Electricity consumption and GDP nexus in Bangladesh: a time series investigation
Abstract
Purpose
The purpose of this paper is to assess the empirical cointegration, longrun and shortrun dynamics as well as causal relationship between electricity consumption and real GDP in Bangladesh for the period of 1971‒2014.
Design/methodology/approach
Autoregressive Distributed lag (ARDL) “Bound Test” approach is employed for the investigation in this study.
Findings
Both shortrun and longrun coefficients are providing strong evidence of having positive significant association between electricity consumption and GDP. Our longrun results remain robust to different measurements and estimators as well. The study reveals the unidirectional causal flow running from per capita electricity consumption to per capita real GDP in the short run. The study result also yields strong evidence of bidirectional causal relationship between per capita electricity consumption and per capita real GDP in the long run with feedback. It is suggested that both electricity generation and conservation policy will be effective for Bangladesh economy.
Originality/value
In prior studies, lack of causality between electricity consumption and GDP is due to the omitted variables. Combined effects of public spending and trade openness on GDP and electricity consumption are also considerable.
Keywords
Citation
Dey, S.R. and Tareque, M. (2019), "Electricity consumption and GDP nexus in Bangladesh: a time series investigation", Journal of Asian Business and Economic Studies, Vol. 27 No. 1, pp. 3548. https://doi.org/10.1108/JABES0420190029
Publisher
:Emerald Publishing Limited
Copyright © 2019, Sima Rani Dey and Mohammed Tareque
License
Published in Journal of Asian Business and Economic Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and noncommercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Bangladesh has ensured its stable economic growth in the last decade, and it also has an aspiration to become a highincome country by 2,041. So, the development of energy and power infrastructure is inevitable to realize the longterm economic development. In the context of Bangladesh, the power sector is one of the largest sectors that consume primary energy. The relationship of GDP and electricity consumption has been immensely debated in the studied literature, yet their causal relationship directions are still unsolved. In the last decades, numerous researchers have attempted to address this issue and tried to investigate the association between electricity consumption and economic growth using both singlecountry and crosscountry data. Plenty of literature exists on the causal relationship between electricity consumption and economic growth across the developing economies. Different countries, methodologies, time periods, even different proxy variables for energy consumption and income have been employed in different studies.
Causality bearing between power utilization and economic development has huge ramifications on political and economical strategy perspectives. The heading of causality can be abridged into four classes: growth hypothesis; conservation hypothesis; feedback hypothesis; and neutrality hypothesis. Singledirection causality from electricity consumption to financial development is a typical experimental finding for some Asian economies (Ho and Siu, 2007).
Studies those attempt to evaluate the connection between power utilization and GDP in setting of Bangladesh are sparse. Mozumder and Marathe (2007) led shortrun Granger causality test for the time period of 1971‒1999, whereas the examination by Ahamad and Islam (2011) assessed their shortrun, longrun and joint causal relationship for the time period of 1971‒2008 and Alam et al. (2012) examined the dynamic causality for the time period of 1972‒2006. Most likely, above investigations are huge on their grounds, yet hardly any study, to date, has been led to survey the longrun relationship between power utilization and GDP with any control variable (considering the combined effects of public spending and trade openness on GDP and electricity consumption) along with their shortrun, long run and joint causal relationship. The sensitivity of our longrun estimates is verified by employing three alternative estimators.
Consequently, the paper examines the longrun association between electricity consumption and GDP in Bangladesh using ARDL bounds test approach. Again, the study investigates the presence and direction of causal relationship to take effective policy decision regarding electricity consumption. A vector errorcorrection model (VECM) based Granger causality test was employed to analyze the relationship; the F and ttests are carried out to gauge the joint significance levels of causality between the electricity consumption and GDP.
The rest of this paper is structured as follows: beginning with the introduction, Section 2 examines about the recent electricity scenario of Bangladesh and Section 3 depicts an outline of the literature review. Section 4 focuses on data and estimation procedures of the investigation. Section 5 examines the experimental outcomes; Section 6 reaches the inference of the study.
2. Recent electricity scenario of Bangladesh
Economic growth of a demanddriven economy like Bangladesh has always been linked with energy (mainly electricity) consumption. Unfortunately, the infrastructure of power sector is not sufficient to meet growing demands and is managed inefficiently. Moreover, the power demand of Bangladesh is increasing rapidly along with the increase of the per capita GDP over the last decades (Table II). Installed power generation capacity was 16046 MW (including captive power) as on December 2017 and 77 percent population had access to the electricity in Bangladesh (Table I).
To sustain the further economic growth, heavy dependence on laborintensive industrial sector like readymade garment (RMG) is not sufficient and it is expected that it will shift to energyintensive industries. Subsequently, energy utilization in the industrial sector is required to increment quickly. To manage the future fast development of vitality utilization in Bangladesh, government has detailed couple of compelling strategies. Without a doubt, for the seventh Five Year Plan (Power System Master Plan (PSMP) 2016), the objective by 2020 is set as “power inclusion to be expanded to 96 percent with continuous supply to ventures” (Table II).
The installed capacity and maximum generation of electricity are increasing over the last few years, but the state is struggling to meet the demanded electricity. Currently, many of power plants in Bangladesh cannot generate electricity as specified in terms of power for each unit. So, hydro power generation studies have become an urgent issue through the government’s renewable energy promotion policy. Hopefully, the new Power System Master Plan study will cover previous challenges and will provide feasible proposal and action plans for implementation as well (Figure 1).
So, the development of energy and power infrastructure, therefore, pursues not only the quantity but also the quality to realize the longterm economic development. Therefore, power proficiency may end up being the most essential alternative to deal with the tremendous neglected power request in the future relying upon the causality directions. Hence, the direction of relationship should be examined cautiously to determine right policy for accelerating economic growth and development.
3. Literature review
The association of energy consumption with economic growth is a special matter of interest and a series of literature on energy consumption and economic growth is available. The relationship between energy consumption and economic growth was first studied by Kraft and Kraft (1978), then the research works had been extended from energy consumption to electricity consumption. A short synopsis of those particular written works on electricity consumption and economic development point of view has been introduced in Table III.
The causal linkages’ nature and directions of the abovementioned literature vary across countries due to econometric techniques and variables used on different time series in their studies. Causality tests give us the insights about whether the information of past electricity movements improves conjectures of developments in economic growth and the other way around.
We can categorize our selected research works into four gatherings. First, an extensive number of studies found unidirectional causality running from electricity (or energy) consumption to GDP. These include Altinay and Karagol (2005) and Acaravci (2010) for Turkey, Aqeel and Butt (2001) for Pakistan, Shiu and Lam (2004) and Yuan et al. (2007) for China, Narayan and Singh (2007) for Fiji Islands, Chandran et al. (2010) for Malaysia, Odhiambo (2009) for Tanzania, Ho and Siu (2007) for Hong Kong, Iyke (2015) for Nigeria and Morimoto and Hope (2004) for Sri Lanka.
The investigations that found unidirectional causality running from GDP to electricity (or energy) consumption comprise the second group. These include Ghosh (2002) for India, Jamil and Ahmad (2010) for Pakistan, Ciarreta and Zarraga (2010) for Spain, Mozumder and Marathe (2007) for Bangladesh and Narayan and Smyth (2005) for Australia.
The studies that found bidirectional causality comprise the third group. These include Tang (2008) for Malaysia, Oh and Lee (2004) and Yoo (2005) for Korea, Polemis and Dagoumas (2013) for Greece, Tang et al. (2013) for Portugal, Hamdi et al. (2014) for Bahrain, Jumbe (2004) for Malawi, Ahamad and Islam (2011) for Bangladesh and Belloumi (2009) for Tunisia. The fourth group comprises studies that found no causal linkages between electricity consumption and GDP, such as Stern (1993) for USA.
The summary of above writing audit reflects on the causal relationship between electricity (or energy) consumption and GDP, but the existing research works fail to provide clear evidence on the direction of causality between them. The inconsistency of the causality findings may attribute to the different data span and source, alternative econometric techniques, different countries’ characteristics and omitted relevant variables (Chen et al., 2007). The causal relationship between energy consumption and economic growth has strong implications from theoretical, practical and policy points of view (Fuinhas and Marques, 2012).
4. Data and estimation techniques
Following Mazumder and Marthe (2007) and Ahamad and Islam (2011), we used both electricity consumption and GDP data for Bangladesh in per capita form. Clearly, besides per capita electricity consumption, different factors could have extraordinary effect on economic growth. Thus, exclusion of those factors could lead to inclination of the estimation results and causality direction of the factors. In this point of view, we included government spending (GE) but in per capita form and trade openness as controlled variable to avoid omitted variable bias and simultaneity bias in our regression following Akinlo (2008) and Tang et al. (2013). Table IV provides the descriptive statistics of the studied variables.
Annual data on PCEC and PCGDP are covering the time period of 1971‒2014 and collected from the World Bank[1]. All data are in real form. The historical data of per capita GDP and per capita electricity consumption for Bangladesh are portrayed in Figure 2.
The functional form of the model to satisfy the prime objective of the study is as follows:
The econometric form of the above model relating to electricity consumption and GDP, once stationarity or cointegration is verified:
A multivariate framework is used in this paper to examine the linkage between electricity consumption and GDP. To analyze the longrun relationship between the studied variables, the study employed autoregressive distributed lag (ARDL) “Bound Test” approach introduced by Pesaran and Shin (1999) and Pesaran et al. (2001)[2]. To correct residual serial correlation and problem of endogenous variables, appropriate modification of the orders of ARDL model is sufficient (Pesaran and Shin, 1999).
Although pretesting of unit root is not necessary to proceed with ARDL bounds testing approach as it can test the cointegration existence between a set of variables of I(0) or I(1) or blender of both, there is a risk of invalid estimation if any variable comes out as integrated of order two or I(2). It is, therefore, essential to test the stationarity properties of each variable before proceeding to the econometric analyses. The augmented Dickey‒Fuller (ADF) and the Phillip‒Perron unit root testing methods will be used for test unit root of the variables under study.
In ARDL conintegration technique, the existence of cointegration or possession of longrun relationship among the variables is primarily determined. At that point, the short and longrun parameters extraction is done in the second step. The bound test approach is mainly based on an estimate of unrestricted errorcorrection model (UECM) by using ordinary least squares (OLS) estimation procedure. ARDL is easy to clarify, gives unprejudiced estimation of the longrun relationship and dynamics as well as the issues of serial correlation and endogeneity are taken care of.
The presence of causality and its direction will be assured by the existence of cointegration of the variables. The bound testing approach to cointegration involves investigating the presence of a longrun equilibrium relationship using the errorcorrection model (UECM) frameworks:
The causal relationship among the studied series exists if the presence of cointegration is confirmed, but it does not demonstrate the direction of the causal relationship. The VECM model derived from the longrun cointegrating relationship can be utilized to catch the dynamic Granger causality (Granger, 1988). Engle and Granger (1987) demonstrated that if the series are cointegrated, the VECM model for the series can be written as follows:
In Equations (6)–(9), changes in the dependent variable are caused not only by their lags, but also by the previous period’s disequilibrium in level, ECT_{t−1}. Given such a specification, the presence of short and longrun causality can be tested. The errorcorrection model results indicate the speed of adjustment back to the longrun equilibrium after shortrun shocks.
The ECM coordinates the shortrun coefficient with the longrun coefficient without losing longrun data. Under ECM technique, the longrun causality is delineated by the negative and significant value of the ECT coefficient δ and the shortrun causality appears by the noteworthy estimation of coefficients of other informative factors (Rahman and Mamun, 2016; Shahbaz et al., 2013). Equation (6) can be considered. If the estimated coefficients on lagged values of per capita electricity consumption (α_{2}s) are factually noteworthy, then the implication is that electricity consumption Granger causes per capita real GDP in the short run. However, longrun causality can be found by testing the criticality of the assessed coefficient of ECT_{t−1}.
5. Empirical results
In this section, we present the empirical results from various approaches. Table IV demonstrates that all variables are nonstationary in their dimensions, yet they turned out to be stationary after first differencing and the results are outlined underneath.
From the above estimates, it can be inferred that both ADF and PP (Table V) test results reveal that the variables are nonstationary at 5 percent level of significance, but they became stationary at the first difference level. Thus, all the variables are integrated of order one, that is I(1), and both possibilities with intercept as well as with intercept and trend are considered.
Since our variables are integrated, so it needs to be found whether the variables are cointegrated or not. To explore the longrun relationship between electricity consumption and GDP, ARDL model to cointegration and error correction is employed.
The ARDL bound tests affirms the existence of longrun association between the factors in Equations (2)–(5) and the outcomes are presented in Table VI. The computed Fstatistic of above equations exceeded the upper bounds at 1 percent level of significance except the second equation when per capita electricity consumption is the dependent variable. As per the rule, the higher Fstatistic value supports the nonacceptance of null hypothesis that confirms the longrun relationship between the factors, which implies that the variables will move together. So the cointegration results lead us to contend that electricity consumption and GDP have a longrun affiliation.
The AIC lag length criterion statistic indicates that ARDL (3,1,3,1) model is the best lag orders combination and the estimation results are reported in Table VII. The result showed that a statistically significant association exists between electricity consumption and economic growth. Intercept term also becomes significant at 5 percent level of significance (Table VIII and Figures 3 and 4).
Both shortrun and longrun coefficients are providing strong evidence of having positive significant association between electricity consumption and GDP at 5 percent level of significance. The value of ECT coefficient in GDP equation is –0.12 which indicates that the alteration coefficient (speed of convergence) to reestablish the equilibrium in the long run by around nine years.
To check the robustness of our longrun results, we employed three alternative estimators: the Phillips and Hansen’s (1990) fully modified OLS (FMOLS) procedure, the Stock and Watson’s (1993) dynamic OLS (DOLS) and the Park’s (1992) canonical cointegration regression (CCR). Although the electricity consumption coefficients in three alternatives are smaller than the ARDL coefficient estimate, but our findings of positive electricity consumption‒economic growth nexus remain robust to all these three estimators (Table IX).
Granger causality test is used to identify the causal relationship between the variables. Existence of longrun relationship leads to expect either unidirectional or bidirectional causal relationship between the series. The dynamic Granger causality test results (Table X) indicate that there is a unidirectional shortrun causal relationship running from per capita electricity consumption to per capita GDP at 1 percent level of significance. The reverse causality, that is PCGDP Granger causes PCEC, is not significant even at 10 percent level. This result is similar to those obtained by Oh and Lee (2004) and Ahamad and Islam (2011), but it is converse of Mazumder and Marthe (2007).
Turning to the longrun causality, the ECT coefficients were rejected in all equations except trade openness, though per capita spending coefficient was not significant. The result implies that electricity consumption, GDP and trade openness have bidirectional causality in the long run. In addition, PCGDP and PCEC variables are not weakly exogenous, proposing bidirectional longrun causality (feedback relationship) between PCGDP and PCEC. Our outcome is additionally in accordance with findings by Oh and Lee (2004), Ahamad and Islam (2011) and Alam et al. (2012); they likewise uncovered feedback hypothesis in the long run between per capita electricity consumption and per capita GDP (Figure 5).
Moreover, a joint Ftest confirms the bidirectional longrun causality between electricity consumption and GDP because we reject the null hypothesis at the 1 percent level (the null hypothesis that the coefficients on the ECTs and the interaction terms are jointly 0 in both the PCGDP and the PCEC equation).
In this way, overall study findings imply that feedback hypothesis (which states that bidirectional causality runs from electricity consumption to GDP) exists both in the shortrun and longrun, indicating that when economy grows, electricity demand increases and the reverse is true as well in Bangladesh.
A series of diagnostic tests were conducted on the ARDL model and the model is found to be robust against residual correlation, and the ARCH test confirms the homoskedasticity of the residuals. At the same time, Jarque‒Bera normality test ensured that estimated residuals are normal, and the CUSUM and CUSUM of Sq. test also confirmed the correct functional form of the model.
6. Conclusion and policy implications
This study examines the causal linkage between electricity consumption and gross domestic product (GDP) in Bangladesh. In this regard, along with two control variables (per capita government spending and trade openness), the study used essential econometric techniques to comprehend the source and direction of conceivable causal connection between them. Cointegration test result establishes the presence of longrun equilibrium relation between PCEC and PCGDP series. Moreover, the robustness of the longrun result is verified by other alternative estimators. For the validation of the causal relationship, VECMbased Granger causality test is led and the results reveal unidirectional shortrun causal relationship running between per capita electricity consumption and per capita GDP, whereas bidirectional longrun and joint causal relationship also exists between per capita electricity consumption and per capita GDP, which demonstrates that electricity consumption can animate economic growth and the reverse is also true. Our study findings might have a considerable impact on the making of essential shortrun and longrun policy insights.
The study findings clearly exhibit that electricity consumption can be considered as a important factor for achieving higher growth of GDP in the short run. So, policy regarding electricity generation, distribution, management and conservation should be given priority to ensure higher economic growth in the short run for Bangladesh economy. On the contrary, longrun bidirectional causal relationship (greater access to electricity and high per capita GDP influence each other) indicates that adequate investment is required for strengthening the electricity supply and also for those factors that will influence the GDP growth.
Figures
Electric power utilization and GDP per capita, 1971–2014
Time periods  Electric power utilization (kWh per capita)  GDP per capita (constant 2010 US$) 

1971–1980  16.67961  342.8396 
1981–1990  35.45743  380.0094 
1991–2000  76.0367  453.2003 
2001–2010  173.8429  625.8588 
2011–2014  283.9119  859.6671 
Note: Average growth rate is a 10year average except the last row, which is a fouryear average
Electric power consumption scenario, 1995–2017
Year  Installed capacity (MW)  Maximum demand (MW)  Maximum peak generation (MW) 

1995–1999  3,084  2,439  2,151 
2000–2004  4,262  3,682  3,187 
2005–2009  5,293  5,207  3,903 
2010–2014  8,274  7,671  5,870 
2015–2017  12,485  11,444  8,777 
Note: Average growth rate is a fiveyear average except the last row, which is a threeyear average
Summary of selected observational studies
No.  Authors  Countries  Study period  Used variables  Causality directions 

1  Altinay and Karagol (2005)  Turkey  1950–2000  Logarithm of electricity consumption and real GDP  EC→Y 
2  Aqeel and Butt (2001)  Pakistan  1955–1996  Logarithm of per capita real GDP, energy consumption and employment  EC→Y 
3  Shiu and Lam (2004)  China  1971–2000  Electricity consumption and real GDP  EC→Y 
4  Narayan and Singh (2007)  Fiji Islands  1971–2002  Logarithm of GDP, electricity consumption and labor force  EC→Y 
5  Yuan et al. (2007)  China  1978–2004  Electricity consumption and real GDP  EC→Y 
6  Chandran et al. (2010)  Malaysia  1971–2003  Electricity consumption, price and real GDP  EC→Y 
7  Odhiambo (2009)  Tanzania  1971–2006  Logarithm of per capita electricity consumption, energy consumption and real GDP  EC→Y 
8  Ho and Siu (2007)  Hong Kong  1966–2002  Electricity consumption and real GDP  EC→Y 
9  Acaravci (2010)  Turkey  1968–2005  Per capita electricity consumption and real GDP  EC→Y 
10  Iyke (2015)  Nigeria  1971–2011  Per capita electricity consumption, inflation and real GDP  EC→Y 
11  Morimoto and Hope (2004)  Sri Lanka  1960–1998  Electricity consumption and real GDP  EC→Y 
12  Ghosh (2002)  India  1951–1997  Logarithm of per capita electricity consumption and real GDP  Y→EC 
13  Jamil and Ahmad (2010)  Pakistan  1960–2008  Electricity consumption, electricity price and real GDP  Y→EC 
14  Ciarreta and Zarraga (2010)  Spain  1971–2005  Logarithm of electricity consumption and real GDP  Y→EC 
15  Mozumder and Marathe (2007)  Bangladesh  1971–1999  Per capita electricity consumption and real GDP  Y→EC 
16  Narayan and Smyth (2005)  Australia  1966–1999  Real income, electricity consumption and employment  Y→EC 
17  Tang (2008)  Malaysia  1972:Q1–2003:Q4  Logarithm of per capita Electricity consumption and real GNP  EC↔Y 
18  Oh and Lee (2004)  Korea  1970–1999  Logarithm of Real GDP, capital, labor and divisia energy  EC↔Y(LR); EC→Y(SR) 
19  Alam et al. (2012)  Bangladesh  1972–2006  Per capita electricity consumption, energy consumption, CO2 emissions and real GNP  EC↔Y(LR); EC↮Y(SR) 
19  Polemis and Dagoumas (2013)  Greece  1970–2011  Residential electricity consumption, electricity price, GDP, employment, light fuel price, heating and cooling degree days  EC↔Y 
20  Tang et al. (2013)  Portugal  1974–2009  Electricity consumption per capita, real GDP per capita, relative price, trade openness, foreign direct investment and financial development  EC↔Y 
21  Hamdi et al. (2014)  Bahrain  1980:Q1–2010;Q4  Logarithm of per capita electricity consumption and real GDP, foreign direct investment and capital  EC↔Y 
22  Yoo (2005)  Korea  1970–2002  Logarithm of electricity consumption and real GDP  EC↔Y 
24  Ahamad and Islam (2011)  Bangladesh  1971–2008  Per capita electricity consumption and real GDP  EC↔Y 
25  Belloumi (2009)  Tunisia  1971–2004  Per capita energy consumption and real GDP  EC↔YLR); EC→Y(SR) 
26  Stern (1993)  USA  1947–1990  Logarithm of GDP, capital, labor and energy  EC↮Y 
Notes: EC and Y represent electricity (energy) consumption and GDP, respectively. →,↔ and ↮ represent unidirectional, bidirectional and neutral causality, respectively
Source: Author compilation
Descriptive statistics of studied variables
Variable  Definition  Mean  SD  Min.  Max. 

PCEC  Per capita electricity consumption (in kWh)  94.45  87.28  10.50  310.39 
PCGDP  Per capita GDP (in constant 2010 US$)  487.67  164.77  317.70  922.16 
PCGE  Per capita general government final consumption expenditure (in constant 2010 US$)  22.66  10.22  3.999  46.09 
TO  Trade openness  0.2135  0.1378  0.0844  0.4797 
Observations  44 
Unit root tests
Augmented Dickey‒Fuller test  Phillips‒Perron test  

Variables  Intercept  Intercept and trend  Intercept  Intercept and trend  Order of integration 
PCEC  6.7943 (1.000)  1.5231 (0.999)  8.8005 (1.000)  2.4461 (1.000)  
PCGDP  6.8645 (1.000)  1.0661 (0.999)  7.5856 (1.000)  1.4502 (1.000)  
PCGE  3.5628 (1.000)  0.2469 (0.997)  0.4495 (0.983)  −1.0411 (0.927)  
TO  −0.9994 (0.745)  −2.6061 (0.279)  −1.1302 (0.695)  −2.7575 (0.220)  
ΔPCEC  −1.8714 (0.342)  −6.3111 (0.000)  −3.6226 (0.009)  −6.3111 (0.000)  I(1) 
ΔPCGDP  −1.9286 (0.316)  −8.5691 (0.000)  −5.0928 (0.000)  −7.8121 (0.000)  I(1) 
Δ PCGE  −5.6785 (0.000)  −5.6688 (0.000)  −5.6785 (0.000)  −5.6688 (0.000)  I(1) 
ΔTO  −5.4138 (0.000)  −6.2424 (0.000)  −5.4900 (0.000)  −6.2429 (0.000)  I(1) 
Bound test results
ARDL models  Dependent variable  Fstatistic  Decision 

Equation (6)  F_{PCGDP}(PCGDP\PCEC, PCGC,TO)  32.64  Cointegration 
Equation (7)  F_{PCEC}(PCEC\PCGDP, PCGE,TO)  3.35  No cointegration 
Equation (8)  F_{PCGE}(PCGE\PCGDP, PCEC,TO)  10.35  Cointegration 
Equation (9)  F_{T0}(TO\PCGDP, PCGE, PCEC)  8.90  Cointegration 
Lower bound critical value at 1 percent  3.65  
Upper bound critical value at 1 percent  4.66 
ARDL Regression outputs
Dependent variable: D(PCGDP)  

ARDL(3, 1, 3, 1) selected based on AIC  
Variable  Coefficient  Prob. 
Constant  24.31690  0.2030 
PCGDP(−1)  −0.120875*  0.0481 
PCEC(−1)  0.367475***  0.0026 
PCGE(−1)  0.770739***  0.0093 
TO(−1)  22.76833  0.1267 
D(PCGDP(−1))  −0.350540***  0.0087 
D(PCGDP(−2))  −0.373907***  0.0000 
D(PCEC)  0.029607  0.8231 
D(PCGE)  1.131274*  0.0494 
D(PCGE(−1))  −1.319006***  0.0009 
D(PCGE(−2))  1.473489***  0.0003 
D(TO)  −18.17714  0.4241 
Adjusted R^{2}  0.999576  
Fstatistic  8571.084 (0.0000)  
DWstatistic  1.499099 
Notes: Figures in ( ) represent probability values. *, ***Represent significance at 5 and 1 percent level, respectively
Estimated ARDL longrun and shortrun coefficients
Longrun coefficient estimates  
Constant  PCEC  PCGE  TO  
201.1741 (0.0033)  3.040130 (0.0002)  6.376337 (0.0962)  188.3628 (0.2890)  
Shortrun coefficient estimates  
Lag order  0  1  2  
ΔPCEC  0.029607 (0.7716)  
ΔPCGE  1.131274 (0.0010)  −1.319006 (0.0002)  1.473489 (0.0000)  
ΔTO  −18.17714 (0.2353)  
ECT_{t−1}  −0.120875 (0.0000)  
Shortrun diagnostic tests  
Adjusted R^{2}  Jarque‒Bera normality test  Breusch‒Godfrey Serial Correlation LM  Heteroskedasticity Test: ARCH  Ramsey RESET test 
0.958779  1.64901 (0.4384)  1.51090 (0.1075)  2.46798 (0.1183)  0.45095 (0.5074) 
Notes: Diagnostic tests results are based on Fstatistic and figures in ( ) represent probability values
Estimated longrun coefficients
ARDL  FMOLS  DOLS  CCR  

Variable  Coefficient  Prob.  Coefficient  Prob.  Coefficient  Prob.  Coefficient  Prob. 
PCEC  3.04013***  0.0002  1.83555***  0.0000  1.84628***  0.0000  1.85862***  0.0000 
PCGE  6.37633*  0.0962  0.968885  0.3588  2.16904*  0.0747  0.735028  0.4858 
TO  188.3628  0.2890  −22.47856  0.6051  −51.74164  0.2537  −14.40794  0.7326 
Constant  201.1741  0.0033  296.6130  0.0000  281.4285  0.0000  299.0104  0.0000 
Notes: *,***Significant at 10 and 1 percent level, respectively
Causality test results based on the error correction model
Source of causation  

Short run  Long run  Joint (short run and long run)  
ΔPCEC  ΔPCGDP  ΔPCGE  ΔTO 

ΔPCEC,

ΔPCGDP,

ΔPCGE,

ΔTO,


Dept. variable  Fstatistic  tstatistic  Fstatistic  
ΔPCEC  –  1.2772 (0.2981)  0.3071 (0.8201)  0.5304 (0.6645)  −8.472*** (0.0000)  –  7.360*** (0.0021)  5.7321*** (0.0069)  5.8525*** (0.0063) 
ΔPCGDP  6.7740*** (0.0011)  –  10.055*** (7.E05)  2.4106* (0.0845)  −3.382*** (0.0017)  58.060*** (0.0000)  –  37.874*** (0.0000)  36.209*** (0.0000) 
ΔPCGE  0.45460 (0.7158)  9.088*** (0.0002)  –  4.0212** (0.0152)  −0.7636 (0.4501)  0.9394 (0.4002)  0.5119 (0.6037)  –  0.3017 (0.7414) 
ΔTO  3.0890** (0.0404)  1.1267 (0.3524)  0.7416 (0.5349)  –  3.0146*** (0.0047)  4.7341** (0.0150)  7.807*** (0.0015)  4.5771** (0.0169)  – 
Notes: *,**,***Significant at 10, 5 and 1 percent level, respectively
Notes
According to World Bank collection of development indicators (2017).
ARDL approach has several advantages over other previous and traditional methods. The first is that it is flexible, as it allows the analysis with I(0), I(1) or a combination of both data. The second is that ARDL test is relatively more proficient in case of small and finite sample data.
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Further reading
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, Vol. 74 No. 366, pp. 427431.
Phillips, P.C.B. and Perron, P. (1988), “Testing for a unit root in time series regression”, Biometrika, Vol. 75 No. 2, pp. 335346.
Corresponding author
About the authors
Sima Rani Dey is currently working as Assistant Professor in Bangladesh Institute of Governance and Management (BIGM) located in Dhaka. She has completed her graduation and postgraduation in Statistics; she did another masters in macroeconomic policy as well later on. Her research interests are mainly the macroeconomic issues including consumption expenditure, energy, external debt, trade and financial development. Carbon emission, urbanization and migrants are the recent contents of her research. She also has an interest to work on human capital development and poverty in order to examine their impact on the economic growth of Bangladesh.
Mohammed Tareque is Director of Bangladesh Institute of Governance and Management (BIGM) located in Dhaka, capital of Bangladesh. He is a postgraduate of economics and has completed his PhD from Boston University. He has served the Government of Bangladesh as Senior Secretary of Finance Division and possesses a vibrant career for his great contribution in Finance ministry. His research interests are the macroeconomic issues of Bangladesh.