Nexus between non-renewable energy demand and economic growth in Bangladesh: Application of Maximum Entropy Bootstrap approach

Renewable and Sustainable Energy Reviews 72 (2017) 399–406

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Nexus between non-renewable energy demand and economic growth in Bangladesh: Application of Maximum Entropy Bootstrap approach☆

MARK

⁎

Mohammad Jahangir Alama,b, , Mumtaz Ahmedc,d, Ismat Ara Begume a

Arndt-Corden Department of Economics, Crawford School of Public Policy, Australian National University, Canberra, ACT 2601, Australia Department of Agribusiness and Marketing, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh c Department of Economics, Cornell University, Ithaca, NY 14853, USA d Department of Management Sciences, COMSATS Institute of Information Technology, Park Road Chak Shehzad, Islamabad 44000, Pakistan e Department of Agricultural Economics, Bangladesh Agricultural University, Bangladesh b

A R T I C L E I N F O

A BS T RAC T

Keywords: Causality Asymptotic theory Production function Meboot approach

The paper analyzes the causal relationship between energy demand and economic growth in Bangladesh using annual time series data from 1980 to 2011 from World Development Indicator (WDI), Database of the World Bank. Since the results of causal direction are sensitive to the models being employed, the paper uses Maximum Entropy Bootstrap (meboot) due to its superiority to overcome small sample bias and limitation of asymptotic distribution theory. To avoid the omitted variables bias, the paper uses multivariate framework in production function specification. The paper finds that a unidirectional causality runs from economic growth to energy demand which indicates that conservative policy might not harm the Bangladesh economy but since economic progress leads higher energy consumption, demand side management of energy and exploring renewable energy sources are must. The implications are discussed.

1. Introduction The Bangladesh economy has been performing well among the South Asian countries in terms of sustained economic growth and fulfilment of several targets of millennium development goals (MDGs) of the United Nations. The economy has already transformed from low income country to lower middle income country (World Bank [1]). This poses a new challenge for the policy makers to maintain the current transformation pace and transforming the country to middle income country by 2021 and aspiring to fulfilling the targets of sustainable development goals (SDGs) of the United Nations. The per capita energy consumption has also been doubled over the last few decades, despite the fact that the energy consumption is primarily dependent on fossil fuel. However, the country's economic development might be associated with the development of, among others, the energy sector. The stable energy supply is a pre-requisite to the economic prosperous in Bangladesh. The expansion of business activities, industrial production, agricultural transformations etc. requires regular energy supply. But coverage of the energy sector in Bangladesh is still limited and per capita energy consumption is among the lowest in the world despite the

fact that the energy consumption (mainly electricity) in the country is fast increasing, mainly due to expansion of economic activities along with the country's investment and trade policies (Asaduzzaman and Billah [2]). In the energy-growth nexus literature, it has long been and still continues to conduct research on the causal relationship between energy demand and economic growth. It is one of most debated topics in energy economics literature but no consensus regarding the direction of causality has been reached till date. The plausible reasons are as follows. First, employing different econometric models by different studies that resulting to variable results, sometimes conflicting (Soytas and Sari [3]). Second, the different time periods are examined, from 10 to 40 years. So, length of the time period studied might influence the results (Kalimeris et al. [4]). Finally, level of disaggregation of energy and inclusion of relevant variables in the estimated models. The authors use different forms of energy as electricity consumption, coal consumption, nuclear energy consumption, natural gas consumption, and total energy consumption. The variable and conflicting results pose a real challenge for policy makers to understand the causal relationship between energy demand

☆

This paper was submitted when the first author was an Endeavour Scholar at the Australian National University, Australia. Corresponding author at: Department of Agribusiness and Marketing, Faculty of Agricultural Economics and Rural Sociology, Bangladesh Agricultural University, Mymensingh2202, Bangladesh. E-mail addresses: [email protected], [email protected] (M.J. Alam), [email protected], [email protected] (M. Ahmed), [email protected] (I.A. Begum). ⁎

http://dx.doi.org/10.1016/j.rser.2017.01.007 Received 21 January 2016; Received in revised form 30 August 2016; Accepted 4 January 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.

Renewable and Sustainable Energy Reviews 72 (2017) 399–406

M.J. Alam et al.

and economic growth. To our understanding, the attempt of the current paper is to contribute to this debate especially for a country like Bangladesh. Since the country is now a lower middle income country and has strong vision to be a middle income country by 2021 and a high income country by 2041, economic growth will have to be 2% higher than current average, 6.1% (7th Five Year Plan [5]). So, the policy makers are in concern and will be interested to know if current energy supply will meet the energy demand in the years to come and if economic growth is highly linked with energy demand. Understanding this will help in designing effective plans to determine the optimum use of energy and if needed, to increase energy supply not only from the non-renewable sources, as currently its about 70% of total energy consumption, but also from different alternative renewable sources such as energy from biomass, solar or hydraulic power. However, the contributions of this paper are twofold. First, it uses a sophisticated econometric approach to overcome the limitations of existing approaches being widely used in literature. Employing such model will provide accurate inference since no consensus exists in the studies currently available particularly, in case of Bangladesh (Khatun and Ahamad [6], Alam et al. [7], Ahamad and Islam [8], Alam and Sarker [9], Mozumder and Marathe [10]). Second, following Ahmed et al. [11], Shahbaz et al. [12], Lean and Russell [13], among many others, the paper includes macro-economic variables such as total labour force and total capital stock in the production function specification to avoid the omitted variable bias and to make the model theoretically consistent, so analyze the causal relationship in multivariate framework. This will add additional feature to understand the nexus between energy demand and economic development in Bangladesh. Against the above backdrop, the objective of the paper is to analyze the causal relationship between energy demand and economic growth in Bangladesh using maximum entropy bootstrap (meboot) approach as existing studies are based on conventional approaches based on asymptotic theory that suffer from small sample bias and thus, may lead to wrong inferences regarding the causal direction. The remainder of the paper is organized as follows. Following the introduction, a brief overview of dynamics in energy demand, supply and economic growth in Bangladesh is presented in Section 2. Section 3 presents a brief review of literature, to highlight the difference and contribution of this paper to existing literature, which is followed econometric model and data description in Sections 4 and 5, respectively. The results and discussions are presented in Section 6. The last section concludes and provides policy direction.

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Coal

Hydroelectric

Natural gas

Oil

Fig. 2. Percentage share of electricity production from different energy sources. Source: BBS [14] and WDI [1].

electricity and solar is about 1%. Total commercial energy supply is 28.44 million ton oil equivalent in 2013-14. Natural gas and hydroelectricity are produced domestically. About two-third of natural gas is used for electricity generation in Bangladesh (Fig. 2). Access to household electricity consumption is still far below than other similar developing countries across South Asian region. Only about 60% of total population has access to electricity in Bangladesh in 2012 (WDI [1]) which means that approximately 59 million people have no access to electricity. Over time, the use of hydraulic power in electricity generation has decreased from 24.78% in 1980 to only 1.58% in 2012 which indicates that Bangladesh is dependent on natural gas. Since energy sector is solely dependent on natural gas reserves, so over exhaustion of natural gas will decrease reserves which will put country into severe energy insecurity in the future if new gas field is not found and other sources such as renewable energy is not ensured. The import (coal and crude oil are imported in Bangladesh) share to total energy demand is less than 10%. The import of refined oil and crude oil is 4.3 million metric tons and 1.2 million metric tons, respectively. The major uses of energy include transport sector, industrial production, business activities, household services, and agricultural production. The per capita energy consumption in Bangladesh is one of the lowest in the South Asian region. The per capita energy consumption in Bangladesh is 204.72 kg of oil equivalent (WDI [1]) which is much below the world average of 1680 kg oil equivalent and 481.62 and 613.72 kg of oil equivalent for Pakistan and India, respectively. In terms of electricity consumption, the per capita electricity consumption in Bangladesh is 217 kW h (kW h) in 2013 when the per capita consumption of Pakistan, India and Bhutan is 368.38 kW h, 529.1 kW h and 1619.48 kW h, respectively. Overall, energy sector represents a very tiny share in the country's overall GDP. The contribution of energy sector to GDP is only about 1.55% in 2013–14 (BBS [14]). The electricity demand by sectors is given in Table 1 below:

2. Brief overview of dynamics in energy demand, supply and economic growth The energy supply pattern in Bangladesh is presented in Fig. 1. The main source of energy is natural gas (72% of total), followed by petroleum oil (19%) and coal (6%). Supply of energy from hydro-

Table 1 Electricity demand by sector in Bangladesh during 2008–09 to 2012–13 (million kW h). Source: Bangladesh Power Development Board, Ministry of Power, Energy and Mineral Resources [15]. Note: The values in parentheses are percentage share. Sectors

2008–09

2009–10

2010–11

2011–12

2012–13

Domestic services Agriculture

10,053 (46.21) 1172 (5.39) 7626 (35.05) 2054 (9.44) 850 (3.91)

11,623 (47.26) 1229 (5.0)

14,678 (49.07) 1492 (4.99) 10,579 (35.37) 2751 (9.20) 413 (1.38)

16,351 (49.94) 1512 (4.62) 11,445 (34.96) 2996 (9.15) 436 (1.33)

21,755 (100)

24,596 (100)

12,757 (48.0) 1269 (4.77) 9566 (35.99) 2574 (9.68) 413 (1.55) 26,579 (100)

26,579 (100)

32,740 (100)

Industrial service Commercial service Others Total Fig. 1. Energy supply in Bangladesh during 1990 to 2011. Source: BBS [14].

400

9002 (36.6) 2336 (9.5) 406 (1.65)

Renewable and Sustainable Energy Reviews 72 (2017) 399–406

M.J. Alam et al.

GDP share at constant price (base 2005-06) in Bangladesh

54.61

54.54

54.22

53.95

53.62

27.38

28.08

29

29.55

30.42

18.01

17.38

16.78

16.5

15.96

2010-11

2011-12

2012-13

2013-14

2014-15(p)

Agriculture

Manufacture

between energy demand and economic growth. A detailed literature on energy—growth nexus is reviewed and presented in Table 3. Energy— growth nexus can be classified into four different, equally possible, hypotheses. First, neutrality hypothesis – implies that no causal relationship exists between energy demand and economic growth. This hypothesis is supported by Soytas and Sari [3], Soytas et al. [17], Halicioglu [18], Payne and Taylor [19], Acaravci and Ozturk [20], Payne [21], Alam et al. [22], Yalta and Caker [23], Wen-Chi [24] etc. Second, growth hypothesis – suggests a unidirectional causality that runs from energy demand to economic growth implying that consumption of energy promotes economic growth. This result is supported by Morimoto and Hope [25], Lee [26], Lee and Chang [27], Akinlo [28], Warr and Ayres [29], Payne [30], Heo et al. [31], Yin and Wang [32], Alam et al. [22], Ghosh and Kakali [33], Shahbaz et al. [34], Lin and Presley [35], Wen-Chi [24], Khatun and Ahamad [6] etc. In this case, the major concern is availability of reserve of non-renewable energy and over-consumption of energy might damage environmental quality – a public good. This environmental damage affects all types of people in a country and beyond although the fruits of economic development may not be well distributed to all. Third, conservation hypothesis – implies that a unidirectional causality exists and runs from economic development to energy demand. This means that the economic development is not dependent on energy and hence any conservation policy will have insignificant impact on economic development (Ozturk [36]). This result is supported by Soytas and Sari [3], Yoo and Kim [37], Mehrara [38], Mozumder and Marathe [10], Ang [39], Hu and Lin [40], Zhang and Chang [41], Chang [42], Kumar [43], Abbas and Nirmalya [44], Govindaraju and Tang [45], Heo et al. [31], Jamil and Eatzaz [46], Shahbaz and Mete [47], Ahmed et al. [11], Bozoklu and Veli [48] etc. Finally, the feedback hypothesis – suggests a bi-directional causality implying that both energy demand and economic development causes each other. This result is supported by Aqeel and Butt [49], Paul and Rabindra [50], Oh and Lee [51], Soytas and Sari [52], Climent and Pardo [53], Yuan et al. [54], Odhiambo [55], Mishra et al. [56], Apergis and Payne [57], Bhusal [58], Kouakou [59], Ahamad and Islam [8], Shahbaz and Hooi [60], Kshsai et al. [61], Shahbaz et al. [62], Tiwari et al. [63], etc. In testing these hypotheses different studies employ different econometric techniques. These are ADRL (Pesaran et al. [64]), causality in TY framework (Toda and Yamamoto [65]), causality in vector error correction framework (Johansen and Juselius [66]) standard Granger causality [67]), panel cointegration, general-tospecific etc. Almost in all studies, due to unavailability of monthly or quarterly data, annual data have been used and thus the empirical analysis, mostly based on small number of observations, suffers finite sample bias (Ahmed et al., [11]). In conventional approach, one needs to test the time series data properties such as testing for the presence of unit root and/or possible cointegration and if data is found to be nonstationary then typically some data transformation (such as differencing) is done to achieve stationarity. It is important to note that any data transformation leads to losing the information that the original data contains and thus may lead to wrong inferences that may misguide policy makers. There are few exceptions, i.e. few studies that use the maximum entropy bootstrap (meboot) approach to overcome the limitations of conventional approaches. Notably, Ahmed et al. [11] for Pakistan, Yalta [68] for Turkey, Yalta and Caker [23] for China, and showed that one can get better and robust findings using meboot approach as opposed to conventional approaches. Unlike the conventional approaches used in literature, meboot does not require the use of methods based on asymptotic theory and hence, leads to robust inferences even when sample size is small (Vinod [69], Vinod [70], Vinod and De-Lacalle [71]). Since meboot provides better inferences without relying on asymptotic methods, hence, there is no need to carry out any unit root and/or cointegration analysis. Several prior studies for Bangladesh (Khatun and Ahamad [6], Ahamad and Islam [8], Alam and Sarker [9], Mozumder and Marathe

Service

Fig. 3. GDP share at constant price (base 2005–06) Bangladesh during 2010–11 to 2014–15 (p). Source: BBS [14] and WDI [1]; Note: The P stands for projected value.

Table 2 GDP growth in Bangladesh during 2010–11 to 2014–15 (p). Sources: BBS [14], p stands for provisional. Sectors

2010–11

2011–12

2012–13

2013–14

2014–15 (p)

Agriculture Industry Service Overall

4.46 9.02 6.22 6.64

3.01 9.44 6.58 6.72

2.46 9.64 5.51 6.14

4.37 8.16 5.62 6.15

3.04 9.60 5.83 6.49

From Table 1, it can be seen that electricity consumption share for domestic services is the highest (49.94%) which is followed by industrial service (34.96%) and commercial service (9.15%) in 2012– 13. The share of domestic services is about half of total electricity consumption. This indicates that the domestic services is the top user of electricity and unlike the industrial, commercial and agriculture, the electricity consumption for domestic services is not directly growth enhancing. The major drivers of the economy such as industrial service, agriculture and commercial service together share only less than 50% of electricity consumption whereas these sectors provide more than half of country's GDP. The GDP share at constant prices in Bangladesh is presented in Fig. 3. More than 50% of GDP comes from service sector. This share is always more than half of total GDP which consumed only less than 10% of total electricity consumption. This someway indicates that in Bangladesh (an energy deficit country), the economy is still performing much better among the South Asian countries, in the sense that the country is maintaining a stable growth of 6% on average. According to a report published by IMF [16], the economic growth of Bangladesh will be among the top five countries across the word in next couple of years but energy insecurity might put a serious pressure on economic growth in the country. For example, if economic growth is directly associated with energy consumption, unstable and access demand will harm the economy to expand. Bangladesh has been maintained a stable economic growth with about more than 6% average over the last decade and has a strong vision of becoming a middle income country by 2021 which would require the country to grow at least 8% per year, and the growth will be driven by industrial and service sectors. This stable economic growth of the last decade resulted Bangladesh to become a lower middle income country in 2015. The GDP growth in industry has been more than 8%. In order to achieving the goal of – to be a middle income country by 2021 and a high income country by 2041 will be difficult unless the stable and continuous supply of energy at reasonable price is ensured in every industries and every regions across the country (Table 2). 3. Literature review There exists a plenty of literature that addresses causal nexus 401

Electricity Electricity Per capita Per capita

Per capita energy consumption, per capita real GDP, FDI inflow Agricultural electricity consumption, agricultural GDP

Energy consumption, CO2 emissions, economic growth Electricity consumption, economic growth Import demand of crude oil, economic growth CO2 emissions, economic growth, coal consumption

Nuclear energy consumption, economic growth Energy consumption, economic growth Energy consumption, economic growth

Urbanization, energy consumption, economic activity

Ahamad and Islam [8] Alam and Sarker [9] Mozumder and Marathe [10] Alam et al. [7]

Khatun and Ahamad [6] Abbas and Nirmalya [44]

Alam et al. [22] Ghosh [72] Ghosh [73] Govindaraju and Tang [45]

Heo et al. [31] Mallick [74] Paul and Rabindra [50]

Ghosh and Kakali [33]

402

Oil Consumption, economic growth Electricity consumption, electricity prices, GPD energy consumption, economic growth Electricity consumption, economic growth Electricity consumption, economic growth Natural gas consumption, economic growth Energy consumption, economic growth Energy consumption-economic growth Electricity supply, economic growth Energy consumption, GDP Energy consumption, economic growth Energy consumption, economic growth Economic growth, natural gas consumption Energy consumption, GDP Energy consumption, economic growth Energy consumption, output

Bhusal [58] Jamil and Eatzaz [46] Aqeel and Butt [49] Shahbaz and Hooi [59] Shahbaz and Mete [47] Shahbaz et al. [34] Shahbaz et al. [62] Ahmed et al. [11] Morimoto and Hope [25] Aslan et al. [76] Bozoklu and Veli [48] Lin and Presley [35] Shahbaz et al. [12] Yalta [68] Yalta and Caker [23] Wen-Chi [24]

1975–2009 1960–2008 1955–1995 1972–2009 1971–2008 1972–2007 1972–2011 1971–2011 1960–1998 1973q1–2012q1 1970–2011 1971–2010 1972–2011 1950–2006 1971–2007 1970–2011

1966–2009 1969–2006

1971–2009

1969–2006 1970–2004 1950–1996

1971–2006 1950–1996 1971–2006 1965–2009

1972–2010 1972–2008

1971–2008 1971–2006 1971–1999 1972–2006

Time period and and and and

Juselius Juselius Juselius Juselius

[66] [66] [66] [66] and ARDL

Johansen and Juselius [66] VAR & Variance Decomposition Engel and Granger [67] Johansen and Jesulius [66] Threshold Cointegration ARDL Toda-Yamamoto Johansen and Jesulius, [66] ARDL ARDL Toda-Yamamoto Variance Decomposition Johansen and Juselius [66] Johansen and Juselius [66] Hsiao Granger Causality ARDL in Cobb-Douglas Prod. function ARDL Toda-Yamamoto ARDL model ARDL Gregory and Hansen structural break cointegration Meboot Yang model Wavelet coherence Frequency domain Nonparametric bootstrap method ARDL Meboot Meboot Frequency domain

Toda and Yamamoto Johansen and Juselius [66] ARDL model Bayer and Hanck Approach

Johansen and Juselius [66] Johansen and Juselius [66]

Johansen Johansen Johansen Johansen

Methods

Nepal Pakistan Pakistan Pakistan Pakistan Pakistan Pakistan Pakistan Sri Lanka U.S. OECD countries South Africa Pakistan Turkey China Algeria Egypt South Africa

India India

India

India India India

India India India India and China

Bangladesh India and Pakistan

Bangladesh Bangladesh Bangladesh Bangladesh

Country

Oc↔Ec Ec→El Ec↔En El↔Ec Ec→El GasC→Ec En↔Ec Ec→En El→Ec En→Eco GDP→En En→Eco GasC→Eco En→≠Ec En→≠Ec En→Ec (Algeria) En↔Ec (Egypt) En→≠Ec (South Africa)

CC↔Ec NEn→Ec

En→Ec

El↔Eco El→Eco (Short run) Eco→El En→Eco El↔Eco En→Eco agriGDP↔agriGDP (India) agriGDP→agriEl (Pakistan) En→≠Y Ec→El Ec→CoIm Ec→CC (India) Ec↔CC (China) NEn→Ec En→Ec Ec↔En

Conclusions

Notes: Eco means Economic growth, En means Energy consumption, q means quarter, m means month, El means Electricity consumption, GasC means Natural gas consumption, ↔ means bi-directional causality, → means uni-directional causality, → ≠ means no causality, GDP means gross domestic product, agriGDP means agricultural GDP, agriEl means agricultural electricity consumption, Oc means oil consumption, CoIm means crude oil import, NEn means nuclear energy consumption, meboot means maximum entropy bootstrap.

Coal consumption, economic growth Economic growth, nuclear energy consumption, labour, capital

Tiwari et al. [63] Wolde-Rufael [75]

consumption and economic growth generation, real GDP GDP, per capita electricity consumption energy consumption, CO2 emissions, per capita real GDP

Variables analyzed

Authors and Years

Table 3 Summary of causality test results by existing studies.

M.J. Alam et al.

Renewable and Sustainable Energy Reviews 72 (2017) 399–406

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M.J. Alam et al.

Keeping in view all this, it is wise to re-visit this conflicting debate using Maximum Entropy Bootstrap (meboot) approach – which does not require pretesting the data series for their order of integration and/ or cointegration as well testing for one or more (possible) structural break(s) in the series under examination. 4. Econometric model – Maximum Entropy Bootstrap To avoid omitted variable bias, we consider relevant macroeconomic variables such as total labour force and total capital stock in our model, hence we estimate the model in multivariate framework and consider a neo-classical production function. We consider energy, labour and capital are three inputs in production function. Total capital stock is gross fixed capital formation in constant prices. Total labour force is total active labour force in Bangladesh, aged 18–65 years. We follow a similar approach used by Ahmed et al. [11], Yalta and Caker [23] and Yuan et al. [54]. We have used the level data in maximum entropy bootstrap technique because this technique can be used under all forms of non-stationarity including those are difficult to determine such as structural break, non-linearity and different level of data integration or fractional integration. The estimated multivariate vector autoregressive (VAR) model is presented below.

Fig. 4. Energy demand and economic growth for Bangladesh, 1980–2011.

q

q

lnYt =c1+ ∑ α1jlnYt − j + j =1

Fig. 5. Total capital stock and total labour force for Bangladesh, 1980–2011.

j =0

q

lnECt =c2 +

q

j =0

q

j =0

q

(1)

q

∑ α2jlnYt −j+ ∑ β2jlnECt −j+ ∑ λ2jlnKt −j+ ∑ π2jlnL t −j+ε2t j =0

[10], and Alam et al. [7]) have examined the casual link between energy demand and economic growth using data on different variables, using different time span and using different econometric models. Due to all this, the empirical outcomes of these studies have been varied and sometimes conflicting. The existing studies use almost the similar time period except Mozumder and Marathe [10] and most of them based their empirical analysis on Johansen and Juselius [66] cointegration approach. Alam et al. [7] find that a unidirectional causality that runs from energy consumption to economic growth both in short run and long-run while a bi-directional causality between electricity consumption and economic growth but no causal relationship in short run. This means that economic development in Bangladesh is highly associated with energy demand. Ahamad and Islam [8] find that electricity consumption and economic growth cause each other, hence, feedback hypothesis exists, whereas Mozumder and Marathe [10] find a support for conservation hypothesis and finally Khatun and Ahamad [6] and Alam and Sarker [9] confirm the growth hypothesis. These varying findings pose serious challenge for the policy makers in Bangladesh. In addition, Johansen and Juselius [66] cointegration approach is highly sensitive to the order of integration of data series, i.e., data series under consideration must have same order of integration, either I(1) or I(2).

q

∑ β1jlnECt −j+ ∑ λ1jlnKt −j+ ∑ π1jlnL t −j+ε1t

j =1

j =0

j =0

(2) where, ci's (i=1, 2) are the constant terms and εi ’s (i=1, 2) are the white noise error terms, ‘q’ is the maximum lag length used, lnY is the natural logarithm of real gross domestic product (GDP) measured in US dollars at 2005 prices, used as a proxy for economic growth and lnEC is the natural logarithm of energy use measured in kilo ton of oil equivalent used as a proxy for energy demand. LnL is the natural logarithm of total labour force and lnK is the natural logarithm of total capital stock. For causality testing, following Ahmed et al. [11], the present paper uses the meboot algorithm to create k=999 independent reincarnations of all variables considered. For obtaining the confidence intervals, the highest density regions (HDRs) approach introduced by Hyndman [77] is employed. This offers a reliable method when sampling distribution is bimodal or multimodal. If ∫ (θˆ ) is the density, then the 100(1−α)% HDR is a subset of the sample space of random variable such that ˆ (θˆ )≥f }, where f is the largest constant such that HDR ( f∝)={θ:f ∞ ∝ Pr(θˆ∈HRD ( fα ))≥1 − α holds true, which means that every point inside the HDR has probability density at least as large as every point outside the HRD (Hyndman [78]). The advantages of meboot approach are as follows. First, it can be used seamlessly under all forms of non-

Table 4 Multivariate analysis using 90% and 95% confidence intervals. Variable

Constant LnY(−1) LnEC(−1) LnK(−1) LnL(−1) LnY(−2) LnEC(−2) LnK(−2) LnL(−2)

VAR (1) meboot 95% confidence intervals

VAR (2) meboot 90% confidence intervals

LnY

LnY

LnEC

LnEC

Lower

Upper

Lower

Upper

Lower

Upper

Lower

Upper

−4.98606 0.431442 −0.27817 −0.14747 −0.47972 – – – –

8.107862 1.112749 0.480616 0.276453 0.681946 – – – –

−11.8261 0.180355 −0.25136 −0.15839 −0.4315 – – – –

2.549477 1.008296 0.525238 0.293658 0.835423 – – – –

−5.46642 0.321368 −0.29263 −0.22865 −0.64313 −0.39542 −0.27483 −0.21216 −0.56969

9.189606 1.121046 0.422403 0.273438 0.674893 0.388609 0.410481 0.301174 0.710131

−11.704 0.061924 −0.35108 −0.25577 −0.70053 −0.24657 −0.35095 −0.20884 −0.60166

2.681395 0.899539 0.483595 0.284328 0.833302 0.520721 0.493854 0.304202 0.856773

Notes: All variables are taken in their natural logarithms, (ii) Confidence intervals are constructed via High Density Regions.

403

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Fig. 6. Plots of High Density Regions (HDR) for the estimates of natural logarithm of real GDP (lnY). Note: The blue, red and green bars respectively show the 90%, 95% and 99% confidence intervals, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7. Plots of High Density Regions (HDR) for the estimates of natural logarithm of energy demand (lnEC). Note: The blue, red and green bars respectively show the 90%, 95% and 99% confidence intervals, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

meboot approach, the readers are referred to MacKinnon [79], MacKinnon [80], Vinod [70], Yalta and Caker [23], and Ahmed et al. [11].

stationarity or fractional stationarity. Second, it is possible to analyze in level data which helps data quality issues and losing number of observation. Third, meboot provides simulation based hypothesis testing which is less susceptible to over rejection even under the limited number of observation case. For detailed discussion on the 404

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5. Data description

7. Conclusions and policy recommendations

The main variables considered in this paper are energy demand and economic growth in Bangladesh. Total energy demand comprises, electricity, natural gas, coal, and petroleum. The real GDP data are used as a proxy of economic growth. For estimating multivariate model, we consider, in addition to energy demand and economic growth, total capital stock and total labour force. The annual time series data are obtained from the World Development Indicators (WDI), the database of the World Bank. The selected time period is from 1980 to 2011, the latest data available in WDI on relevant variables. The plots of data series are provided in Figs. 4 and 5. To visualize the energy demand and economic growth, Fig. 4 shows that the selected variables move together, which indicate that there might be a strong causal relationship between energy demand and economic growth over the time but it does not indicate the direction of causality. We also find the similar pattern for the other two macroeconomic variables – total capital stock and total labour force. These two variables are important in analyzing the causal relationship in production function specification (Stern [81]). In estimating the model, we have transformed data into natural logarithm to minimise the heteroscedasticity problem. Also, one can consider the estimated coefficient from the model as elasticity.

We conclude that causal relationship between energy demand and economic growth is highly dependent on the econometric techniques employed. If an analysis is solely based on econometric approach which does not take into account the limitations of the conventional approaches which are based on asymptotic theory, may lead to wrong inferences. Using meboot, we find that conservation hypothesis holds true for Bangladesh. Since higher consumption of non-renewable energy might be associated with higher carbon emissions, hence environmental quality degradation, optimal use of energy is not economically harmful. We find that economic growth leads to higher energy demand. Since Bangladesh economy is currently at the take off stage and expected to fly in the near future, it is highly important to explore the potential of renewable energy sources such as solar power, biomass, hydraulic power etc. This will help the Bangladesh government to meet-up the energy demand and also to achieve the sustainable development goals of the United Nations and limits the environmental quality damage – quality damage of a public good, which has strong implication to global climate change, public health, poverty and sustainable food security in Bangladesh and beyond. It is important to note that since our analysis is solely based on the consumption of energy at aggregate level, more disaggregated analysis based on different types of energy – renewable and non-renewable, also in non-renewable – coal, electricity, petroleum will provide more disaggregate results that must have of great interest to the policy makers in Bangladesh and could be our future research endeavour.

6. Results and discussions The empirical results are presented in Table 4. In order to examine the robustness of results at different lags, the model is estimated at both vector autoregressive (VAR) model of order one as well as of order two, i.e., VAR(1) and VAR(2). This multivariate VAR framework is theoretically consistent because it is constructed with the context of production function specification (Stern [81], Stern [82]). The plots for high density regions (HDR) for the estimates of real GDP and energy demand are presented in Figs. 6 and 7. The bars in each plot represents probability coverage levels such as 90%, 95%, and 99%, respectively. These figures show how the HDRs, narrower than the naive percentile intervals, and cover for α=0.1 in all models. The values in bold row in Table 4 indicate the estimate where the value of the corresponding coefficient under null hypothesis, i.e. zero lies outside the (1−α)% confidence intervals constructed via HDR, both in VAR(1) and VAR(2) specifications. The results indicate that a unidirectional causality between energy demand and economic growth exists and runs from economic growth to energy demand. This confirms the conservative hypothesis- which implies that economic growth is not dependent on energy demand and hence, any policy towards energy conservation will not undermine the economic progress in Bangladesh. In Bangladesh, the energy consumption has been increased from 102.56 kg of oil equivalent in 1980 to 213.66 kg of oil equivalent in 2012, which is more than double over the last three decades. The GDP per capita growth has been increased from 1.94% in 1980 to 5.25% in 2012 (WDI [1]). Given that energy supply is mainly based on non-renewable source (about 70% of total), a wellplanned conservation policy and demand side management will play an active role in energy sector management in Bangladesh. This will help to curve the pollution and emissions that arise by consuming nonrenewable energy consumption and hence will contribute to combating the global warming problem. Also, better management of the sector will meet-up the increasing energy demand. Similar results are reported by Mozumder and Marathe [10] while investigating the causal relationship between per capita electricity consumption and per capita GDP for Bangladesh. Ahmed et al. [11] also find the similar result in case of Pakistan. However, using the same approach (meboot), Yalta [68], Yalta and Caker [23] find no causal relationship between energy demand and real GDP for Turkey and China, respectively.

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