Elasticities of Energy, Environment, and Economy in Long...

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





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

http://www.sciencedirect.com.ezproxy.bu.edu/science/article/pii/S0954349X15000351#bib0230

http://www.sciencedirect.com.ezproxy.bu.edu/science/article/pii/S0954349X15000351#bib0255





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





(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





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





(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





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





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.





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.





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



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





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.









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









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



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.





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.









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









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


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.





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.





Table 12: Estimation results of equation 6 in the ECM model (short run elasticities)




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









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.





Table 13: Estimation results of equation 7 in the cointegrated model (short run elasticities)




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




*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively





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.





Table 14: Estimation results of equation 8 in the ECM model (short run elasticities)




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




*, **, and *** denote statistical significance at 10%, 5%, and 1% levels, respectively





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


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


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





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.





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





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





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.





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.





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/

http://www.custoseagronegocioonline.com.br/numero1v11/OK_7_growth_english.pdf





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.





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





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

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