Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

17 

 

Journal of Applied Economics 
and Business Studies (JAEBS) 

Journal homepage: https://pepri.edu.pk/jaebs 

ISSN (Print): 2523-2614 

   ISSN (Online) 2663-693X 

 

 

Do the electricity price shocks influence the Sectoral 

Production and KSE100 Index in Pakistan? An ARDL 

structural breaks approach 

 

Abbas Khan1*, Muhammad Yar Khan2 and Abdul Qayyum Khan3 
1 Department of Management Sciences, COMSAT University Islamabad Wah Campus 
2 Department of Management Sciences, COMSAT University Islamabad Wah Campus 
3 Department of Management Sciences, COMSAT University Islamabad Wah Campus 
 

ABSTRACT 

This research investigates the long-term cointegration of electricity price 

with sectoral production and equity market in Pakistan. Fourteen major 

industrial sectors and the KSE100 index is taken into consideration to 

determine the relationship. Literature in this regard is available but this 

research is distinct from previous literature for it tests the sectoral 

production and equity market relationship with electricity price change 

in Pakistan. Monthly data from 1st Jan 2011 till 31st Dec 2019 is taken 

for fourteen sectors from the sources of Quantum Index Pakistan Bureau 

of Statistics (PBS) and for KSE100 index from (www.investing.com). An 

Auto Regressive Distributed Lag (ARDL) model and bound test for 

multiple structural breaks has been applied. It is found that almost the 

production of all industrial sectors and KSE100 index stock prices are 

adversely affected by the electricity price shocks both in long-term and 

short-term. The study suggests that management should implement a 

moderate monitory policy that is neither more expansionary nor 

contractionary. The government should provide incentives to those who 

successfully control energy wastage. A mixed kind of energy policy is 

recommended with higher weightage to the development of renewable 

energies to reduce foreign exchange outflow with imported furnace oil. 

This study is limited to the sectoral production and equity market of 

Pakistan. A cross-sectional research is encouraged to compare the 

connection between major energy costs and macroeconomic variables in 

different countries. 

 Keywords  
Electricity 

Price 

Change, 

Sectoral 

Production, 

KSE100, 

Economic 

Growth, 

ARDL, 

Pakistan. 
JEL 
Classification 

Q21, D13, 

E23, O4 
 

 

 
* abbaskhanswabi@gmail.com 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

18 

1. Introduction 

The World Bank Enterprise Survey 2015 states that 45.3% of the total firms in Pakistan 

have identified electricity as the top obstacle for the business sector in Pakistan (Bank, 2015). 

Like other developing countries, the shortfall and higher cost of electricity will affect the 

economic activities. At average, in South Asia each firm is facing a load shedding of 5.3 hours 

out of 24 hours while in Pakistan the average load shedding faced by each firm is 13.2 hours 

out of 24 hours (Grainger & Zhang, 2017, 2019). Currently, 50 million people have no access 

to electricity while others in access are facing regular load shedding. About 75% of the firms in 

Pakistan have pointed out electricity as a major barrier to the production growth as shown in 

Figure 1. Pakistan is reckoned on 115 out of 137 countries for its reliable source of electricity 

in the world (Schwab, 2018). In Pakistan the distortion of power sector costs 7% of the total 

GDP, which equals to $18 billion a year. The report analyzes the power supply cycle including 

power generation, and supply to the users. This distortion is caused by poor infrastructure, faulty 

metering and theft cases which increases load shedding and per unit cost. Consequently,  the 

businesses collapse and the units of production are reduced (The World Bank, 2013). Therefore, 

electricity sector reform should be the top priority for the government of Pakistan to quickly 

yield major economic gains which will directly increase the firm’s productivity and reliability. 

It also reduces the cost of production and 𝐶𝑂2 emissions (Grim et al., 2020). 

 

 

Figure 1 Proportion of industries recognizing electricity as a major obstacial to production. 

The early classical study considered energy as the fundamental factor of industrial 

production. The study identified that the output cost varies with the cost of input. The classical 

theory proposed that additional to energy, the labor and capital are other major input costs of 

production (Kümmel, 1982). The concept of the classical theory is contradicted by stating that 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

19 

the importance of energy as input is increased with technological advancement (Jorgenson, 

1984; Rosenberg, 1983). The significance of energy consumption is increased with the 

technological advancement in the industrial production. The pollution taxes and environmental 

control system also discourages the consumption of oil and gas. Thus, the dependency of 

electricity as a major input source for production is increased (Ayres et al., 2013; Guo et al., 

2019; Wu et al., 2019). 

The link between the cost of energy and IP is one of the most important subjects for the 

economic policy makers. Most of the researchers and policy makers focused on the cause and 

effect of overall production, GDP and electricity consumption. Recently, the research aimed to 

investigate the electricity shortage effect on industrial production in Pakistan (Grainger & 

Zhang, 2019). According to the third quarterly report of the State Bank of Pakistan for the year 

2018-2019, the inflation rate increased from 6% to 8.5% in the third quarter of 2019. The cost 

push factors of inflation in the energy sectors are petrol, gas and electricity. It has risen up the 

consumer price index and increased the cost of production (SBP, 2019).  

In Pakistan, mostly the impact of electricity shortage is taken as a proxy with overall 

industrial production (A. Ali et al., 2019; Grainger & Zhang, 2017; Jamil & Ahmad, 2010; 

Yasmin & Qamar, 2013). Traditionally, crude oil price, natural gas price and CPI are used to 

quantity the aggregate output of Pakistan. Specifically, the researchers focused on aggregate 

industrial output rather than sectoral production. Furthermore, the Planning Commission of 

Pakistan (PCP) in 2019 reported 2/3 of the electricity generated from high cost thermal power 

plants which has negatively affected the economy (Government, 2020). Previous studies usually 

investigated the casual connection between energy costs and GDP. There is a gap to quantity 

the impact of electricity price shock and the output of different industrial sectors in Pakistan. 

This research objective is to expand the understanding by investigating the short-run and 

long-run connection between electricity price change and production growth in different 

industrial sectors of Pakistan. The sector specific impact of electricity price change is significant 

for various reasons. Firstly, the impact of the electricity price change is not similar for all sectors. 

The sector sensitivity to electricity price changes should be asymmetric because all the sectors 

might not be exposed to the electricity price change. The sector sensitivity depends on utilization 

of electricity as a major or a minor input source. Secondly, by adding the electricity input cost 

to the final goods available in market may reduce production cut offs. Measuring sectoral 

production sensitivity to electricity price changes is more explanatory than GDP. Thirdly, the 

industries may switch from high cost to low cost energy input. This behavioral heterogeneity of 

the industries will identify the sensitivity to electricity price shocks. 

This study analyzes various industrial sectors in Pakistan thus helping the policy makers and 

concerned reader and researcher to get comprehensive information about a relationship with 

electricity prices. It can help the government authorities to take well informed decisions for the 

betterment of effected industrial sectors by identifying cheaper sources of energies, power 

subsidies, awareness of energy savings and tax relief. 

The paper consists of: Section-2 Review of Literature, Section-3 on Data Collection and 

Methodology, Section-4 as Empirical findings and discussion, Section-5 on Main findings 

lastly, Section-6 as Conclusion. 

 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

20 

2. Review of Literature: 

The study of the connection between energy prices and the sector specific industrial 

production is not common. Although, previous literature mainly focused on the casual 

connection between the consumption of energy and growth in the economy (Bildirici et al., 

2012; Destek & Aslan, 2017; Dogan, 2015; Ghali & El-Sakka, 2004; Ozturk, 2010; Polemis & 

Dagoumas, 2013; Wolde-Rufael, 2014; Wu et al., 2019). A sector specific relationship with 

electricity consumption is carried out between 1993–2006 and 1993-2011. The findings 

proposed that the irregular upsurge in the consumption of electricity is due to the structural 

changes in the production line (Blignaut et al., 2015; Inglesi-Lotz & Blignaut, 2011). Electricity 

consumption is usually more reactive to GDP (Bildirici et al., 2012; Ciarreta & Zarraga, 2010b, 

2010a; Ghali & El-Sakka, 2004). Using the panel data to analyze the connection between 

consumption of electricity and growth in the economy of 12 European countries, but still the 

sectoral investigation is not emphasized (Ciarreta & Zarraga, 2010a). The relationship between 

consumption of electricity, cost of electricity and aggregate production between 1960-2010 is 

investigated. The study found a unidirectional causal relationship between electricity prices and 

GDP (Jamil & Ahmad, 2010). The short-run and long-run effect of electricity consumption and 

its determinants is investigated. The results suggested a short-term price adjustment strategy 

adopted by Pakistan is not efficient. Rather, Pakistan should improve the utilization of power 

generation plants to reduce the cost of electricity available to the industries or to reuse the oil 

and gas resources for generating electricity. Otherwise, a shortage will widely increase the cost 

of electricity available to the industries (Alter & Syed, 2011).  

Another study analyses the connection between consumption of electricity and increase in 

economy. The study determines a positive bi-directional association among the two variables. 

It is further suggested to cover the increasing demand of electricity Pakistan needs an effective 

power generation policy and cost control (Shahbaz & Lean, 2012). The economy and firm’s 

demand for electricity between 1998-2008 is investigated. The findings illustrates a negative 

relationship with 1% increase in the cost of electricity will lead to approximately -0.58% 

reduction in the electricity demand across the firms (Amjad Chaudhry, 2010). 

Indeed, the Islamabad Chamber of commerce and industry in 2013 reported summer as the 

worst season for the public and commercial sectors of Pakistan with a maximum average of 16-

18 hours load shedding all over the country (Magnet, 2013). The shortage of electricity causes 

a decrease in the production, increase unemployment and closure of the industries. 

The study investigated Canada, Ecuador, Norway and South Africa (Fei et al., 2014), 

Bangladesh (Masuduzzaman, 2013), Nigeria (Danmaraya & Hassan, 2016; Polemis & 

Dagoumas, 2013), South Africa (Amusa et al., 2009; Bildirici et al., 2012), China (Zhang et al., 

2017) found a positive connection among consumption of electricity and growth in the 

economy. Another study in Brazil, China, Russia, India and South Africa (Khobai, 2018), 

Pakistan (Balcilar et al., 2019), USA (Alola & Yildirim, 2019) supports energy as a main driver 

of the economy. The literature has the opinion that the cost of electricity and consumption have 

a significant role in the industrial production. This implies that higher electricity prices have a 

negative impact on the consumption of electricity and production growth. Other studies by 

(Capros et al., 2016; Gonese et al., 2019) examined the effect of electricity and gas prices on 

sectoral production in the European Union (EU) and South Africa. The findings suggest that 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

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electricity is an essential part of the production having a significant effect on the output growth 

of different sectors in both EU and South Africa. 

The shortage of Electricity will increase the input cost by using diesel generators in 

producing electricity. It will reduce the capital available for productive use leading to decrease 

in the output. More critically, if the alternative source of electricity generation is not available 

during shortages  it may lead to shut down industries, spoil of useful raw materials and labor 

productivity (Allcott et al., 2016). Continuous electricity shortage compels firms to outsource 

electricity which will increase the output cost (Fisher-Vanden et al., 2015). Due to higher 

electricity cost firms avoid using energy intensive technology. Adopting this strategy leads to 

decrease in the long term productivity growth (Abeberese, 2017). 

Another research examines the casual connection between growth in GDP and demand of 

electricity. It concludes bi-directional association between change in GDP and consumption of 

electricity (Faisal et al., 2017). A comprehensive overview of various studies in China spanning 

from 1978 to 2016 concluded a significant relationship between electricity consumption and 

growth in the economy (Zhang et al., 2017). Another study investigates the casual relationship 

between electricity demand and GDP. A long-term bi-directional interdependency is found 

between both variables, but the relationship is insignificant in the short-run (Hasan et al., 2017).  

The urbanization and electricity consumption are investigated in Pakistan considering 

technology and transformation. It is found that both variables have a unidirectional positive 

impact on electricity consumption (Shahbaz et al., 2017). The energy insecurity is explained as 

the unavailability of energy at reasonable prices. A study analyzed the energy security of 

Pakistan in four dimensions as accessibility, availability, affordability, and applicability. The 

results found that the Pakistan economy is continuously insecure over the last five years. It is 

recommended that Pakistan should move toward green energy and advance metering systems 

(S. Malik et al., 2020). The energy security is investigated between GDP growth and electricity 

consumption of the developing economy in South Asia. The results reviled there is no long-

term connection between consumption of energy and aggregate output. A 1% increase in the 

population will increase the consumption of electricity by 4.16%. It is suggested to use large 

scale hydropower to improve energy efficiency and climate control in the developing country 

of South Asia like Nepal (Paija, 2019).  

Additionally, there is a significant effect of energy costs on macro-economic variables 

(Taghizadeh-hesary et al., 2015), energy insecurity also has great effect on finish goods and 

food prices (Taghizadeh-hesary et al., 2019). Energy price shocks influencing different 

macroeconomic factors i.e., GDP, rate of interest, rate of inflation, foreign exchange rate, human 

development, stock and bond prices, portfolio optimization and business cycle (Ahmed et al., 

2018; Marza & Daly, 2018; Naser, 2019; Nazlioglu et al., 2019; Pönkä & Zheng, 2019; S. 

Sarwar et al., 2019; Waheed et al., 2018; Wesseh & Lin, 2018). Historically, the industrial 

production considered to be positively correlated with stock market return. It is further 

concluded that the growth in the industrial production largely represent the price movement of 

the stock market (Fama, 1981, 1990). Recently, a large proportion of researches constituted a 

literature which investigates the relationship between stock market and industrial production in 

different countries and scenarios. A study examined the cointegration between the stock price 

and industrial production, supply of money and foreign exchange rate by using long-term 

cointegration bound test. The results found a long-term cointegration between the stock price 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

22 

movement and all the macroeconomic variables (Bekhet & Matar, 2013). There are many 

studies investigating the causality between equity market return and macroeconomic variables 

but the causality in the context of non-linear situation is investigated in China. It is concluded 

that the causality still exists between the stock price and macroeconomic variable in non-linear 

condition (Borjigin et al., 2018). It is further concluded that industrial production and inflation 

rate performs a vital part in the equity return volatility during long-run and short-run prospects 

(Engle et al., 2013). It is further added that macroeconomic fundamentals are playing an 

important role in speculation of stock market return (Girardin & Joyeux, 2013). Another study 

has taken the industrial production and long-term interest rate as a factor that influences the 

European stock price movement. The finding revealed that the weight has clearly moved from 

interest rate to industrial production. It is concluded that IP has a greater impact of stock prices 

in comparison to interest rate (Peiro, 2016). 

Pakistan Stock Exchange (PSE) is established in 2016 by merging three different stock 

markets i.e., Karachi, Lahore, and Islamabad. It is regulated by Security Exchange Commission 

of Pakistan (SECP). KSE100 includes the top 100 multiple sector companies enlisted in PSE 

based on higher market capitalization. 

Like other countries, in Pakistan energy is the major driver of the economy. In Pakistan 

most electricity is generated using thermal power plant (Solangi et al., 2018; Zameer & Wang, 

2018). Pakistan is facing a shortfall of 65000 Mega Walt (MW) due to load shedding. To fulfill 

the required demand of electricity in Pakistan by using imported oil from gulf countries. It is 

concluded that Pakistan’s economy is exposed to energy price shocks like other developing 

countries (Wakeel et al., 2016).  

In the context of Pakistan, previous literature found a negative connection of energy cost 

with economy. The relationship of individual sector contributing to the economy is not yet 

identified. All industrial sectors are individually contributing an important part in the economic 

development of a country. The study will provide new foresight by finding out the effect of 

electricity cost on sectoral production of Pakistan. This study will help energy policy makers to 

identify the most sensitive sectors to electricity price shocks. Further, it will grab the attention 

of policy makers to make a sophisticated energy policy for all affected industrial sectors. 

Additionally, the results will help the investors to identify the energy price relationship with 

stock market return in Pakistan. Further, the association between IP and equity market return 

will be cross validate. 

3. Data Collection 

This study considers 14 industrial sectors that are critically important for the GDP of 

Pakistan. These industrial sectors include automobiles (cars, cars parts and lubricants), 

chemicals (refineries, petrochemicals, metallurgical and mineral based products), food, 

beverages and tobacco (cigarettes, foods sub products), iron & steel products (construction 

materials), coke petroleum products (oil and gas products, distribution services, alternative 

energy resources), paper board (raw paper, packing, plastics and construction materials), 

pharmaceuticals (medicines and surgical products, health care and biotechnological products), 

rubber products (general use rubber products for home and commercial use), nonmetallic 

mineral products (cement, ceramics, glass, and lime), textile ( animal wool and silk, cotton, flax 

and bamboo, glass fiber and synthetic materials), fertilizers (agricultural products), electronics 

(house hold equipment and heavy duty machinery), leather products (cloths, shoes, bags etc.), 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

23 

wood products (furniture and fixtures), engineering products (construction and material, 

manufacturing equipment). Additionally, the KSE100 index that comprises of the top 100 

companies from all the 14 industrial sectors of Pakistan.  

Large-scale manufacturing data is available on the Quantum Index (QI). QI measures output 

and structural changes of large-scale manufacturing industries. It provides data regarding 

production, raw material, contribution to GDP, fix assets and large-scale manufacturing taxes. 

It also provides data regarding new industrial development and production. In terms of data this 

study uses monthly data during 1st Jan 2011 and 31st Dec 2019 (Zhang et al., 2017). All the 

industrial sector data is available in Pakistan Bureau of Statistics (PBS). QI is calculated at 

constant factor cost of year 2005-2006 with help of Laspeyer’s formula (Biggeri et al., 2017) as 

equation 1 base year 2005-2006. 

    𝑄𝐼(𝑃𝑀 ) =
𝑀(𝑛)

𝑀(0)
∗ 100                         Eq 1 

Equation 2 calculates monthly growth rate electricity relative to base year CPI 2007-2008: 

      𝐶𝑃𝐼(𝐸𝑀 ) =
𝑀(𝑛)

𝑀(0)
∗ 100                        Eq 2 

Equation 3 calculates the log return of the closing prices. 

                             𝐾𝑆𝐸100(𝑅𝑚 ) = 𝐿𝑛 (
𝑝(𝑛)

𝑝(0)
)            Eq 3 

Where, in equation 1 the “𝑃𝑀” represents a large-scale manufacturing industry, “𝑀(𝑛)” is 
the real output for the current month and “𝑀(0)” is the real output of the base year 2005-2006 
and in equation 2 “𝐸𝑀” represents electricity prices, “𝑀(𝑛)” is the current month CPI and 
“𝑀(0)” is the previous month CPI. The data for the control variables like government 
expenditure, money in circulation, foreign direct investment and wholesale prices are extracted 

from the Statistics of Pakistan’s Economy report, 2018 available on website of State bank of 

Pakistan (SBP) (Yasmeen et al., 2019). KSE100 index monthly data is extracted from 

(www.investing.com). In Equation 3 the 𝑅𝑚 is the log stock return, 𝑝(𝑛) is the current price, 

𝑝(0) is the previous month price, and Ln is a natural log (Hanif, 2020). 

Further, adding the control variable like government expenditure, money in circulation, 

foreign direct investment and wholesale prices can improve the relationship between electricity 

price and sectoral production. Higher electricity prices lead to increase the production cost 

which has a direct impact of the whole sale prices of the unit produced which creates an 

inflationary situation in the country. It depresses the saving power of the public and increases 

the money supply in the country. Money supply has a positive effect on the growth of Industrial 

Production (IP). Foreign Direct Investment (FDI) also has a positive effect on the industrial 

production. But the increase in the energy prices may lead to decrease in the FDI due to increase 

in the cost of production. On other hand, Pakistan is generating electricity mostly form imported 

furnace oil but due to decrease in the Pakistani rupee comparatively with dollar the energy prices 

will increase which also leads to increase in the production cost and decrease the overall 

production of the industry. 

Due to circular debts many industries are tax default which leads to decrease the government 

tax revenue and increase the budget deficit. It leads to shutdown the production of different 

industries in Pakistan (Yasmeen et al., 2019). The list of variables is mentioned in Table 1. 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

24 

Table 1: Acronyms of the variables 

Acro Full Title 

ELEP Electricity Prices 

FDI Foreign Direct Investment 

GXP Government Expenditure 

MC Money in Circulation 

WPI Wholesale Price Index 

AUT Automobiles Chemicals (cars, cars parts and lubricants) 

CHE Chemicals 

FOO Food, Beverages Tobacco (cigarettes, foods sub products 

IRO Iron Steel Products (construction Materials) 

COK Coke Petroleum Products (oil and gas products, distribution services, 

alternative energy resources 

PAP Paper Board (raw paper, packing, plastics and construction materials) 

PHA Pharmaceuticals (medicines and surgical products, health care and bio 

technological products) 

RUB Rubber Products (general use rubber products for home and commercial use) 

NON Nonmetallic Mineral Products (cement, ceramics, glass, and lime 

TEX Textile (animal wool and silk, cotton, flax and bamboo, glass fiber and 

synthetic materials) 

FER Fertilizers (agricultural products) 

ELE Electronics (household equipment and heavy-duty machinery) 

LET Leather Products (cloths, shoes, bags etc.) 

WOO Wood Products (furniture and fixture) 

ENG Engineering Products (construction and material, manufacturing equipment) 

KSE100 Karachi Stock Exchange Top 100 firms 

ARDL Autoregressive Distributed Lag 

ADF Augmented Dickey–Fuller 

PP Phillips–Perron 

CUSUM Cumulative Sum 

CUSUMSQ Cumulative Sum of Squares 

4. Methodology 

This study investigates the effect of electricity price changes on industrial sector’s 

production using multifactor nonlinear regression analysis considering the open economy 

industrial sector (IS) function for sectoral production. It determines the effect of electricity price 

changes in sectoral production. Each model includes electricity price as an explanatory variable. 

To increase the model goodness of fit the study considers other four explanatory variables i.e., 

government expenditure (GXP) on projects, money in circulation (MC) due to general public 

investment, Foreign Direct Investment (FDI) and Wholesale Prices (WPI) (Bohi, 2017; Jo, 

2014; Yasmeen et al., 2019). 

This research utilized Auto Regressive Distributed Lag (ARDL) model to investigate the 

impact of electricity prices on sectoral production in Pakistan (Akadiri et al., 2019; Shin & 

Smith, 2001). The ARDL model is gaining increasing popularity because of high potential and 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

25 

less glitches connected with it in comparison with other cointegration models (M Hashem 

Pesaran, 1997; M Hashem Pesaran & Shin, 1998; Yasmeen et al., 2019). The Eagle Granger 

method is used to examine the connection between two variables and for more than two 

variables then the Johansen Cointegration is used (Econometrics, 2015; Engle et al., 1987; 

Johansen, 1988). Vector Auto Regressive (VAR) Model has certain shortcomings. It is applied 

only when there is a large sample size used in the study and the VAR model prerequisite is that 

all the variables must be stationary at the same level (Johamen & Jtiselius, 1990). In comparison 

to VAR the ARDL model has some additional benefits i.e., ARDL can be used for small sample 

size, if the variables are stationary at level or at first order difference or a mix of both while 

Johansen cointegration the variables must be in similar order difference (Mohammad Hashem 

Pesaran & Pesaran, 1997; Shin & Smith, 2001). ARDL allow variables with optimal lags while 

it is not allowed in other conventional cointegration models. By applying bound test the OLS 

model is transformed to Error Correction Model (ECM). ECM helps further in adjusting the 

long-run and short-run relationship without losing the long-run information (Laurenceson & 

Chai, 2003). As discussed, earlier ARDL approach cannot be functional if there are second order 

difference I (2) stationary variables. For testing the stationarity of the variables with I(0) and 

I(1), the ADF and PP tests are used to test 𝐻0 of a unit root (Dickey & Fuller, 1979; Phillips & 
Perron, 1988). For testing the unit root most of the studies used ADF and PP tests. In time series 

data, due to structural breaks the ADF and PP have low power of finding unit root therefore the 

multiple structural break test is used (Bai & Perron, 2003; Balcilar et al., 2017; Smith et al., 

2019a).  

Further, ARDL approach has two steps. Firstly, to test the 𝐻0 of no long run cointegration 
between the variables. By using the f-statistics value the existence of cointegration is confirmed 

than the study further interprets the coefficients for long-run and short-run. The ARDL model 

generates the lower bound I(0) and upper bound I(1) critical values. The f-value greater than 

upper bound I(1) it means long-run cointegration exists in the relationship and the 𝐻0 is rejected. 
In contrast, if the f-statistics is lesser than lower bound I(0) we can accept the 𝐻0 and ARDL 
approach cannot be applied. The results are inconclusive to apply ARDL in case the f-value is 

in between lower and upper bound. In 2017, the Bound test is found significant for small sample 

size (Ahmed et al., 2018; Garg & Prabheesh, 2020). The usefulness of small sample size i.e., 30 

to 80 observations is supported  (Narayan, 2007). This study uses the smaller sample size based 

on the methodology of (Yasmeen et al., 2019). By applying the ARDL model for different 

industries the ECMs are calculated as following. 

∆𝐴𝑈𝑇𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2018
+  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 4 

∆𝐹𝑂𝑂𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2017 + 𝜇𝑡 

Eq 5 

∆𝐼𝑅𝑂𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2017
+ 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 6 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

26 

∆𝐶𝑂𝐾𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2013
+ 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡  

 

Eq 7 

∆𝑃𝐴𝑃𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2017
+ 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 8 

∆𝑃𝐻𝐴𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2017
+ 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 9 

∆𝑅𝑈𝐵𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2016
+ 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 10 

∆𝑁𝑂𝑁𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2011
+ 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 11 

∆𝑇𝐸𝑋𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2015
+ 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 12 

∆𝐹𝐸𝑅𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2013
+ 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 13 

∆𝐸𝐿𝐸𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2013 + 𝜇𝑡 

Eq 14 

 

 

∆𝐿𝐸𝑇𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2016
+  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 15 

∆𝐸𝑁𝐺𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 +  𝑑𝑢𝑚𝑚𝑦2013
+  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 16 

∆𝐾𝑆𝐸100𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝛿2∆𝐹𝐷𝐼𝑡−1 + 𝛿3∆𝐺𝑋𝑃𝑡−1 + 𝛿4∆𝑀𝐶𝑡−1 + 𝛿5∆𝑊𝑃𝐼𝑡−1 + 𝜇𝑡  

Eq 17 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

27 

In Equation 4,” 𝛽0” is a constant and “𝛽1 𝑡𝑜 𝛽5” are utilized for error correction in the 
model. The dummy variables are used after applying the structural break bound test (Bai & 

Perron, 2003; Yasmeen et al., 2019). The “∆” and “𝜇𝑡” represent the white noise error term. The 
long run association among the variables is represented by “𝛿1 𝑡𝑜 𝛿5”. The ARDL model 
estimates “(𝑛 + 1)𝑘” times regression to get optimal lags length criteria. Where “𝑛” is 
maximum number of lags and “𝑘” is the number of variables under investigation. The ARDL 
model is applied to check the long-term cointegration among the variables by using Wald F-

statistics. The null hypothesis is no long-term cointegration which is “𝐻0 = 𝛿1" 𝑡𝑜 "𝛿5 = 0”. 
The alternative hypothesis “𝐻𝑎" is long-term cointegration among the variables. Equation 5 to 
17 follow the same explanation.  

By evaluating long run cointegration by using f-Statistics value and applying the bound test for 

structural breaks and finding long term coefficients in the above model the study further finds 

the short-term coefficients using the model below. 

∆𝐴𝑈𝑇𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

𝑛1𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2018

+  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡  

Eq 18 

∆𝐹𝑂𝑂𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛2𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2017 + 𝜇𝑡  

Eq 19 

∆𝐼𝑅𝑂𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛3𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2017 + 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡  

Eq 20 

∆𝐶𝑂𝐾𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛4𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2013 + 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

 

Eq 21 

∆𝑃𝐴𝑃𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛5𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2017 + 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 22 

∆𝑃𝐻𝐴𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛6𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2017 + 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡 

Eq 23 

∆𝑅𝑈𝐵𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝛿1∆𝐸𝐿𝐸𝑃𝑡−1 + 𝑛7𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2016 + 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡  

Eq 24 

∆𝑁𝑂𝑁𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛8𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2011 + 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 25 

∆𝑇𝐸𝑋𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛9𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2015 + 𝑑𝑢𝑚𝑚𝑦2018 + 𝜇𝑡 

Eq 26 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

28 

∆𝐹𝐸𝑅𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛10 𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2013 + 𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡  

Eq 27 

∆𝐸𝐿𝐸𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛11𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2013 + 𝜇𝑡  

Eq 28 

 

 

∆𝐿𝐸𝑇𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛12 𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2016 +  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡  

Eq 29 

∆𝐸𝑁𝐺𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛13𝐸𝐶𝑇𝑡−𝑖 +  𝑑𝑢𝑚𝑚𝑦2013 +  𝑑𝑢𝑚𝑚𝑦2019 + 𝜇𝑡  

Eq 30 

∆𝐾𝑆𝐸100𝑡 = 𝛽0 + ∑ 𝛽1𝑖 ∆𝐸𝐿𝐸𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽2𝑖 ∆𝐹𝐷𝐼𝑡−1 +

𝑛

𝑖=1

∑ 𝛽3𝑖 ∆𝐺𝑋𝑃𝑡−1 +

𝑛

𝑖=1

∑ 𝛽4𝑖 ∆𝑀𝐶𝑡−1 +

𝑛

𝑖=1

∑ 𝛽5𝑖 ∆𝑊𝑃𝐼𝑡−1

𝑛

𝑖=1

+ 𝑛14𝐸𝐶𝑇𝑡−𝑖 + 𝜇𝑡  

Eq 30 

Equation 18 represents the short run cointegration among the IVs and DVs. The “𝐸𝐶𝑇” is 
the Error Correction Term. 𝐸𝐶𝑇 is used when there is abnormality in the data, showing how 
long it will take to get back into its normal position in the long-term. “𝑛1” is the coefficient of 
the 𝐸𝐶𝑇. The dummy variables are used due to structural breaks in the data. The same 
explanation is followed by Equation 19 to 31. The model stability of both short-term and long-

term cointegration is tested by using CUSUM and CUSUMQ (Evans, 1974).  

5. Results: 

This portion of the study explains the unit root testing, structural breaks in the data, model 

fitting, auto correlation and heteroscedasticity of the data. In Table 2 provides the results of 

ADF and PP unit root test to investigate the stationarity at I(0) and I(1). The results confirmed 

that all the variables are stationary at level and first order difference and a mix of both. The 

prerequisite of the ARDL has been accepted which means all the variables are stationary at level 

or first difference.  

Table 2: Unit Root Test 

Variables  
ADF PP 

I(0) I(1) I(0) I(1) 

ELE  -6.1652*** (1) -10.6589*** (1) -6.1516*** (1) -36.2876*** (1) 

ELEP  -10.5650*** (1) -9.8471*** (1) -10.5716*** (1) -104.0480*** (1) 

ENG -2.3713 -11.1627*** (2) -3.6598 -23.1542*** (2) 

FER -3.3128 -6.7570*** (2) -2.5401 -6.5013*** (2) 

IRO -3.0672 -10.2214*** (2) -3.0758 -10.2287*** (2) 

LET -3.9652*** (1) -10.3248*** (1) -3.9652 -23.2421*** (1) 

NON -3.6235*** (1) -13.8405*** (1) -3.3212*** (1) -16.3095*** (1) 

RUB -3.5172*** (1) -13.5994*** (1) -3.4465*** (1) -13.6553*** (1) 

TEX -3.6453*** (1) -13.4525*** (1) -3.5125*** (1) -14.0943*** (1) 

AUT -0.4199  -10.2469*** (1) -0.41998 (1) -10.2469*** (1) 

CHE -2.4073  -14.0132*** (1) -3.0274*** (1) -16.261*** (1) 

COK -4.6401*** (2) -10.4101*** (2) -4.6198*** (2) -16.2024*** (2) 

FOO -1.7829*** (2) -13.9188*** (2) -2.2189 -13.9077*** (2) 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

29 

PAP -3.2660*** (0) -12.3287*** (0) -3.0935*** (0) -13.3710*** (0) 

PHA -3.5810***(0) -7.4020***(0) -6.9604***(0) -31.9163***(0) 

FDI -12.2934*** (0) -11.0515*** (0) -12.4329*** (0) -61.1246*** (0) 

MC -2.5674 -4.6406*** (0) -2.4973 -4.7361*** (0) 

WPI -2.5731 -9.0003*** (0) -2.8641 -8.9984*** (0) 

GXP -2.8790 -3.7623*** (0) -2.3481 -5.6263*** (0) 

KSE100 -11.0121***(0) -9.1917***(0) -11.0387***(0) -72.8086***(0) 

Test Critical Value 

1% level -3.494378 -3.494378 -3.493129 -3.493747 

5% level -2.889474 -2.889474 -2.888932 -2.8892 

10% level -2.581741 -2.581741 -2.581453 -2.581596 

Note: All the regressions are based on trend and intercept, the optimal lags are selected using Schwarz 

Bayesian Criterion (SCI ), () is used for optimal lags, *** is P-value less than 0.05 means the null 

hypothesis of unit root is rejected. Critical Value at 1%. 

As discussed previously, the findings of unite root testing are ambiguous due to structural 

changes in the time series data. Following the procedure used previously by applying Bai–

Perron multi structural breaks test (Bai & Perron, 2003; Balcilar et al., 2017; Smith et al., 2019b; 

Yasmeen et al., 2019). Before exploring the short-term and long-term cointegration it is 

important to find structural changes in the data. Table 3 is providing the summary of multiple 

structural breaks in the data under study. It is observed that all the variables include structural 

breaks. For AUT, CHE, COK, ELE, ENG, FER, FOO, NON, PAP and PHA with one structural 

break, one dummy variable is added. For IRO, LET, RUB and TEX with more than one 

structural break, multiple dummy variables are added. Lastly the KSE100 index has no 

structural breaks in the time series data. Structural breaks improve the model stability and 

provide more significant results for interpretation.  

Table 3: Bai–Perron structural breaks in the data 

Variables  Schwarz* Criterion LWZ* Criterion 

AUT 2018M06  2018M06  

CHE 2015M09  2015M09  

COK  2013M10  2013M10  

ELE 2013M09 2013M09 

ENG 2013M12 2013M12 

FER 2012M10 2012M10 

FOO 2017M10 2017M10 

IRO 2012M07, 2017M05, 2018M08  2012M07, 2017M05, 2018M08  

LET 2012M04, 2013M07, 2016M08  2016M08 

NON 2013M07, 2014M11, 2016M03, 2018M07 2015M11, 2018M07 

PAP 2017M08  2017M08 

PHA 2017M08 2017M08 

RUB 2012M06, 2013M10  2012M06  

TEX 2012M04, 2018M07 2012M04, 2018M08 

ELEP 2013M12 2013M12 

FDI 2014M10  2018M06  

MC 2013M09 2013M09 

WPI 2017M10 2017M10 

GXP 2017M08  2017M08 

KSE100 N/A N/A 

** Bai–Perron critical values. 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

30 

  

In table 4 interpreting the F-value of bound test to validate the presence of long-term 

cointegration against the 𝐻0 of no long-term cointegration. Different structural breaks are 
noticed in 2013, 2015, 2016 and 2017. The reasons are Pakistan is producing 64% of electricity 

from thermal power plant but in 2013 due to shortage of oil and gas the country lost a potential 

of 3000MW of electricity generation. Pakistan is using imported furnace oil and gas imported 

from other countries (Ministry of Finance, 2013). In 2015-2016 circular debt is the main reason 

of structural breaks when the power generating companies get defaulted and failed to pay dues 

to the oil and gas suppliers. The power generating companies are unable to generate enough 

sales due to inefficient power distributing companies like (DISCOs). Power distributing 

companies have no control on electricity theft cases, power distribution losses and low-cost 

tariffs. During 2015 and 2016 the circular debt has increased from 05 billion to 06 billion 

(Tauhidi & Chohan, 2020). In 2017 onward there is a 20%  depreciation in the Pakistan rupees 

against the U.S. dollar which made the import of furnace oil more expensive and result in 

decrease of foreign exchange reserves from $9.9 bn to $8.1 bn within four months  (Simon 

Nicholas & Buckley, 2018). 

In each model sectoral output, KSE100 index is taken as a dependent variable and electricity 

prices, FDI, WPI, MC and GXP as IV. The dummy variables are also added because of structural 

breaks. The f-statistics of bound test are interpreted to verify the long-term cointegration in the 

data. In table 4 the F-value of all variables are higher than upper bound which means the long-

term cointegration among the variables.  

Variables 

Bound test 

cointegration 

F-Value 

I(0) I(1) remarks 

AUT 

9.5155 3.79 4.85 

long term 

cointegration  

CHE 

27.1777 3.79 4.85 

long term 

cointegration  

COK  

58.7201 3.79 4.85 

long term 

cointegration  

ELE 

35.1667 3.79 4.85 

long term 

cointegration  

ENG 

22.2469 3.79 4.85 

long term 

cointegration  

FER 

46.9500 3.79 4.85 

long term 

cointegration  

FOO 

20.0303 3.62 4.16 

long term 

cointegration  

IRO 

61.3658 3.62 4.16 

long term 

cointegration  

LET 

5.00531 3.62 4.16 

long term 

cointegration  

NON 

6.90343 3.62 4.16 

long term 

cointegration  

PAP 

65.4195 3.62 4.16 

long term 

cointegration  



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

31 

PHA 

49.3819 3.62 4.16 

long term 

cointegration  

RUB 

117.4260 3.62 4.16 

long term 

cointegration  

TEX 

07.7853 3.62 4.16 

long term 

cointegration  

ELEP 

06.3514 3.62 4.16 

long term 

cointegration  

FDI 

35.1667 3.62 4.16 

long term 

cointegration  

MC 

61.3658 3.62 4.16 

long term 

cointegration  

WPI 

65.4195 3.62 4.16 

long term 

cointegration 

GXP 

117.4260 3.62 4.16 

long term 

cointegration  

KSE100 

24.0643 2.56 3.49 

long term 

cointegration 

Note: If the value of F-Stat is > than 4.16 the Long-Term Cointegration exist, If the F-Stat value is < 

3.62 Short-Term Cointegration exists and if the value is between 3.62 and 4.16, the model is 

inconclusive. 

This study has taken the sectoral production to check the long-term and short-term 

relationship with electricity price change in Pakistan. The table 5 provides the results of long-

term and short-term cointegration for all industrial sectors. In the context of Pakistan, electricity 

is one of the major input source to produce output (Yasmeen et al., 2019). It is very important 

to investigate the fluctuation of energy prices and its impact on the economy. The study 

investigated the impact using ARDL models.  

The result in table 5 indicates that all the IVs have significant and long-term negative 

relationship with electricity cost. The negative relationship indicates that all the sectoral 

productions are exposed to the electricity price shocks. These negative results have some serious 

consequences. On the supply side all sectors are highly dependent on electricity price shocks. 

The operations of all industrial sectors are highly dependent on energy prices and negatively 

affect the production growth and profitability (Zameer & Wang, 2018). On demand side, the 

industries and households also affect the electricity price shocks. It increases the expenditure 

and reduces the purchasing power of the public which leads to reduce the unnecessary purchases 

and increase savings. As a result, it decreases the aggregate demand of the finished products 

and cutoff on the productions of the industries. Electricity price is playing one of the vital 

positions in the growth of Pakistan’s economy. In Pakistan electricity generation is extremely 

in need of imported furnace oil (Zameer & Wang, 2018). Preceding researches focused on the 

impact of oil price in industrial sectors of Pakistan (Yasmeen et al., 2019). Another study 

examines the impact of oil price variation on monetary policy (A. Malik, 2008). Other economic 

variable like inflation and interest rates are checked with oil price shocks and found a positive 

long run association between rate of inflation and interest rate (K. Malik et al., 2017). The 

impact of oil price shocks on the stock exchange is also investigated (Najaf & Najaf, 2016; 

Waheed et al., 2018). Recently, the connection between oil price shocks and trade deficits is 

investigated and found a positive relationship between increase in oil price and trade deficit 

(Ahad & Anwer, 2020). Additionally, the electricity prices are extremely in need of oil prices 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

32 

and suffering from a severe crisis in the last two periods. This study focuses on electricity prices 

and sectoral production growth in Pakistan. The study on individual industrial sector is rare. All 

the industrial sectors have a negative significant connection with electricity price shocks in long 

run and short run except coke petroleum products (oil and gas products, distribution services, 

alternative energy resources), engineering products (construction and material, manufacturing 

equipment), nonmetallic mineral products (cement, ceramics, glass, and lime) and paper board 

(raw paper, packing, plastics and construction materials) that have an insignificant short-term 

relationship with electricity price shocks.  

Table 4: Long-run and Short-run Coefficients using ARDL Models. 

Variable ARDL Long-Term (P-value) ARDL Short-Term (P-value) 

AUT -0.9467(0.0000) -0.2494(0.0391) 

CHE -1.2416(0.0000) -0.4307(0.0000) 

COK  -1.6416(0.0000) -0.3935(0.0779) 

ELE -1.8475(0.0000) -0.3802(0.0024) 

ENG -0.9827(0.0000) 0.0172(0.7167) 

FER -0.6732(0.0000) 0.3267(0.0013) 

FOO -1.3687(0.0000) -0.3687(0.0075) 

IRO -0.0757(0.0041) 0.9242(0.0000) 

LET -0.3193(0.0000) 0.6727(0.0000) 

NON -1.3263(0.0000) -0.3263(0.0619) 

PAP -1.1882(0.0000) -0.1882(0.1975) 

PHA -1.5588(0.0000) -0.5588(0.0007) 

RUB -0.1220(0.0021) 0.5762(0.0000) 

TEX -0.2362(0.0002) 0.6423(0.0000) 

KSE100 -1.0829(0.0000) -0.0018(0.0100) 

Note: () is the p-value which is significant at 1%, 5% and 10%. 

6. Robustness test of the Models: 

The diagnostic investigation is applied to check the stability of the estimated models. 

Following tests are used: Breauch–Godfrey LM test to check serial correlation with the 𝐻0 of 
no serial correlation, Jarque-Bera test to check the validity of 𝐻0 is normally distributed, 
Breusch-Pagan-Godfrey test for heteroskedasticity in the model. In table 5, the findings of the 

LM test provide the information about the serial correlation. It is determined that the p-value is 

greater than 5%, which accept the  𝐻0 of no serial correlation in all the variables. The value of 
𝑅2 is very high which confirms the appropriateness of the models. The results of Breusch–
Pagan–Godfrey heteroscedasticity test accepts the null hypothesis of homoscedastic for all the 

residuals. The results of Jarque–Bera test are insignificant which accept the null hypothesis of 

normality. After applying the diagnostic test for model’s stability. The CUSUM and CUSUMQ 

tests are applied to test the coefficient’s constancy (Brown et al., 1975; Khan et al., 2020; 

Yasmeen et al., 2019). The results of CUSUM and CUSUMQ in figure 1 to 14 demonstrates all 

the coefficients are stable and within the boundaries of 5% significance level. Summing-up all 

the results the models used in the study are stable, residual normally distributes and 

homoscedastic, having no auto correlation and free of errors. It is derived that the association 

between sectoral production growth and price of electricity is justified which can be interpreted 

and used for future policy implications.  
Table 5: Diagnostic Test Results for All Models 

Variables  LM Test:  𝑅2 (F-Stat) Heteroscedasticity  Normality  



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

33 

AUT 0.067951(0.7943) 0.92 10.00663(0.0008) 0.131973(0.3884) 65.7885(0.3210) 

CHE 0.022234(0.1428) 0.81 0.991776(0.0000) 6.011307(0.4219) 92.1418(0.1641) 

COK 3.36237(0.1862) 0.96 2.722422(0.0000) 1.127679(0.0614) 56.272(0.1821) 

ELE 7.981973(0.0568) 0.95 1.856718(0.0000) 10.69740(0.0982) 41.6351(0.1321) 

ENG 1.813207(0.6121) 0.82 10.34549(0.0000) 0.007015(0.1486) 31.0100(0.1131) 

FER 0.879715(0.6441) 0.81 2.962061(0.0106) 0.001733(0.9677) 85.7640(0.0841) 

FOO 3.171740(0.2048) 0.87 3.429027(0.0025) 2.156707(0.9507) 46.6993(0.0731) 

IRO 0.128622(0.7199) 0.94 326.4993(0.0000) 12.41041(0.0596) 219.7135(0.1531) 

LET 1.240154(0.5379) 0.89 24.11097(0.0000) 23.46367(0.3818) 44.9160(0.32110 

NON 2.632324(0.2682) 0.95 2.694657(0.0136) 11.25665(0.1278) 60.7815(0.2001) 

PAP 2.353056(0.3083) 0.79 1.364547(0.0000) 1.714686(0.6337) 44.9160(0.1321) 

PHA 7.092175(0.2880) 0.88 15.15688(0.0000) 15.96735(0.1200) 81.2668(0.1231) 

RUB 0.079018(0.9613) 0.90 66.39520(0.0000) 67.75367(0.8654) 44.3294(0.3214) 

TEX 0.156421(0.1564) 0.73 28.08270(0.0000) 8.548380(0.2006) 37.4473(0.2131) 

KSE100 0.4261(0.5155) 0.80 03.1029(0.0037) 0.003415 (0.6848) 0.0413(0.9795) 

Note: The p-values are in closed in (), LM test for  Serial Correlation, If the p-value > 0.05 the  𝐻0 of 
no serial correlation is accepted. Jarque–Bera test if p-value > 0.05 the  𝐻0 of normality is accepted,  
Breusch–Pagan–Godfrey heteroscedasticity test. If critical value > 0.05 the  𝐻0 is the data is 
homoscedastic.  

 

 
Figure 2 CUSUM and CUSUMQ for Automobiles Industry Model 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

34 

 
Figure 3 CUSUM and CUSUMQ for Chemical Industry Model. 

 

Figure 4 CUSUM and CUSUMQ for Coke Petroleum Products Industry Model. 

 

Figure 5 CUSUM and CUSUMQ for Electronics Industry Model. 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

35 

 

Figure 6 CUSUM and CUSUMQ for Engineering Products Industry Model. 

 

Figure 7 CUSUM and CUSUMQ for Fertilizers Industry Model. 

 

Figure 8 CUSUM and CUSUMQ for Food, Beverages Tobacco Industry Model. 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

36 

 

Figure 9 CUSUM and CUSUMQ for Iron Steel Products Industry Model. 

 

Figure 10 CUSUM and CUSUMQ for Leather Products Industry Model. 

 

 

Figure 11 CUSUM and CUSUMQ for Nonmetallic Mineral Products Industry 

Model. 



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37 

 

Figure 12 CUSUM and CUSUMQ for Paper Board Industry Model. 

 

Figure 13 CUSUM and CUSUMQ for Pharmaceuticals Industry Model. 

 

 

Figure 14 CUSUM and CUSUMQ for Rubber Products Industry Model. 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

38 

 

Figure 15 CUSUM and CUSUMQ for textile industry model. 

 

Figure 16 CUSUM and CUSUMQ for KSE100 model. 

7. Conclusion:  

The growth of sectoral production and electricity price fluctuation is investigated in the 

context of Pakistan. The single sector investigation is insufficient to provide full knowledge 

about the economy. The impact of electricity prices on sectoral production is more beneficial 

than the aggregate level of production. The study utilized the multifactor ARDL approach to 

investigate the long-term connection between cost of electricity and growth in sectoral 

production. The results found all the sectors have a long-term negative relationship with 

electricity price shocks. However, two of the sectors have an insignificant short-term 

relationship with electricity prices. The electricity prices affect all the economic sectors both on 

supply and demand side. The electricity price shocks increase the production cost and decrease 

production of goods supply. The increase in electricity prices reduces the income level and 

reduce the overall demand for consumption of goods produced. The coke, petroleum products 

and electronics industries are most negatively affected by electricity price shocks. The coke, 

petroleum industry produces the alternative sources of energies in Pakistan and mostly the 

electricity generated is dependent on fossil fuels and the price relationship go parallel (Rehman 

& Deyuan, 2018). Source of energy is the main driver behind every industry and if production 



Journal of Applied Economics and Business Studies, Volume. 5, Issue 1 (2021) 17-46      https://doi.org/10.34260/jaebs.512   

39 

hampers consequently the whole economy is disturbed. The electronics industry production is 

affected due to demand driven link with higher electricity prices. If the electricity prices go up 

the demand of heavy-duty machinery and household equipment goes down which will lead to 

decrease in the production of this industry (Yasmeen et al., 2019). The production of 

pharmaceutical industry, chemical industry, textile industry and leather industry have decreased 

due to higher cost and unavailability of power supply (M. S. Ali et al., 2017). The agriculture 

sector of production has decreased due to increase in irrigation cost that ultimately affect the 

production of fertilizer industries (Shahbaz, 2015). The findings are consistent with previous 

study as the KSE100 index has a significant negative relationship with electricity price shocks 

(S. Sarwar et al., 2018). In Pakistan mostly electricity is generated by using furnace oil but due 

to increase in the cost of oil leads to increase in the cost of electricity which is utilized by 

machineries to produce food items and non-food item rubber, automobile parts, non-metallic 

and paper products industries. It enforces the industries to cutoff on production (M. N. Sarwar 

et al., 2020). Various factors are investigated in relationship with the production of cement and 

steel industries in Pakistan. Increase in the electricity price is weighted 70% in relationship with 

cement and steel production (AHMAD et al., 2018). The results of the study match the current 

state of Pakistan’s economy. It is inferred that additional to oil price the electricity price is 

another barrier to the industrial production growth and higher returns on equity. 

The study proposed different policy implications for government of Pakistan. The results 

revealed the upsurge in electricity prices disturb the growth of sectoral production which also 

have a negative impact on equity market return. The study suggests a moderate monetary policy 

to overcome the negative impact of high electricity price. Monetary policy should be neither 

higher expansionary (leading to inflationary situation) nor higher contractionary (causing 

growth reduction). The solution is temporary for the short-term in case the electricity prices are 

persistently high. In long-term the involvement of the government is increased.  

The following strategies may be taken by the government to overcome the negative impact 

of electricity price on sectoral production and equity market growth. Save energy programs may 

be initiated by the government to bring awareness in the general public on how to reduce energy 

wastage and the government should provide incentives to the general public who successfully 

manage to reduce electricity wastages. (Hille et al., 2019). Private sectors should be encouraged 

to invest in renewable energy projects and government should offering tax reliefs to boost the 

usages of biofuels and renewable energies. To minimize electricity losses government should 

upgrade electricity transmission lines and power grids. Strict custom duties shall be applied on 

the import of heavy electronic appliances and the import duties on solar cells and other 

equipment shall be reduces.  

Further, the government should use a mix kind of energy policy with greater weightage to 

renewable energies. Government should invest in exploring new reservoirs of crude oil and 

natural gas. It will reduce the cost of oil import and the outflow of foreign currency for 

generating electricity (Hanif, 2020). Consequently, increasing the supply of energy to the 

industries and protects from electricity price shocks (Simon Nicholas & Buckley, 2018). It is 

proposed that government should increase investment in electricity generation projects to 

provide lower cost electricity input to the industries. Considering the findings, the study 

provides some suggestions to policy maker to control negative impact of electricity price shocks 

on sectoral production. The study is limited to electricity prices which can be further extended 



Abbas Khan, Muhammad Yar Khan and Abdul Qayyum Khan 

40 

in relationship with other major energy prices i.e., crude oil and natural gas. A cross-sectional 

study may be encouraged to identify the generalized impact of energy prices on macroeconomic 

variable in developed and developing countries with advanced renewable energy and shale gas 

production like U.S. 

 
  



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41 

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