An empirical analysis of CO2 emission in Pakistan using EKC hypothesis

Abstract

Purpose
– The purpose of this paper is to fill the gap between energy and growth literature in Pakistan. In this regard, the authors investigated the environmental Kuznets curve (EKC) hypothesis and concluded the relationship between carbon emission and other four variables (energy consumption, economic growth, trade openness and population) at the same time. It is hoped that the policy implications of this research will provide a strong base to address the problem of environmental degradation in Pakistan.

Design/methodology/approach
– This study investigates the relationship between CO2 emission, economic growth, energy consumption, trade‐liberalization, and population density by using the EKC hypothesis for Pakistan. The cointegration analysis with Auto Regressive Distributed Lag (ARDL) bound testing approach is employed over time series data from the period 1971 to 2008. The stability of model was also checked at the end.

Findings
– The results of the study do not support EKC in a short‐run, whereas the long‐run inverted U shaped hypothesis was confirmed between carbon emission and growth, energy consumption, trade openness and population density. Thus, findings of the study confirmed that EKC was a long‐run phenomenon in the case of Pakistan and most interestingly, with all other explanatory variables, population density also appeared to be a contributor to environmental degradation in Pakistan.

Originality/value
– This work is original and a new contribution to single country analysis. It is first time that carbon emission is empirically tested for all four major determinants (economic growth, energy consumption, trade‐liberalization, and population density) at the same time. The long ranged time series data of 38 years enhances the validity of results. The most surprising finding of this research is that the population density also contributes to environmental degradation in Pakistan.

Full text

An empirical analysis of CO
2
emission in Pakistan using
EKC hypothesis
Khalid Ahmed and Wei Long
School of Economics, Wuhan University of Technology,
Wuhan, People’s Republic of China
Abstract
Purpose – The purpose of this paper is to fill the gap between energy and growth literature in Pakistan.
In this regard, the authors investigated the environmental Kuznets curve (EKC) hypothesis andconcluded
the relationship between carbon emission and other four variables (energy consumption, economic
growth, trade openness and population) at the same time. It is hoped that the policy implications of this
research will provide a strong base to address the problem of environmental degradation in Pakistan.
Design/methodology/approach – This study investigates the relationship between CO
2
emission,
economic growth, energy consumption, trade-liberalization, and population density by using the EKC
hypothesis for Pakistan. The cointegration analysis with Auto Regressive Distributed Lag (ARDL)
bound testing approach is employed over time series data from the period 1971 to 2008. The stability
of model was also checked at the end.
Findings – The results of the study do not support EKC in a short-run, whereas the long-run inverted
U shaped hypothesis was confirmed between carbon emission and growth, energy consumption, trade
openness and population density. Thus, findings of the study confirmed that EKC was a long-run
phenomenon in the case of Pakistan and most interestingly, with all other explanatory variables,
population density also appeared to be a contributor to environmental degradation in Pakistan.
Originality/value – This work is original and a new contribution to single country analysis. It is
first time that carbon emission is empirically tested for all four major determinants (economic growth,
energy consumption, trade-liberalization, and population density) at the same time. The long ranged
time series data of 38 years enhances the validity of results. The most surprising finding of this
research is that the population density also contributes to environmental degradation in Pakistan.
Keywords Pakistan, Population distribution, Energy consumption, Economic growth, Trade,
Environmental Kuznets curve, CO
2
emission, Population density, Cointegration
Paper type Research paper
1. Introduction
The increasingly widening environmental concerns linke d to the adverse climate change
impact on earth have recently persuaded world economies to use the green energy and to
considerably reduce emission of CO
2
. According to the recent studies, the large part of
carbon emission will be coming from the developing economies due to the rapid
economic growth. Ever since the beginning of industrialisation in 1970s, energy
consumption has largely been on the rampant which multiplied the international trade
but has seemingly posited some serious threats to environment. The process of
globalization, which advantages the developing countries to nurture their economies
through reduced investment and trade barriers including the transfer of technology,
mobilised capital and labour. However, it also shifts the burden of increasing share of
environmental pollution due to the increase in energy consumption. The ongoing
process of industrialization in developing countries is highly vulnerable to global
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1477-0024.htm
Journal of International Trade Law
and Policy
Vol. 12 No. 2, 2013
pp. 188-200
qEmerald Group Publishing Limited
1477-0024
DOI 10.1108/JITLP-10-2012-0015
JITLP
12,2
188

climate as a whole. Subsequently, the situation seems to have been further complex
because neither the environmental degradation nor the economic growth can be
compromised. However, turning the simple economic growth to environmental friendly
growth through technique and technological effect is likely to be a way forward.
As witnessed, most of the developing countries’ growth is export oriented and
larger part of their GDP comes from manufacturing sector. This trend of industrial led
growth has also rapidly increased the greenhouse gases (GHG) emission trend. Owing
to global externality, the adverse effects of emission has impact all around the globe. It
is now growing concern both in developing and advanced countries to reduce or at
least maintain this trend through effective policy reforms and multilateral agreements.
However, most of the OECD countries have already started different measures towards
environmental safeguard. Whereas developing countries are the most vulnerable to
environmental change, are still far behind in term of measures. So, there is an immense
need of policy reforms both, at country and regional level, to cop this issue. This study
is also an attempt to empirically analyse Pakistan’s (one of the South Asian developing
countries) direct and assist policy formulation. For this purpose, environmental
Kuznets curve (EKC) provides effective hypothesis for energy and growth nexus with
other potential variables like which contribute to environmental degradation.
Initially, the EKC was an inverted U-shaped relationship between income and income
inequality proposed by Simon Kuznets in 1955. The EKC is adopted in environmental
economic literature since 1990s, the prominent researchers such as, Grossman
and Krueger (1991, 1995), Shafik and Bandyopadhyay (1992), Lucas et al. (1992),
Panayotou (1993, 1997), Selden and Song (1994) and Vincent (1997) found an inverted
U-shape relationship between income and pollution for various pollutants. For two
decades, the EKC has been hypothesized in empirical studies and various statistical tests
have been used on panel and time series data techniques related to group of countries,
as a single country and cross-country data as well. However, it is claimed that in order to
acquirethe better findingsand implications, single country analysis is more suitable option
because countries differ in size, geography and other economic characteristics. It is also
suggested that EKC shows pollution and other variables of environmental degradation
relationship with time so the EKC is a long-run phenomenon (Lindmark, 2002). As a result
the time, series data technique is advantageous over other techniques (Akbostanci et al.,
2009). In this study, we have hypothesized the EKC for energy consumption, trade,
economic growth and population density. This empirical study is first of its kind which
has included four major variables of environmental degradation in the case of Pakistan.
The economy of Pakistan has shown enormous growth during 2001 to 2007.
Consequently, the energy consumption especially in industrial sector has added pollution
to the environment, which resultantly raises concerns among domestic and international
environmental protection agencies. In Pakistan, most of the CO
2
emission is generated by
natural gas which is almost the half of the total emission. Although Pakistan’s growing
economic stability witnessed handsome rise in income level, it was only compromised at
the cost of further deterioration and increase in environmental pollution. During the last
decade, transportation sector grew rapidly with simultaneous increase in number of
individual and commercial vehicles. As per the statistics, country’s per capita energy use
has increased by (40 percent) from 2001 to 2007,where the total energy used by industrial
and manufacturing sector has increased by (43 percent) during the year (2008-2009).
Unfortunately during this span of time, the inefficient and under developed technology
CO
2
emission
in Pakistan
189

further exacerbated the environmental pollution inthe shape of greenhouse gas emission.
Higher demand and lack of technology fuelled up the environmental degradation. Over the
last few decades, environmental pollution has been very serious global issue. To address
this serious threat, the participation of every single country has been effective to mitigate
or considerably reduce the emission level. Taking this into consideration, Government of
Pakistan has been taking remedial action towards the sustainable development path from
time to time. In this regard, Pakistan is also one of those countries which announced the
National Environmental Policy (NEP) in 2005. The basic purpose of this initiative was to
safeguard the natural environment and to ensure healthy atmosphere to the citizens. But
industrial economic growth and dangerous increase in population are still considered to be
two of the biggest challenges for Pakistan towards greenhouse growth. This study is
undertaken in order to comprehensively test the hypothesis of EKC for both in a long-run
and short-run in presence of energy consumption, trade openness, economic growth and
more especially population growth rate.
2. Literature review
The work on EKC started in 1990s when world started to realize that earth average
temperature increased dramatically. In order to address this serious threat to environment,
Earth summit in Reo-de-Janeiro (Brazil) was held to discuss the global issue of climate
change. Researchers of environmental economics hypothesized EKC which received
alarmingly very serious attention rapidly. It was found that the industrialization has
become threatening cause to emit excessive GHG especially CO
2
. Therefore, in order to
assess the interconnection of variables empirically, the first relationship was made
between economic growth and CO
2
emission. The work was first started by the Grossman
and Krueger (1991) to study the effect on NAFTA, However, EKC became more important
when Shafik and Bandyopadhyay’s (1992) contributed in the background study for the
1992 World Development Report stating that environmental quality improvement is
essential for the sustainable development. Further, this study was followed by Shukla and
Parikh (1992), Grossman and Krueger (1995), Shafik (1994), Selden and Song (1995),
Jaeger et al. (1995), Tucker (1995), Jha (1996), Horvath (1997), Barbier (1997), Matyas et al.
(1998), Ansuategi et al. (1998), Heil and Selden (1999), List and Gallet (1999),
Brandoford et al. (2000), Stern and Common (2001), Roca (2003), Friedl and Getzner
(2003), Dinda and Coondoo (2006), Managi and Jena (2008), Coondoo and Dinda (2008), Jalil
and Mahmud (2009) and Akbostanci et al. (2009).
Energy is generally considered as the driving engine of the any growing economy.
Therefore, the rapid economic growth was guaranteed by largely functioning industries
which subsequently required more energy consumption. Drawing on that, energy
consumption was also included for empirical examination in EKC hypothesis. The energy
consumption contributes at the highest to environmental degradation; therefore the
energy-economic growth nexus is included as the important determinant of carbon
emission. Some of the studies in this regard include Hwang and Gum (1991), Stern (1993,
2000), Masih and Masih (1996), Yang (2000), Glasure (2002), Hondroyiannis et al. (2002),
Ghali and El-Sakka (2004), Wolde-Rufael (2006, 2009), Narayan and Singh (2007),
Narayan et al. (2008) and Jalil and Mahmud (2009).
After determining the energy and economic growth, the trade openness is considered to
be the next critical contributor to the environmental degradation. The relationship
between environment and international trade has been empirically investigated but this
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effect depends mainly on the policies implemented within the economy. On the trade
determinant of environmental degradation, there are two types of studies, one argues in
favor and other in against. The studies reported that trade openness was more likely to
influence environment quality negatively include; Suri and Chapman (1998),
Schmalensee et al. (1998), Beghin et al. (1999), Abler et al. (1999), Lopez (1994), Cole et al.
(2000); Antweiler et al. (2001), Copeland and Taylor (2001), Chaudhuri and Pfaff (2002),
Ozturk and Acaravci (2010) and Nasir and Rehman (2011). On the other hand, those who
found trade openness had positive impact believed that trade openness also helped to
counter negative effect in long-term. It helps economy to seek technology and services in
order to attain efficiency. Moreover, after attaining certain level of growth the
environmental degradation also declinesand trade plays vital role for better environment
in long-term. Precisely, the mix-results are found in literature regarding the role of
international trade. The results of the empirical studies in the favour of environment
because of trade openness are Lucas et al. (1992), Shafik and Bandyopadhyay (1992),
Birdsall and Wheeler (1993), Runge (1994), Helpman (1998), Ferrantino (1997) and
Grether et al. (2007).
The other variables which are also considered beside economic growth include
energy consumption, trade openness, technological movement, population growth and
density. The studies which include population as the determinant of environmental
degradation is Dinda (2004), Lean and Shahbaz (2011) and prior to this Panayotou (1997)
indicated population is one the factor contributing to the environmental degradation.
The role of the economic growth rate and population density is also an essential factor.
Booming economic growth and increasing population do increase moderately the
environmental price. Therefore, in our study we have included the population with three
other major factors of environmental degradation in order to sketch the comprehensive
view of economy with all four major possible determinants.
3. Data and model specification
The data used in this study is taken from the World Bank Development Indicators
(WDI) from year 1971 to 2008. For emission we use CO
2
per capita (in metric tons),
growth as country’s annual GDP rate, trade openness as annual trade to GDP ratio, and
population density as annual population growth rate. In this section we will focus on
the theoretical linkage between EKC hypothesis and determinants of energy
consumption, economic growth, and trade openness and population growth. According
to the hypothesis as the income level increases initially it leads to poor environmental
condition as economic activity is multiplied, the more pollution and dirt is spread in
atmosphere and ground but as soon as the income level and growth reaches up to
certain level the environmental quality starts getting better. It is because first the
population ignores the pollution and dirt but after attaining g the high income level
environment pollution bother them and they begin to pay more attention to have better
environmental quality. For example, currently some cities in developing countries may
not be as clean as in developed countries’ cities but after certain level of economic
growth the residents will demand and care for cleanliness. Just like the cities like
Mumbai-India, Saint Palo-Brazil and Shanghai-China may not be as clean as New York,
Tokyo or Berlin but after shifting from the transition economy the environment of the
cities will be cleaner. This trend in public and economy is considered as the inverted
U-shape relationship called EKC hypothesis.
CO
2
emission
in Pakistan
191

After developing the relationship between carbon emission and economic activities
like; growth, energy consumption and international trade. It has been studied that there
is causality between these determinants and emission. It may be in same direction and
may be in different ways and means. The various studies have been made on different
individual countries but the results mainly differ because of the difference in economic
characteristics and national policies in respective country. However, many believed that
the foster in economic activities enhance the energy demand which further deteriorate
the environment, Yu and Jin (1992), Aqeel and Butt (2001), Jobert and Karanfil (2007) and
Soytas et al. (2009).
In some studies the population and trade has been taken as the constant variables
such as Antweiler et al. (2001). But as we mentioned above the difference in policy
instrument the results may vary. Now on the basis of EKC hypothesis we can form a
linear quadric function which creates relationship between carbon dioxide emission
and energy consumption, economic growth, trade openness and population. We will
form the long-run relationship between these variables in order to test the EKC
hypothesis validity, we will estimate as follow:
lnEt¼
a
0þ
a
1lnYtþ
a
2ðlnY tÞ2þ
a
3ðlnYÞ3þ
a
4lnENtþ
a
5lnXtþ
a
6lnPtþ1tð1Þ
Here, we have taken the log(ln) of all variables, where E represents per capita
CO
2
emission, Y per capita real income, EN energy consumption per capita (metric tons),
X is the trade openness ratio, P is population growth, and 1
t
is a standard error term.
According to the EKC hypothesis the sign of
a
1
should be positive, the sign for a
2
negative and a
3
positive may be negative, due to increasing change in energy
consumption the value of
a
4
is also expected to be positive. Where, the trade openness
and population growth are mixed because in case of developed country the value of
a
5
and
a
6
are negative most probably; and positive in case developing country.
The difference in economic structure causes this effect because the economy of
developed countries mainly rely on service industry which is clean and less energy
intensive while on the other hand the economies of the developing countries mainly rely
on manufacturing industry which is energy intensive and comparatively weak in
technology which multiply this effect in result of carbon emission, Grossman and
Krueger (1995).
Amongst the various methods used before, the residual-based approach by Engle
and Granger (1987); maximum likelihood-based approach by Johansen and Juselius
(1990); autoregressive distributor lag (ARDL) by Pesaran et al. (2001), in this study we
have prefer to use ARDL approach as it is more reliable in small samples and avoids
the problems of endogeneity and help to estimate the coefficients in the long-run.
Moreover, the assessment of both short- and long-run effects of independent variable
over dependant variable take place simultaneously and does not require order of
integration during econometric methods such as; unit-root-test. The recent use of
similar approach for different countries study includes; Jayanthakumara et al. (2011),
Saboori et al. (2011) and Lean and Shahbaz (2011).
Now, in order to examine the long-run relationship of the variables (carbon
emission, energy consumption, economic growth, trade openness and population
density) through ARDL bound approach to cointegration. The equation of the model is
as follows:
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DlnEt¼
a
0þX
n
k¼1
a
1kDlnEt2kþX
n
k¼1
a
2kDlnYt2kþX
n
k¼1
a
3kDðlnY t2kÞ2
þX
n
k¼1
a
4kDðlnYt2kÞ3þX
n
k¼1
a
5kDlnENt2kþX
n
k¼1
a
6kDlnXt2k
þX
n
k¼1
a
7kDlnPt2kþ
d
1lnEt21þ
d
2lnYt21þ
d
3ðlnYt21Þ2þ
d
4ðlnYt21Þ3
þ
d
5lnENt21þ
d
6lnXt21þ
d
7lnPt21þ1t
ð2Þ
Now; as per the procedure of ARDL approach the equation (2) is tested in ordinary least
square (OLS) method. The null hypothesis is tested of: H0:
d
1¼
d
2¼
d
3¼
d
4¼
d
5¼
d
6¼
d
7¼0 against its alternative: H1:
d
1¼
d
2¼
d
3¼
d
4¼
d
5¼
d
6¼
d
7¼0
to determine the relationship of no cointegration or no long-run. The F-test is
conducted to determine the existence of long-run relationship amongst the variables.
The critical values of F-statistics are available with lower and upper bound limit (Note:
Table II) in Pesaran (1997) and Pesaran et al. (2001). If the F-statistics is smaller than
the lower critical bound (LCB) limit then the null hypothesis of cointegration cannot be
rejected but if it is greater than upper critical bound (UCB) limit then the null
hypothesis is rejected. The results within the bound limits are inconclusive. After that
we run the model in Schawrtz-Byesian criteria (SBC) and Akaike’s information criteria
(AIC) in order to estimate the long-terrn relationship amongst the variables.
Subsequently, we estimate the error correction model (ECM); the equation (3) is
formulated as follow:
DlnEt¼
a
0þX
n
k¼1
a
1kDlnEt2kþX
n
k¼1
a
2kDlnYt2kþX
n
k¼1
a
3kDðln Yt2kÞ2
þX
n
k¼1
a
4kDðlnYt2kÞ3þX
n
k¼1
a
5kDln ENt2kþX
n
k¼1
a
6kDlnXt2k
þX
n
k¼1
a
7kDlnPt2kþ
u
ECTt21þ1t
ð3Þ
Here; equation (3) represents the vector error correction model (VECM), if the value of
ECT lies between 0 and 21, the adjustments of dependant variable in present period
will fall equivalent to the error value of last period. Where, (ECT
t21
) is the error
correction term, and in the end we estimate the stability of coefficients through
cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ).
4. Empirical results and interpretation
The first necessary step we follow is to investigate the unit roots in variables through
augmented Dickey-Fuller (ADF) and Philips-Perron (PP) initiated by Dickey and Fuller
(1979) and Philips and Perron (1988). It helps to identify the order of variables’
integration and important step in time series data analysis. As Table I shows our
variable are stationary at first difference and ready for bounds test for cointigration.
Then we undertake the F-test using optimum number of lags from vector auto
CO
2
emission
in Pakistan
193

regression (VAR). When F-statistics is obtained which is higher than the upper and
lower bound limits confirm the cointegration (Table II). The short-run results are stated
in Table III which shows there is no short-run relationship amongst variables except
energy consumption which shows positive value but it is slightly affected.
ADF test statistics PP test statistics
Variable Constact Constant and trend Constatnt Constant and trend
lnE 26.098802 *25.925494 *211.17908 *210.71904 *
lnY 27.3781 *27.350643 *25.190726 *25.081491 *
lnEN 24.222188 *24.098763 *25.371414 *25.178689 *
lnX 22.926722 ** 24.560419 25.164466 24.945526
lnP 25.326813 *24.211966 0.157161 22.089497
lnY
2
26.496915 *26.322685 *24.931949 *24.866817 *
lnY
3
25.579645 *25.410592 *24.638653 *24.663459 *
Dlne 26.865249 26.382855 26.287725 26.441025
DlnY 24.614846 24.541309 24.874481 24.874361
DlnEN 24.440726 24.258443 25.261179 25.054259
DlnX 25.007402 24.821987 26.123043 26.31247
DlnP 25.400854 27.152204 22.097497 22.289268
Dlny
2
24.378117 24.350362 24.645355 24.725334
DlnY
3
24.060267 24.116628 24.392642 24.585298
Note: Rejection of null hypothesis at: *1 and **
10 percent of significance level with first difference
Table I.
Unit root tests result
Regressor Coefficient t-statistics
DlnY 7.98 0.83
DlnY
2
4.12 0.37
DlnY
3
0.92 0.097
DlnEN 0.048 0.057
DlnX 0.02 0.11
DlnP 22.311 23.643 **
DlnC 0.05 3.01 **
ECT(21) 20.5 23.2 *
Diagnostic test statistics
R
2
0.782606 LnY 20.866 lnX 20.168
F(7,27) 6.138 lnY
2
22.49 lnP 21.724
DW statistics 1.475 lnY
3
5.19
RSS 0.189 LnEN 0.0131
Note: Coefficient significant at: *5 and **
10 percent levels
Table III.
The result of ECM
F-statistics Optimum lag order Lower bound Upper bound
10.70905 *1,1,1,0,1 I(0) I(1)
Notes: Significant at: *1 percent level; CV [at 1% (3.516, 4.781), 5% (2.649, 3.805) and 10% (2.262,
3.367)]
Source: Pesaran et al. (2001)
Table II.
F-test results for
cointegration
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When we turn to Table IV for long-term relationship the positive sign on Y and negative
on Y
2
shows the inverted U-shaped relationship between growth and carbon emission.
Although population has smaller value but it does influence the environmental
degradationin Pakistan. As the model is linear presented in log-linear form so the value of
Y 6.75 denotes that the increase in 1 percent real GDP will raisethe 6.75 percent per capita
carbon emission. The other variables can alsobe calculated in same manner. The influence
of energy consumption is larger than trade openness because of the economy highly rely
on services and agriculture share rather than manufacturing sector. It tends to emit less
carbon consequently, contribute less to environmental degradation.
Now, as far as error correction is concerned, it is noted that deviation from
equilibrium is mainly corrected by the energy consumption, trade, and emission but
growth observed less exogenous. It shows all variable except population rely on growth.
The most fruitful result of this study is turned out in shape of population, which
positively influences the country’s environmental degradation.
In order to check the stability amongst co-efficient the technique of CUSUM and
cumulative square sum (CUSUMSQ) technique is used (Figures 1 and 2). in plot the
two straight lines representing two statistics bound by the 5 percent significant level
authenticate the stability of model as they lie within the boundaries of lines.
5. Conclusion
This study investigated the EKC hypothesis and concluded the relationship between
carbon emission and other four variables (energy consumption, economic growth, trade
openness and population) at the same time by using auto regressive distributed lag
(ARDL) methodology for country Pakistan from the period of 1971 to 2008 through time
series data analysis. The estimation was based on both short- and long-run results and
the stability of model was also checked at the end.
The results of the study do not support EKC in a short-run, whereas the long-run
inverted U-shaped hypothesis was confirmed between carbon emission and growth,
energy consumption, trade openness and population density. Thus, findings of the study
confirmed that EKC was a long-run phenomenon in the case of Pakistan and most
interestingly with all other explanatory variables population density also appeared to be
Dependant variable: lnE
Regressor Coefficient t-values
lnY 6.75 2.83 ***
lnY
2
20.49 22.48 **
lnY
3
25.69 27.08 *
lnEN 1.272 2.334 ***
lnX 0.109 2.431 ***
lnP 0.081 0.948 ***
lnC 8.426 2.538 ***
Diagnostic test statistics Teat-stats p-value
Serial corelation 0.1399 0.74
Functional form 0.553 0.29
Normality 0.71 0.401
Heteroskedasticity 0.63 0.37
Note: Significant of coefficient at: *1, **
5, and ***
10 percent levels
Table IV.
Long-run estimate results
and diagnostic test stats
CO
2
emission
in Pakistan
195

a contributor to environmental degradation in Pakistan. The trade openness helps
improve the environment in short-run path only. The error correction value also showed
the quick diversion to equilibrium at long-run path. These results can be used potential
help for policy makers in Pakistan to add more efforts to implement national
environmental program started back in 2005. The control on population growth is also a
factor to be reconsidered, which is stagnant since 2005 after continued decline since 1990.
Besides national level, there is immense need of formulating regional policies to curb the
carbon emission. The carbon tariff can also be considered in the worst case scenario. The
trade liberalization should be continued as it assists to import latest technology. Revisiting
the urban planning and forest policy need may potentially help offset the adverse effects
of urbanization and deforestation because of industrial led growth.
Figure 2.
Cumulative sum of
squares of recursive
residual (CUSUM square)
–0.4
0.0
0.4
0.8
1.2
1.6
80 85 90 95 00 05
CUSUM of Squares 5% Significance
Figure 1.
Cumulative sum of
recursive residual
(CUSUM)
–20
–10
0
10
20
80 85 90 95 00 05
CUSUM 5% Significance
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Further reading
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(The) World Bank (2012), World Development Indicators, World Bank, Washington, DC.
Corresponding author
Khalid Ahmed can be contacted at: mangrio85@yahoo.com
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