153 in 3SLS and
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2165 www.globalbizresearch.org
0.3409 in GMM with the positive signs; the absolute values of trade openness elasticities are
from 0.0091 in FIML and 0.1136 in all the methods of Limited Information Methodology
(2SLS, W2SLS, and LIML) with positive signs except for FIML with the negative one.
Although the determination coefficients and adjusted determination coefficients are near to
each other just like the equation 3, they are less than those of equation 3, ranging between
0.6981 for the adjusted determination coefficient in FIML and 0.8345 for determination
coefficient in all the methods in the Limited Information Methodology (2SLS, W2SLS, and
LIML). Despite the lower explanatory strength in comparison with the equation 3, the
explanatory variables can explain the endogenous variable well. Comparable with equation 3,
DW statistics are in the indecisive zone, ranging between 1.3636 in GMM and 1.4105 in LIML,
which neither confirming nor rejecting autocorrelation in the residuals of the equation 3 in the
system.
All in all, the environmental pollution elasticities of economic growth are totally positive,
labour elasticities of economic growth are mainly negative, and capital and trade openness
elasticities of economic growth are mainly positive. Although energy and labour are considered
as the production inputs, their negative role can be due to the diminishing returns law. In
contrast with energy and labour, capital and trade openness can increase economic growth,
albeit with negligible effect compared to the others. In spite of some differences in the results
of the various methods, they are consistent with one another, leading us to hinting the similar
implications.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2166 www.globalbizresearch.org
Table 9: Estimation results of equation 4 in the cointegrated model (long run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C 12.1058 2.4440**
(0.01)
12.1058 2.7413***
(0.00)
12.1058 2.4440**
(0.02)
18.4203 6.8906***
(0.00)
20.6219 1.3423
(0.17)
13.4011 4.2926***
(0.00)
LCO 1.9755 2.0670**
(0.04)
1.9755 2.3184**
(0.02)
1.9755 2.0670**
(0.04)
1.9216 2.2643**
(0.02)
1.2998 0.4584
(0.64)
1.9439 3.2299***
(0.00)
LE -0.9268
-2.4493**
(0.01)
-0.9268
-2.7473***
(0.00)
-0.9268 -2.4493**
(0.02)
-1.2427 -3.8429***
(0.00)
-1.2039 -0.6505
(0.51)
-1.0250
-5.0566***
(0.00)
LOP 0.1136 1.3728
(0.17)
0.1136 1.5397
(0.12)
0.1136 1.3728
(0.17)
0.0196 0.2949
(0.76)
-0.0091 -0.0681
(0.94)
0.0860 2.4366**
(0.01)
LLAB -1.5771 -0.8638
(0.38)
-1.5771 -0.9689
(0.33)
-1.5771 -0.8638
(0.39)
-0.3604 -0.2301
(0.81)
1.2656 0.3809
(0.70)
-1.1563
-1.0326
(0.30)
LCAP 0.3376 1.2901
(0.20)
0.3376 1.4471
(0.15)
0.3376 1.2901
(0.20)
0.2153 1.2181
(0.22)
0.2502 0.3505
(0.77)
0.3409
1.6782*
(0.09)
DR -0.0738 -0.3407
(0.73)
-0.0738 -0.3821
(0.70)
-0.0738 -0.3407
(0.73)
0.1390 0.7694
(0.44)
0.1654 0.1465
(0.88)
-0.0096
-0.0880
(0.93)
DW 0.2769 1.4496
(0.15)
0.2769 1.6259
(0.10)
0.2769 1.4496
(0.15)
0.1018 0.6453
(0.52)
-0.0821 -0.3586
(0.71)
0.2106
1.7132*
(0.08)
R2 0.8345 0.8345 0.8345 0.7644 0.7537 0.8212
Adjusted R2 0.7972 0.7972 0.7972 0.7112 0.6981 0.7809
D.W. 1.4718 1.4718 1.4718 1.3201 1.0951 1.4042
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2167 www.globalbizresearch.org
Table 10 reveals the long term elasticities, statistics, and explanatory strength of the equation
5 in the cointegrated simultaneous model using Limited Information methodology (2SLS,
W2SLS, and LIML) and Full Information methodology (3SLS, GMM, and FIML). Comparable
to equation 4, all the methods in the Limited Information Methodology (2SLS, W2SLS, and
LIML) present the precisely-equal elasticities in value and sign, despite the relatively-diverse
ones in those of the Full Information Methodology. Equivalent to equation 3 and contrary to
equation 4, all the elasticities are less than one, symbolizing the inelasticity of energy
consumption in response to all the explanatory variables of the equation. However, urbanization
is an exceptional which has the greatest elasticities not only in equation 5 but also in all the
equations of the system. On the one hand, urbanization elasticities of energy are the greatest
ones in the equation. On the other hand, it is only the urbanization which shows a statistically-
significant effect in equation 5. The coefficients signs of urbanization, CO2 emissions, and
financial development are positive in all the methods; but those of the capital and labour are
negative. The results of the most effective variables in the long run are reported in the next
paragraph and then those of the other variables are explained on the eve of the explanatory
strength analysis of the equation.
Based on table 10, urbanization is the most powerful impetus behind energy consumption
in long run. On the one hand, urbanization elasticities of energy are the highest ones in equation
5 and even in all the other equations of the system ranging between 1.8829 in FIML and 3.2439
in all the methods of the Limited Information Methodology (2SLS, W2SLS, and LIML) with
positive signs. On the other hand, it is only the urbanization statistics which are statistically
significant (over 99% in W2SLS, 95% in 2SLS and GMM, 90% in 3SLS), but the statistical-
insignificance in FIML nonetheless.
After urbanization, it is CO2 emissions variable which has the greatest elasticities between
0.2017 in GMM and 0.8049 in all the methods of the Limited Information Methodology (2SLS,
W2SLS, and LIML) with positive signs. Capital, GDP, labour, and financial development show
the near elasticities in absolute values to each other, between 0.1103 in 3SLS and 0.2885 in all
the methods of the Limited Information Methodology (2SLS, W2SLS, and LIML) for capital;
between 0.1831 in GMM and 0.2711 in all the methods of the Limited Information
Methodology (2SLS, W2SLS, and LIML) for GDP; between 0.0462 in 3SLS and 0.5138 in
FIML for labour; and 0.0898 in GMM and 0.1627 in all the methods of the Limited Information
Methodology (2SLS, W2SLS, and LIML) for financial development. It is worth mentioning
that the economic elasticities of energy are negative in all the methods of the Limited
Information Methodology (2SLS, W2SLS, and LIML) and positive all the methods of the Full
Information Methodology (3SLS, FIML, and GMM). This paradox implies a potential
conflicting role of economic growth on energy consumption.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2168 www.globalbizresearch.org
The above-mentioned resulted-elasticities are dependable due to the most tremendous
explanatory strength of the equation in the system which is validated by the Durbin Watson
statistics, determination coefficients and adjusted determination coefficients. The explanatory
strength of equation 5 is the greatest one in the system without any sign of autocorrelation. The
determination coefficients and adjusted determination coefficients are close to each other
ranging between 0.9590 in FIML and 0.9753 in 3SLS. Equivalent to the equation 3 and 4, the
DW statistics are in the indecisive zone ranging between 1.5365 in GMM and 1.7915 in FIML
which neither confirming nor rejecting autocorrelation in the residuals of the equation 3 in the
system.
All in all, urbanization intensifies dramatically the energy consumption in Iran supporting
that consumerism is the inveterate concomitant of urbanization. As the production inputs,
capital and labour have negative effects on the energy consumption, albeit insignificantly,
signifying the capacity for substituting capital and labour for energy. The positive effect of
financial development, although insignificantly, implies that the financial sector assigns the
finances to the more energy-consuming plans. The various signs of economic elasticities of
energy suggest the antithetical effects of economic growth on the energy consumption in the
changing circumstances. In spite of some differences in the results of the various methods, they
are consistent with one another, leading us to hinting the similar implications.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2169 www.globalbizresearch.org
Table 10: Estimation results of equation 5 in the cointegrated model (long run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C -14.8283 -0.9222
(0.35)
-14.8283 -1.0344
(0.30)
-14.8283 -0.9222
(0.36)
-4.6201 -0.3353
(0.73)
-3.6957 -0.0144
(0.98)
-2.8098 -0.2507
(0.80)
LCO 0.8049 0.8067
(0.42)
0.8049 0.9049
(0.36)
0.8049 0.8067
(0.42)
0.2516 0.2910
(0.77)
0.3729 0.0277
(0.97)
0.2017 0.3036
(0.76)
LGDP -0.2711
-0.4816
(0.63)
-0.2711
-0.5402
(0.59)
-0.2711 -0.4816
(0.63)
0.2286 0.4760
(0.63)
0.4648 0.0383
(0.96)
0.1831
0.5270
(0.59)
LFD 0.1627 1.1257
(0.26)
0.1627 1.2626
(0.20)
0.1627 1.1257
(0.26)
0.0971 0.7773
(0.43)
0.1397 0.0998
(0.92)
0.0898 0.7899
(0.43)
LLAB -0.2284 -0.1730
(0.86)
-0.2284 -0.1941
(0.84)
-0.2284 -0.1730
(0.86)
-0.0462 -0.0409
(0.96)
-0.5138 -0.0691
(0.94)
0.3201
0.3223
(0.74)
LCAP -0.2885 -0.6649
(0.50)
-0.2885 -0.7458
(0.45)
-0.2885 -0.6649
(0.51)
0.1103 -0.2891
(0.77)
-0.2786 -0.0709
(0.94)
0.1393
-0.4631
(0.64)
LU 3.2439 2.3826**
(0.01)
3.2439 2.6724***
(0.00)
3.2439 2.3826**
(0.02)
2.1930 1.8773*
(0.06)
1.8829 0.0736
(0.94)
2.1936
2.3940**
(0.01)
DR 0.2589 0.8885
(0.37)
0.2589 0.9966
(0.32)
0.2589 1.8885
(0.38)
0.1978 0.7722
(0.44)
0.3604 0.2358
(0.81)
0.1796
0.9549
(0.34)
R2 0.9698 0.9698 0.9698 0.9753 0.9666 0.9749
Adjusted R2 0.9630 0.9630 0.9630 0.9697 0.9590 0.9692
D.W. 1.6246 1.6246 1.6246 1.6209 1.7915 1.5365
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2170 www.globalbizresearch.org
Table 11 displays the classical tests results of the cointegrated model of the simultaneous
equation system including normality and autocorrelation which mainly accept the classical
assumptions. The normality test is run for each equation individually and for the system of
equations as a whole. Based on the resulted Jarque-Bera statistics, the distribution of residuals
is normal, except for the equation 3 and 5 and whole the system in which the normality
assumption is rejected by more than 99%, 90%, and 99% statistically-significant effect,
respectively. The System Residual Portmanteau test with 12 lags is employed to examine the
autocorrelation of residuals which supports the hypothesis of no-autocorrelation (about which
the DW statistics is silent due to their location in the indecisive zone), other than FIML. Owing
to the acceptance of the classical assumptions, the estimated coefficients are reliable for more
interpretation, discussion, and conclusion. The results of the most effective variables in short
run are reported in the next paragraph and then those of the other variables are explained on the
eve of the explanatory strength analysis of the equation.
Table 11: Residuals diagnostic tests results of the cointegrated model
Test Equation Limited Information Methods Full Information Methods
2SLS W2SLS LIML 3SLS FIML GMM
H0 :Normality
(Jarque Bera)
1 0.9256
(0.62)
0.9256
(0.62)
1.8313
(0.40)
0.9256
(0.62)
0.3480
(0.84)
10.1031
(0.00)
2 0.6510
(0.72)
0.6510
(0.72)
0.0078
(0.99)
0.0359
(0.98)
1.6131
(0.44)
1.1845
(0.55)
3 1.6510
(0.44)
1.6119
(0.44)
2.2131
(0.33)
1.7077
(0.84)
1.7004
(0.42)
4.9395
(0.08)
Joint 3.1886
(0.78)
3.1886
(0.78)
NAa 2.6693
(0.84)
3.6616
(0.72)
16.2273
(0.01)
H0 :No Autocorrelation (SRPTb):
Significant lags (up to 12 lags)
NSSc NSS NA NSS 4-7 NSS
Probabilities are written in parentheses. a Not Applicable b System Residual Portmanteau Test c No Significant Statistic
3.1 Results of Simultaneous Error Correction Model (SECM) for short run elasticities
Table 12 reveals the short term elasticities, statistics, and explanatory strength of the
equation 6 in the Simultaneous Error Correction Model (SECM) using Limited Information
methodology (2SLS, W2SLS, LIML) and Full Information methodology (3SLS, FIML,
GMM). Comparable to the equations 4 and 5 in the cointegrated model, 2SLS and W2SLS
present the precisely-equal short run elasticities in value and sign. The short run economic
elasticities of environmental pollution are the highest elasticities in equation 6. All the short
term elasticities of environmental pollution are less than one and inelastic. Moreover, the short
term elasticities of equation 6 in the SECM are less than their counterpart long run elasticities
in equation 3 in the cointegrated model. Most of the results show no statistically significant
effect.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2171 www.globalbizresearch.org
Based on table 12, economic growth is the most powerful impetus behind environmental
pollution in the short run. Economic growth has the highest elasticities ranging from 0.5249 in
FIML and 0.2729 in LIML and 3SLS with positive sign. Subsequent to Economic growth, it is
the energy consumption showing the greatest elasticities in absolute values with 0.5130 in
FIML and 0.0376 in 3SLS. The other variables show negligible effects. Other than in GMM,
the determination coefficients and the adjusted determination coefficients are too low; even
they are negative in 3SLS and LIML, implying the weak explanatory power of the variables.
However, the D.W. statistics offer no autocorrelation. The ECM coefficients are, unexpectedly,
positive in 2SLS, LIML, and 3SLS but they are negative in the other methods expectedly.
However, they are statistically-insignificant in all the methods.
Therefore, the economic growth and energy consumption play the most important role in
the environmental pollution in short run. Like the counterpart cointegrated models,
environmental pollution is inelastic in response to the change of explanatory variables, even
economic growth and energy consumption. It suggests that short run policies are insufficient as
the long run ones. However, the GDP- and energy-consumption-policities to decrease CO2
emissions in the long run are more efficient than in the short run, due to the higher elasticities
in long run. Moreover, the location of Iran on the ascending phase of EKC is supported and the
PHH acceptance is another evidence for the location.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2172 www.globalbizresearch.org
Table 12: Estimation results of equation 6 in the ECM model (short run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C 0.0227 0.0497
(0.62)
0.0227 0.550
(0.58)
0.0232 0.0588
(0.95)
0.0232 -6.4624
(0.64)
0.0041 0.0027
(0.99)
0.0110 0.8434***
(0.00)
LGDP 0.3596 1.1886
(0.23)
0.2732 1.3160
(0.19)
0.2729 0.1227
(0.90)
0.2729 0.7893
(0.43)
0.5249 1.4626
(0.64)
0.3221 3.3333***
(0.00)
LE -0.1144 -0.4027***
(0.00)
-0.1144 -0.4459
(0.65)
-0.0761 -0.0143
(0.98)
-0.0761 12.8846
(0.79)
0.5130 0.7515
(0.45)
0.0376
0.5254
(0.60)
LOP 0.0291 0.3366
(0.73)
0.0291 0.3726
(0.71)
0.0150 0.0115
(0.99)
0.0150 0.1574
(0.87)
0.0885 0.1744
(0.24)
0.0298 0.6062
(0.54)
LFD -0.0162 -0.1785
(0.85)
-0.0162 -0.1976
(0.84)
-0.0555 0.0000
(0.96)
-0.0555 -0.5207
(0.60)
-0.0158 -0.1819
(0.85)
0.0064
0.1503
(0.88)
DR -0.0038 -0.0838
(0.93)
-0.0038 -0.0928
(0.92)
-0.0057 -0.0209
(0.98)
-0.0057 -1.1136
(0.90)
-0.0005 -0.0003
(0.99)
0.0044
-2.3345
(0.73)
𝜀�̂�−1 0.1162 0.2157
(0.82)
-0.1162 -0.2389
(0.81)
0.4221 0.0288
(0.97)
0.4221 -1.6468
(0.51)
-0.6439 -1.2160
(0.22)
-0.2436
-1.1084
(0.27)
R2 0.2694 0.2694 -0.0770 -0.0617 0.6318 0.9510
Adjusted R2 0.1280 0.1280 -0.2855 -0.2672 0.5605 0.9431
D.W. 1.8621 1.8621 1.7692 1.7706 1.6412 1.3636
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2173 www.globalbizresearch.org
Table 13 reveals the short term elasticities, statistics, and explanatory strength of the
equation 7 in the Simultaneous Error Correction Model (SECM) using Limited Information
(2SLS, W2SLS, LIML) and Full Information methodology (3SLS, FIML, GMM Comparable
to the equations 4 and 5 in the cointegrated model, 2SLS and W2SLS present the precisely-
equal short run elasticities in value and sign. The highest elasticities are the labour and capital
ones. All the short term elasticities of environmental pollution are less than one and inelastic
other than the capital ones. Moreover, the short term elasticities of equation 7 in the SECM are
less than their counterpart long run elasticities in equation 4 in the cointegrated model. It is
worth mentioning that all the short term elasticities of economic growth are positive in sign. No
variables show statistically significant effect. Other than in GMM, the determination
coefficients and the adjusted determination coefficients are too low, implying the weak
explanatory power of the variables. However, the D.W. statistics offer no autocorrelation. The
ECM coefficients are, unexpectedly, negative in LIML, 3SLS, and FIML but they are negative
in the other methods expectedly.
Therefore, all the explanatory variables show positive relationship with the endogenous and
like the counterpart cointegrated models, economic growth change is inelastic in response to
the change of explanatory variables, suggesting that short run policies are insufficient, contrary
to the long run ones. It is worth mentioning that GDP, labour, and energy consumption show
negative coefficients in long run and positive in short run requiring more attention in adopting
such policies owing to their potentially-various-effects in different situations.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2174 www.globalbizresearch.org
Table 13: Estimation results of equation 7 in the cointegrated model (short run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C -0.2125 -2.2751**
(0.02)
-0.2125 -2.6043**
(0.01)
-0.2055 -0.3588
(0.72)
-0.2055 6.7231***
(0.00)
-0.0622 -0.0406
(0.96)
-0.2466 -5.1652***
(0.00)
LCO 0.2542 2.3982
(0.69)
0.2542 0.4559
(0.64)
-0.2496 0.0009
(0.99)
0.2496 0.3776
(0.70)
1.2698 0.5299
(0.59)
1.3214 1.1300
(0.26)
LE 0.1298
0.4875
(0.62)
0.1298
0.5581
(0.57)
0.3035 0.0719
(0.94)
0.3035 1.5122
(0.13)
-0.6886 -0.2934
(0.76)
0.2740
1.3457
(0.18)
LOP 0.0763 0.9743
(0.33)
0.0763 1.1153
(0.26)
0.1384 0.2192
(0.82)
0.1384 2.0640
(0.04)
-0.1301 -0.3338
(0.73)
0.0895 2.4338**
(0.01)
LLAB 0.6526 0.5654
(0.57)
0.6526 0.6472
(0.51)
0.2061 0.0084
(0.99)
0.2061 0.2439
(0.80)
0.5954 0.3761
(0.70)
0.7974
1.5326
(0.12)
LCAP 1.2926 1.9016*
(0.06)
1.2926 2.1767**
(0.03)
1.2898 0.3253
(0.74)
1.2898 2.3552
(0.02)
0.4440 0.4953
(0.62)
1.6046
3.6975***
(0.00)
DR 0.1900 0.2401**
(0.02)
0.1900 2.5642**
(0.01)
0.1862 0.6099
(0.54)
0.1862 0.7897***
(0.00)
0.0521 0.0343
(0.97)
0.2234
5.4610***
(0.00)
DW 0.0026 0.0441
(0.96)
0.0026 0.0504
(0.95)
-0.0220 -0.0135
(0.98)
-0.0220 0.3907
(0.69)
-0.0075 -0.2779
(0.78)
0.0104
0.4474
(0.65)
𝜀�̂�−1 0.7384 2.2515**
(0.02)
0.7384 2.5773**
(0.01)
0.6110 0.0974
(0.92)
0.6110 2.7016***
(0.00)
-0.4131 -0.3089
(0.75)
0.7098
6.2931***
(0.00)
R2 0.2694 0.2294 0.3383 0.7644 0.2757 0.2774
Adjusted R2 0.1280 0.0168 0.1558 0.7112 0.0759 0.0781
D.W. 1.8621 1.4668 1.6254 1.3201 1.6618 1.5663
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2175 www.globalbizresearch.org
Table 14 shows the short term elasticities, statistics, and explanatory strength of the equation
8 in the Simultaneous Error Correction Model (SECM) using Limited Information
methodology (2SLS, W2SLS, LIML) and Full Information methodology (3SLS, FIML, GMM
Comparable to the equations 4 and 5 in the cointegrated model, 2SLS and W2SLS present the
precisely-equal short run elasticities in value and sign. All the elasticities (except urbanization
and labour) are less than one, symbolizing the inelasticity of energy consumption in response
to those explanatory variables of the equation. Moreover, all the variables show that the
statistically-insignificant effects. Short run urbanization and labour elasticities of energy are the
greatest ones in the equation. The short term elasticities of equation 8 in the SECM are less than
their counterpart long run elasticities in equation 5 in the cointegrated model. The coefficients
show various signs in different methods implying that they do not follow a lucid rule. In spite
of no autocorrelationt, all the determination coefficients and adjusted determination coefficients
are negative
Therefore, the results of equation 8 are unreliable due to the low explanatory strength and
conflicting results in various methods. Ignorant of this unreliability, the urbanization- and
labour-policies are the most effective ones. Owing to the high effect of urbanization on energy
consumption in long run, it plays an important role in changing the amount of energy
consumption, even if the results of equation 8 are unreliable.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2176 www.globalbizresearch.org
Table 14: Estimation results of equation 8 in the ECM model (short run elasticities)
Limited information methods (single-equation) Full information methods (system of equations)
2SLS W2SLS LIML 3SLS FIML GMM
Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. t-stat. Coef. z-stat. Coef. t-stat.
C -0.0969 -0.3124
(0.75)
-0.0969 -0.3576
(0.72)
-14.1257 -0.0000
(0.99)
-0. 1257 -0.4605
(0.64)
0.3134 0.2026
(0.83)
-0.0368 -0.1772
(0.85)
LCO -0.0324 0.0486
(0.96)
-0.0324 0.0556
(0.95)
0.3774 0.0000
(1.00)
0.3774 0.5384
(0.59)
3.7145 0.2831
(0.77)
-0.0999 0.2172
(0.82)
LGDP -0.5327
-0.3365
(0.73)
-0.5327
-0.3852
(0.70)
0.7561 0.0000
(1.00)
0.7561 0.7881
(0.43)
-1.1559 -0.1641
(0.86)
1.4377
2.9616**
(0.00)
LFD -0.0236 -0.1406
(0.88)
-0.0236 -0.1609
(0.87)
-0.2270 0.0000
(1.00)
-0.2270 -1.0211
(0.31)
-0.0177 0.0998
(0.99)
-0.3565 -1.6579
(0.10)
LLAB 1.5823 0.6872
(0.49)
1.5823 0.7866
(0.43)
-1.2740 0.0000
(1.00)
-1.2740 -0.6018
(0.54)
-2.8576 -0.2486
(0.80)
-2.1021
-
1.8776*
(0.06)
LCAP 0.6825 -0.6649
(0.57)
0.6825 -0.6453
(0.52)
-0.9227 -0.0003
(0.99)
-0.9227 -0.6945
(0.48)
-4.3907 -0.3026
(0.76)
-1.3042
-1.3627
(0.17)
LU 4.0947 2.2864
(0.77)
4.0947 2.3278
(0.74)
16.4626 2.0452
(0.96)
16.4626 1.3312*
(0.18)
19.3151 0.2236
(0.82)
12.9166
1.9523*
(0.05)
DR 0.0661 0.3666
(0.71)
0.0661 0.4197
(0.67)
-0.0218 -0.0002
(0.99)
-0.0218 -0.1233
(0.90)
-0.4277 0.2444
(0.80)
-0.0478
-0.4038
(0.68)
𝜀�̂�−1 0.5202 0.1896
(0.85)
0.5202 0.2170
(0.82)
-3.5789 -0.0002
(0.99)
-3.5789 -1.9324*
(0.05)
-1.9161 -0.4064
(0.68)
-4.3837
-
1.8676*
(0.06)
R2 -0.8942 -0.8942 -4.5935 -1.7571 -5.3914 -4.6857
Adjusted R2 -1.4168 -1.4168 -6.1365 -2.5177 -7.1546 -6.2542
D.W. 2.0655 2.0655 1.5350 1.5150 1.2322 1.5834
2SLS, WSLS, LIML, 3SLS, and FIMLare the abbreviations of the Two-Stage Least Square, Weighted Two-Stage Least Square, Limited Information Maximum
Likelihood, Three-Stage Least Square, and Full Information Maximum Likelihood.
Probabilities are written in parentheses.
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2177 www.globalbizresearch.org
Table 15 displays the classical tests results of the SECM including normality and
autocorrelation which mainly reject the classical assumptions. The normality test is run for each
equation individually and for the system of equations as a whole. Based on the resulted Jarque-
Bera statistics, the distribution of residuals is abnormal. The System Residual Portmanteau test
with 12 lags is employed to examine the autocorrelation of residuals which supports the
hypothesis autocorrelation (about which the DW statistics is silent due to their location in the
indecisive zone).
Table 15: Residuals diagnostic tests results of the ECM model
Test Equation Limited Information Methods Full Information Methods
2SLS W2SLS LIML 3SLS FIML GMM
H0 :Normality
(Jarque Bera)
1 24.3336
(0.00)
24.3336
(0.00)
40.2391
(0.00)
42.2135
(0.00)
0.1317
(0.93)
13.0880
(0.00)
2 1.1853
(0.55)
1.1853
(0.55)
0.0152
(0.99)
0.8071
(0.66)
1.0643
(0.58)
0.0770
(0.96)
3 12.8459
(0.00)
12.8459
(0.00)
0.0322
(0.98)
0.1006
(0.95)
1.0564
(0.35)
0.3562
(0.83)
Joint 38.3649
(0.00)
38.3649
(0.00)
NAa 43.1213
(0.00)
3.2524
(0.77)
13.5213
(0.03)
H0 :No Autocorrelation (SRPTb):
Significant lags (up to 12 lags)
NSSc NSS NA 2 1 2
Probabilities are written in parentheses.
a Not Applicable
b System Residual Portmanteau Test
c No Significant Statistic
4. Discussion
In general, the economic and environmental policies require time to reveal their overall
effects, perhaps many years, if not many decades. Moreover, the elasticities of economic growth
are greater than those of environmental pollution implying that environmental pollution is not
a good reason to reduce the economic growth. So, the governors are advised to make a
compromise between economy and environment by improving the economic infrastructure and
developing more effective environmental- and cultural-policies. Methodologically speaking, all
the six methods imply that the SECM does not work well in this case due to the severely low
determination coefficients and the unexpected signs of the ECM coefficients.
The long-term solutions are more effective than the short-term ones showing the time-
consuming nature of the environmental, economic, and energetic policies. In the long run
equilibrium, the strongest interactions are involved among CO2 emissions, GDP, and energy
consumption, although it is urbanization which presents the most profound impacts on the
energy consumption. Both the long and short run elasticities of environmental pollution
(equation 3 and 6) are inelastic whereas those of economic growth (equation 4 and 7) are elastic.
It means that, both in long and short run, the economic variables increase the environmental
pollution while their effects on the economic growth are much higher than their effects on the
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2178 www.globalbizresearch.org
environmental pollution. So the environmental pollution provides a flimsy pretext to impose a
limit on economic activities. Rather than restriction on economic growth, the infrastructure
improvement paves the way for rising both the environmental quality and economic growth
together, having no qualms about the economic impacts on environment.
Moreover, economic growth, energy consumption and trade openness increase the
environmental pollution both in long and short run.. The acceptance of PHH and the positive
nexus between economic growth and environmental pollution are two clear signals for the
location of Iran economy on the ascending phase of EKC. It implies that the economic activities
are pollutant; energy is consumed insufficiently; those kinds of energy are consumed which are
pollutant; the pollutant technologies are imported and the energy-consuming products are
exported. They are persuasive evidence for tightening the environmental regulations. However,
the negative nexus of financial development and environmental pollution in the long and run
advocates the environmentally-efficient allocation of finance for economic activities. In
addition to the economic policies, the cultural strategies should be developed due to the positive
relationship between urbanization and energy consumption implying that consumerism is an
inveterate concomitant of urbanization.
Consequently, despite the reliability of the results of the SECM, the policy-makers are
suggested to encourage the green economic sectors by formulating the long run environmental
policies. In addition, the cultural and energetic programs should be implemented to increase the
environmental quality without stopping the economic growth. There are some examples for
these strategies in the conclusion section which are explained in more details.
5. Conclusion
This study estimates the long and short term elasticities of environmental pollution
economic growth, and energy using the cointegrated and Error Correction simultaneous models
in Iran from 1974 to 2012. Two methodologies are employed for the estimation: Limited
Information methodology (2SLS, W2SLS, and LIML) and Full Information methodology
(3SLS, GMM, and FIML).
On the one hand economists believe that Iran has an emerging economy needing an
enormous growth in economic activities to increase income, employment, and welfare. On the
other hand, environmentalists identify the environment of Iran as a heavily polluted one in the
world requiring either reduction in or amendment to the pollutant economic involving the
intensive energy-using sectors. Since economic activities in Iran is concentrated on the energy
sectors, especially the fossil fuel ones such as oil and gas, economic growth is a surplus to the
environmental requirements in Iran. This study tries to make a compromise between those
economists and environmentalists on the reciprocally destructive influence of environment and
economy.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2179 www.globalbizresearch.org
The results put many suggestions forward: exerting more strict environmental-policies;
improving the economic infrastructure to the less energy-using and greener one; and designing
more effective cultural-strategies. Moreover, the long term elasticities are much higher than
their according short term elasticities implying that long run policies are more effective; and
the environmental, economic, and energetic plans need time to exhibit their own overall effects.
The strongest pattern of interaction is observed among environmental pollution, economic
growth, and energy consumption except for the urbanization which shows the most intense
relationship with the energy consumption both in short and long term. It represents
consumerism as the concomitant of urbanization exhorting the governors to adopt more
effective cultural-strategies.
Based on the above-mentioned discussions, the conclusions and suggestions are analyzed in
the following subsections from two distinctive point of view: 1) economy and environment; 2)
energy and environment.
5.1 Economy and environment
Economic growth plays a fatal role in the environment of Iran which can be resolved by
developing greener economic sectors. For example, many economic sectors manufacture the
products with a short lifetime after which they are left in the environment, such as
petrochemical products. In contrast, many service subsectors, such as financial firms, offer
green commodities. It is the development of these green economic subsectors which provides
the simultaneous development of environment and economy, especially in the long run.
However, many service subsectors are intensively energy-using, like transportation. The
governors are advised to make these subsectors environmentally, economically, and
energetically more efficient to provide another solution to the compromise between economy
and environment. The role of commodities’ properties, green economic sectors, and pollutant
activities are analyzed subsequently.
The nexus of environment and economy can be determined by the chemical and physical
characteristics of the commodities produced in the economy. On the one hand, the economy of
Iran relies heavily on the petrochemical products, polluting the environment. Many
petrochemical products are non-recyclable and they are amassed in the ecosystem with the
unchanging mode such as plastics. Many of them are the noxious gases which released into the
air such as Ammonia, Ethylene, Freon-12, pesticides, herbicides etc. in addition to the polluting
manufactures, it is the waste-products exacerbating the environmental problems for instance
greenhouse gas emissions of the manufactures (CO2, NO2, SO2, etc). On the other hand, the
environment of Iran suffers from a traditional system of waste management with a perfunctory
glance at the classification, disposal, and recycle of the refuse. Since the polluting nature of the
economic products and the traditional system of waste management are two sides of the same
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2180 www.globalbizresearch.org
coin, urging the policy-makers to transit the economy from the polluting products to the
environmentally-friendly ones; to employ the more efficient technologies for reducing the by-
products of the manufactures; and to enhance the waste management. Achieving these aims
requires a 3-dimensional forum of policies ranging from incentive policies (like making loans
to the projects, producing the energy-efficient and recyclable goods during an environmentally-
friendly process), preventive policies (like imposing environmental tax or asking official
permission for either the production of non-recyclable products or the polluting activities), and
cultural policies (like advertisements and educational clips about the environmental protection
in the media).
This study represents the financial sector of Iran as a green sector, development of which
plays a dual role both in the environmental improvement and the economic growth. It shows
that the financial firms respect the environmental issues in financing the proposed projects.
Although the positive effects of financial development on the environment and economy are
modest, it can be nominated as an alternative to replace the other polluting sectors of economy.
The governors are suggested to encourage people for investing in the financial institutes such
as banks and bourses. Moreover, the researchers are advised to search for the other economic
sectors with a positively dual role in economy and environment. Development of these green
sectors paves the way for bringing peace between economy and environment. However, there
are environmentally-unfriendly economic-sectors with a large scale in Iran such as trade and
energy sectors. On the one hand, the negative role of trade on the environment can be due to
the import of the energy-consumptive and environmental-polluting commodities rather than the
energy-efficient and environmentally-friendly technologies (coinciding with the PHH). On the
other hand, it is owing to the export of energy-consuming and energy-intensive products, for
instance the petrochemical ones. Clearly, the largest segment of Iran export is assigned to the
oil and petrochemical products which are heavily polluting. The governors are advised to
impose some environmental strategies in the customs procedure like asking environmental
permission, the license of energy-efficiency standard, and a higher tariff on the trade of
polluting goods.
Consequently, there are some green economic sectors, such as financial firms, in Iran which
should be developed notwithstanding the polluting ones like trade and energy. Many policies
are suggested in this study focused on the waste management, economic transition, cultural
activities, and customs regulations which are more effective in the long run.
5.2 Energy and environment
Energy consumption in Iran, just like economic growth, has a fatal role in the environment
since the economy of the country is based on the production of the fossil fuel energies, such as
oil and gas and the derivatives, which are extremely polluting the environment. There is no
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2181 www.globalbizresearch.org
doubt that Iran is the one of the greatest oil and gas exporters in the world with large segment
of petrochemical products in the portfolio of the economic productions. In addition to
production, the consumption of those energies is an added impetus behind the negative effect
of energy on the environment in Iran which is an inveterate concomitant of urban population
agglomeration in many developing countries.
The production of fossil fuel energies and the derivatives pollutes the environment in Iran,
especially owing to the substantial portion of these products in the GDP of the country. All the
steps of producing these products are polluting: locating the oil and gas fields, drilling, and
extraction and recovery. For example, greenhouse gases emissions from the transportation,
drilling, and extraction devices and plants; the drilling fluids, the chemical substances for
preparing the holes; and the undesirable leakage of the noxious substances. The environmental
pollution of Iran increases due to the production and extraction of those energies by which the
other countries increase their own economic growth. The environment of Iran not only does
suffer from the production of these pollutants but also it is threatened the consumption of them
by the urban population.
Consumption of energy is another stimulus to pollute the environment in Iran in which the
urban population plays a key role. The refractory consumerism not only is inherited in the vast
majority of Iranian urban population but also it is deeply rooted in the inefficiently energy-
using infrastructure of the mega cities of the country, for example a dearth of standard
transportation system. It exhorts the governors to adopt the cultural policies over the
environmental issues or imposing the environmental tax on the consumption of energy-using
goods to increase the price and decrease the demand. Furthermore, the encouragement for the
rural life is another policy which can be implemented by economic incentives (like increase in
the rural loans and urban taxes) and cultural incentives.
As a conclusion, the energy production and consumption are two side of the same coin in
the environmental pollution in Iran needing economic, energetic, environmental, and cultural
policies to solve the issues, especially the long run strategies. As a future study, the nexus of
environmental pollution with the other economic subsectors can be examined to find more
green economic activities. The growth of these green economic sectors changes the nature of
the economic growth from a polluting character into a green one.
Reference
Al-Mulali U. and Sab C.N.B.C. (2013). “Inestigating the Validity of Pollution Haven Hypothesis in the
Gulf Cooperation Council (GCC) Countries”, Energy Policy, 60, 813-819.
Alves, D.C.O. and Bueno, R.D. (2003). “Short-Run, Long-Run and Cross Elasticities of Gasoline
Demand in Brazil, Energy Economics”, 25, 191-199.
http://dx.doi.org/10.1016/S0140-9883 (02)00108-1
Baek, J. (2015). “Environmental Kuznets curve for CO2 emissions: The case of Arctic countries”, Energy
Economics, 50, 13-17.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2182 www.globalbizresearch.org
Central Bank of the Islamic Republic of Iran (2014). Economic Time Series Database, Economic
Research and Policy Department. http://tsd.cbi.ir/
Central Intelligence Agency (CIA), (2014). The World Factbook, Available at:
https://www.cia.gov/library/publications/resources/the-world-factbook/geos/ir.html
Cobb, C. W. and Douglas P. H. (1928). “A theory of production”, The American Economic Review, 18,
1, 139-165.
Dickey, D. A. and Fuller W. A. (1979). “Distribution of the estimators for autoregressive time series with
a unit root”, Journal of the “American Statistical Association, 74, 366, 427-431.
Duffy, C. A. (2015) “A climate change poem for today: The Question by Theo Dorgan”, The Guardian,
01.June.2015. Available at:
http://www.theguardian.com/environment/2015/jun/01/a-climate-change-poem-for-today-the-question-
by-theo-dorgan
Duro, J. A. (2013). “Weighting vector and international inequality changes in environmental indicators:
An analysis of CO2 per capita emissions and Kaya factors”, Energy Economics, 39, 122-127
Francis, P. (2015) “Encyclical Letter”, Vatican Press, Available at:
http://w2.vatican.va/content/dam/francesco/pdf/encyclicals/documents/papa-
francesco_20150524_enciclica-laudato-si_en.pdf
Greene W.H., (2012). “Econometric Analysis”, Pearson. 7th edition.
Engle, R. F. And Granger, C. W. J. (1987). “Co-integration and error correction: Representation,
estimation, and testing”, Econometrica, 55, 2, 251-276.
Grossman, G.M. and Krueger A.B. (1991). “Environmental Impact of a North American Free Trade
Agreement” Working Paper 3914. National Bureau of Economic Research, Cambridge, MA.
Gujarati, D. (2004). “Basic Econometrics”, McGraw-Hill. 4th edition.
Kaldor, N. (1966). “Causes of the slow rate of growth of the United Kingdom”, Cambridge University
Press, Cambridge.
Kaldor, N. (1967). Strategic factors in economic development, Cornell Uni-versity Press, Ithaca, NY.
Kaya, Y. (1989). “Impact of carbon dioxide emission control on GNP growth: Interpretation of proposed
scenarios”, paper presented to the energy and industry subgroup, response strategies working group,
Intergovernmental Panel on Climate Change, France, Paris.
Kaya, Y. and Yokoburi, K. (1997). “Environment, energy, and economy: strategies for sustainability”,
United Nations University Press, Tokyo.
Obama, B. (2015). “Global warming”, Bloomberg, Available at:
http://www.bloomberg.com/politics/articles/2015-04-19/obama-no-greater-threat-to-planet-than-
climate-change
Obama, B. (2015). Weekly address: climate change can no longer be ignored, Office of the Press
Secretary: The White House Retrieved, Available at:
https://www.whitehouse.gov/the-press-office/2015/04/18/weekly-address-climate-change-can-no-
longer-be-ignored.
Omri, A. (2013). “CO2 Emissions, Energy Consumption and Economic Growth Nexus in MENA
Countries: Evidence From Simultaneous Equations Models”, Energy Economics, 40, 657-664.
Omri A., Nguye D.K., Rault C. (2014). “Causal Interactions Between CO2 Emissions, FDI, and
Economic Growth: Evidence From Dynamic Simultaneous-Equation Models”, Economics Modelling,
42, 382-389.
Omri, A. (2015) “Financial development, environmental quality, trade and economic growth: What
causes what in MENA countries”, Energy Economics, 48, 242-252.
Pachauri, R. K. (2014). “Climate change impacting entire planet, raising risk of hunger”, floods, conflict
– UN report UN Daily New, 31.March.2014.
Phillips, P. C. B. and Perron, P. (1988) “Testing for a unit root in time series regression”, Biometrica,
75, 2, 335-346.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2183 www.globalbizresearch.org
Ramanathan, R. (1999). “Short- and Long-Run Elasticities of Gasoline Demand in India: An Empirical
Analysis Using Cointegration Techniques”, Energy Economics, 21, 321-330.
http://dx.doi.org/10.1016/S0140-9883 (99)00011-0
Report of the Intergovernmental Panel on Climate Change (IPCC) Yokohama, Japan (2014)
Rodrik, D. and May (2009). “Growth after the crisis”, Harvard Kennedy School, Cambridge, MA.
Ruijven, B. J. V., Daenzer, K., Fisher-Vanden, K., Kober, T., Paltsev, S., Beach, R. H., Calderon, S. L.,
Calvin, K., Labriet, M., Kitous, A., Lucena, A. F. P. and Vuuren D. P. V. (2015). Accepted manuscript:
“Baseline projections for Latin America: base-year assumptions, key drivers and greenhouse emissions”,
Energy Economics.
Sargan, J. D. and Bhargava, A. (1983). “Testing residuals from least squares regression for being
generated by the Gaussian random walk”, Econometrica, 51, 1, 153-174.
Szirmai, A. (2012). “Industrialisation as an engine of growth in developingcountries 1950–2005”,
Structural Change and Economic Dynamics 23 (December (4)), 406–420.
Szirmai, A. (2015), “Socio-Economic Development”, second ed. CambridgeUniversity Press,
Cambridge.
Taghvaee, V. M. and Hajiani, P. (2014). “Price and income elasticities of gasoline demand in Iran: Using
static, ECM, and dynamic models in short, intermediate, and long run”, Modern Economy, 5, 939-950.
http://dx.doi.org/10.4236/me.2014.59087
Taghvaee, V.M., Parsa H. (2015). “Economic growth and environmental pollution in Iran: Evidence from
manufacturing and services sectors”, Custos E Agronecio Online, 11(1), 115-127.
Taghvaee, V.M., Shirazi, J.K. (2014). “Analysis of the relationship between economic growth and
environmental pollution in Iran (evidence from three sections of land, water and atmosphere)”, Indian
Journal of Scientific Research, 7(1), 31-42.
World Bank Databases (2014). World Development Indicators, Available at:
http://www.data.worldbank.org
Yale University Database, Environmental Performance Index, Available at:
http://epi.yale.edu/
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2184 www.globalbizresearch.org
Appendices:
Table 1: Unit root test results: Augmented Dickey Fuller (ADF) and Phillips Perron (PP)
Variables Test Teste
d in
Intercept Intercept and
trend
None Stationaritya
τ
Statistic Prob. τ Statistic Prob.
τ
Statistic Prob.
lCO
ADF
Level -0.2591 0.92 -2.2274 0.46 0.8258 0.88
I(1) First -4.9901*** 0.00 -5.0464*** 0.00
-
4.9369*** 0.00
PP
Level -0.3882 0.90 -2.2017 0.47 0.7596 0.87
I(1) First -4.9440*** 0.00 -4.9575*** 0.00
-
4.9139*** 0.00
lGDP
ADF
Level -1.7211 0.41 -1.7712 0.69 -0.4332 0.51
I(1) First -3.9342*** 0.00 -4.1728** 0.01
-
3.9786*** 0.00
PP
Level -1.8194 0.36 -1.7800 0.69 -0.3750 0.54
I(1) First -3.9435*** 0.00 -3.7549** 0.03
-
3.9894*** 0.00
LE
ADF
Level -1.4835 0.53 -2.0945 0.53 1.9177 0.98
I(1) First -6.1147*** 0.00 -6.1543*** 0.00
-
5.3591*** 0.00
PP
Level -1.5103 0.51 -2.1374 0.50 2.1124 0.99
I(1) First -6.1122*** 0.00 -6.1787*** 0.00
-
5.5977*** 0.00
LOP
ADF
Level -1.6021 0.47 -1.6663 0.74 -1.2024 0.20
I(1) First -5.1127*** 0.00 -5.0384*** 0.00
-
5.0427*** 0.00
PP
Level -1.9324 0.31 -2.0205 0.57 -1.1140 0.23
I(1) First -5.1328*** 0.00 -5.0601*** 0.00
-
5.0640*** 0.00
LU
ADF Level -2.3659 0.15 -0.1010 0.99 2.0451 0.98
I(1) First -2.6197* 0.09 -3.5712** 0.04 -2.3895** 0.01
PP Level -5.3469*** 0.00 -1.8398 0.66 11.4589 1.00
I(0) First -2.2407 0.19 -2.6912 0.24 -2.1667** 0.03
LFD
ADF
Level -3.1428 0.03** -1.9135 0.62 -0.3609 0.54
I(0) First -5.6625*** 0.00 -5.6913*** 0.00
-
5.7074*** 0.00
PP
Level -2.4346 0.13 -2.3133 0.41 -0.3609 0.54
I(1) First -5.6625*** 0.00 -5.6909*** 0.00
-
5.7074*** 0.00
LCAP
ADF
Level -1.1624 0.68 -2.7660 0.21 0.4727 0.81
I(1) First -3.1320** 0.03 -2.9488 0.15
-
3.3053*** 0.00
PP
Level -2.3549 0.16 -2.7455 0.22 1.7816 0.98
I(1) First -3.1874** 0.02 -5.2973*** 0.00
-
3.4144*** 0.00
LLAB
ADF
Level -3.5767** 0.01 -3.4841** 0.05 -1.1093 0.23
I(0) First -3.0367** 0.04 -0.0858 0.99
-
2.8290*** 0.00
PP
Level -0.7881 0.81 -1.7261 0.71 0.4850 0.49
I(1) First -4.3566*** 0.00 -4.3848*** 0.00
-
4.3895*** 0.00
*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively. a I(x) shows the integration degree.
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2185 www.globalbizresearch.org
Table 2: Variables’ coefficients arrangement
Coefficients of the variables
Equation
no. 1 𝐿𝐶𝑂 𝐿𝐺𝐷𝑃 𝐿𝐸 𝐿𝑂𝑃 𝐿𝐹𝐷 𝐿𝑈 𝐿𝐿𝐴𝐵 𝐿𝐶𝐴𝑃 𝑑𝑟 𝑑𝑤
(1) 𝛽10 1 𝛽11 𝛽21 𝛾11 𝛾21 0 0 0 𝛾31 0
(2) 𝛽02 𝛽12 1 𝛽22 𝛾12 0 0 𝛾22 𝛾32 𝛾42 𝛾52
(3) 𝛽03 𝛽13 𝛽23 1 0 𝛾13 𝛾23 𝛾33 𝛾43 𝛾53 0
Table 3: Order condition assessment
No. of the variable
Order condition Identification Predetermined
(K)
Endogenous
(M)
System 7 3
Equation (1) 3 3 𝐾𝑠𝑦𝑠𝑡𝑒𝑚 − 𝐾𝐸𝑞.1 > 𝑀𝐸𝑞.1
− 1 Over-identifiable
Equation (2) 5 3 𝐾𝑠𝑦𝑠𝑡𝑒𝑚 − 𝐾𝐸𝑞.2 = 𝑀𝐸𝑞.2
− 1 Just- identifiable
Equation (3) 5 3 𝐾𝑠𝑦𝑠𝑡𝑒𝑚 − 𝐾𝐸𝑞.3 = 𝑀𝐸𝑞.3
− 1 Just- identifiable
Table 4: Rank condition assessment
Equation
no. Matrices
Rank
condition
(1) [0 𝛾22𝛾23 𝛾32
]; [0 𝛾32𝛾23 𝛾43
]; [0 𝛾52𝛾23 0
]; [𝛾22 𝛾32𝛾33 𝛾43
]; [𝛾22 𝛾52𝛾33 0 ]; and [
𝛾32 𝛾52𝛾43 0 ] Satisfied
(2) [𝛾21 0𝛾13 𝛾23
] Satisfied
(3) [𝛾11 0𝛾12 𝛾52
] Satisfied
Table 5: Exogeneity test results
Equation Statistic Wald test 𝜏11 = 𝜏21 = 0 Null
hypothesis
Simultaneity
hypothesis
Significance
level Prob. Degree of
freedom
(6)
F-
statistic 6.9461 0.00 2, 31 Rejected Accepted 1%
Chi-
square 13.8923 0.00 2 Rejected Accepted 1%
Table 6: Engel and Granger (EG) and Augmented Engel and Granger (AEG) cointegration tests
results
Method Equation Test Intercept Intercept
and
trend
None
τ
Statistic
Prob. τ
Statistic
Prob. τ
Statistic
Prob.
2SLS (1)
EG -3.6416 (0.00) -4.2177 (0.00) NA
AEG -4.3407 (0.00) -4.2356 (0.00) -4.4012 (0.00)
(2) EG -4.3840 (0.00) -4.4794 (0.00) NA
Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB)
An Online International Research Journal (ISSN: 2306-367X)
2017 Vol: 6 Issue: 1
2186 www.globalbizresearch.org
AEG -4.4618 (0.00) -4.3626 (0.00) -4.5312 (0.00)
(3) EG -4.3105 (0.00) -4.6975 (0.00) NA
AEG -4.8102 (0.00) -4.6633 (0.00) -4.8917 (0.00)
W2SLS
(1) EG -3.6416 (0.00) -4.2177 (0.00) NA
AEG -4.3407 (0.00) -4.2356 (0.00) -4.4012 (0.00)
(2) EG -4.3840 (0.00) -4.4794 (0.00) NA
AEG -4.4618 (0.00) -4.3626 (0.00) -4.5312 (0.00)
(3) EG -4.3105 (0.00) -4.6975 (0.00) NA
AEG -4.8102 (0.00) -4.6633 (0.00) -4.8917 (0.00)
LIML
(1) EG -3.7035 (0.00) -4.3186 (0.00) NA
AEG -4.4588 (0.00) -4.3266 (0.00) -4.5204 (0.00)
(2) EG -4.3840 (0.00) -4.4794 (0.00) NA
AEG -4.4618 (0.00) -4.3626 (0.00) -4.5312 (0.00)
(3) EG -4.3105 (0.00) -4.6975 (0.00) NA
AEG -4.8102 (0.00) -4.6633 (0.00) -4.8917 (0.00)
3SLS
(1) EG -3.6416 (0.00) -4.2177 (0.00) NA
AEG -4.3407 (0.00) -4.2356 (0.00) -4.4012 (0.00)
(2) EG -3.7332 (0.00) -4.0829 (0.00) NA
AEG -4.1063 (0.00) -4.9984 (0.01) -4.1685 (0.00)
(3) EG -4.0864 (0.00) -4.6305 (0.00) NA
AEG -4.8045 (0.00) -4.6633 (0.00) -4.8868 (0.00)
FIML
(1) EG -3.7168 (0.00) -4.2009 (0.00) NA
AEG -4.3036 (0.00) -4.1931 (0.00) -4.3618 (0.00)
(2) EG -4.4647 (0.00) -3.7091 (0.00) NA
AEG -3.7229 (0.00) -3.6211 (0.04) -3.7764 (0.00)
(3) EG -4.2479 (0.00) -5.2790 (0.00) NA
AEG -5.5705 (0.00) -5.4170 (0.00) -5.6443 (0.00)
GMM
(1) EG -3.9189 (0.00) -4.6050 (0.00) NA
AEG -4.7605 (0.00) -4.6365 (0.00) -4.8129 (0.00)
(2) EG -4.1626 (0.00) -4.3074 (0.00) NA
AEG -4.2692 (0.00) -4.1890 (0.01) -4.3222 (0.00)
(3) EG -3.8580 (0.00) -4.4928 (0.00) NA
AEG -4.6528 (0.00) -4.4697 (0.00) -4.6755 (0.00)
Table 7: Cointegration Regression Durbin Watson (CRDW) results
Equation 2SLS W2SLS LIML 3SLS FIML GMM
(1) 1.3695 1.3695 1.4105 1.3695 1.4042 1.3636
(2) 1.4718 1.4718 1.4718 1.3201 1.0951 1.4042
(3) 1.6246 1.6246 1.6246 1.6209 1.7915 1.5365