Inflation in Pakistan: evidence from ARDL bounds ...

Int. J. Management Development, Vol. 1, No. 3, 2016

Inflation in Pakistan: evidence from ARDL bounds testing approach Muhammad Shahid Hassan Department of Economics, School of Business and Economics, University of Management and Technology, Lahore, Pakistan Email: [email protected]

Faridul Islam* Department of Economics, Morgan State University, Baltimore, MD 20251-0001, USA Fax: 443-885-8731 Email: [email protected] *Corresponding author

Muhammad Ijaz Department of Finance, School of Business and Economics, University of Management and Technology, Lahore, Pakistan Email: [email protected] Abstract: This paper applies the ARDL bounds testing approach to cointegration to explore a long- and short-run relationship among export per capita; indirect taxes per capita; external debt per capita; exchange rate; crude oil prices and inflation in Pakistan over the period of 1976-2011. The ADF and PP unit root tests are applied to examine the stationarity properties of each series. We find that the series are cointegrated. The impacts of exports, exchange rate and crude oil prices on inflation are found to be positive and highly significant; but that of indirect taxes is positive, significant at the 10% level only. The impact of external debt is negative but not statistically significant. Finally, based on CUSUM and CUSUM square graphs, we confirm that the estimates are structurally stable. The findings shed new insight for the policymakers in controlling inflation in Pakistan. Keywords: exports; indirect taxes; exchange rate; crude oil price; inflation; Pakistan. Reference to this paper should be made as follows: Hassan, M.S., Islam, F. and Ijaz, M. (2016) ‘Inflation in Pakistan: evidence from ARDL bounds testing approach’, Int. J. Management Development, Vol. 1, No. 3, pp.181–195.

Copyright © 2016 Inderscience Enterprises Ltd.

181

182

M.S. Hassan et al. Biographical notes: Muhammad Shahid Hassan is an Assistant Professor of Economics, School of Business and Economics, University of Management and Technology, Lahore, Pakistan. He is doing PhD in Economics from National College of Business Administration and Economics, Lahore, Pakistan. He has published in national/international journals, e.g., Economic Modelling; Social Indicators Research; Hacienda Pública Española/Review of Public Economics; Transylvanian Review of Administrative Sciences; World Applied Science Journal and Middle-East Journal of Scientific Research. He has conducted workshop on Applied Econometric Techniques. Faridul Islam is a Professor and Chair of the Department of Economics Morgan State University, Baltimore, MD. He earned his MS degree from the London School of Economics and the PhD degree from the University of Illinois-Urbana. He has published in Economics Letters, Journal of Economic Education, Journal of Asian Econ., Economic Modelling, The International Trade Journal, Economic Change and Restructuring, International Journal of Social Economics, Indian Economic Review, Journal of Economic Development, Journal of Developing Areas, Journal of Business Econ. and Management, Bangladesh Dev. Studies, Econ. Bulletin, Review of Applied Econ, inter alia. Muhammad Ijaz is an MS scholar in the Department of Finance, School of Business and Economics, University of Management and Technology, Lahore, Pakistan. He has taken courses in the domain of finance including Global Financial Management; Islamic Finance and Corporate Governance; Inferential Statistics for Finance and Financial Econometrics, inter alia. He serves as Teacher Assistant to help students in the areas economics/finance/theoretical and quantitative courses. At present he is working on his final dissertation.

1

Introduction

The objective of the paper is to explore a long- and short-run relationship among export per capita; indirect taxes per capita; external debt per capita; exchange rate; crude oil prices and inflation in Pakistan. For this, we apply the ARDL bounds testing approach to cointegration covering the period, 1976–2011. The Augmented Dickey-Fuller and the Phillip-Perron unit root tests have been applied to examine the stationarity properties of each series. Inflation refers to persistently rising prices of goods and services in an economy. If inflation remains stubbornly high, it can seriously impair welfare by eroding the value of domestic currency – loss of purchasing power. In the same vein, people consume more and save less. Also, inflation worsens income inequality, and aggravates poverty, an unwelcome challenge to a government (Shahbaz et al., 2010). Given the significant relevance of the topic in the current context of Pakistan, a good understanding of the determinants of inflation can be helpful for policymakers. The specification of the model appears reasonable in capturing the underlying economic characteristic of the nation, and the methodology is appropriate for the purpose owing to the small sample size. The authors are not aware of a similar study. This paper fills a gap in knowledge and thereby can be seen as a contribution to the extant literature. Inflation raises prices which leads to higher wages and thus cost of production, and again further increase in prices of goods, a condition known as wage-price spiral. Rising

Inflation in Pakistan

183

cost is a major source of competitive disadvantage in a global world. The general perception is: inflation can adversely affect macroeconomic indicators. However, low inflation around 2% acts as tonic for an economy. This in part explains why central banks across the world set this magic number as a major macroeconomic policy target. Low inflation is comparable with grease for the wheels that keeps the economy running on a sustainable growth trajectory. Broadly speaking, from historical perspectives, Pakistan has not been through periods of very high inflation, although there have had episodes when it hit double digit. The annual five years average inflation between 1976 and 2011 has close to 7%, exceeding 11% only twice (Table1). Policymakers have been working very hard to find a way to stabilise the menace. For instance, government has tried to get a grip on its fiscal deficit by using various measures, without much success. These include boosting the supply of agriculture goods in local market in an effort to dampen inflationary pressure. The government also has helped to create a favourable investment climate to promote economic growth; and established different price control committees. While the last item is non-market approach, it aims to put a brake on the tendency to steep rise in prices; and may have been motivated more by politics than by economics. Table 1

Historic trends of inflation in Pakistan

Years

Inflation*

1976–1980

8.727052

1981–1985

7.169496

1986–1990

6.784393

1991–1995

11.19715

1996–2000

7.297321

2001–2005

5.172139

2006–2011

12.54193

Note: *Annual averages growth of CPI. Source: Calculation by the authors using WDI (2013) Figure 1

Trends of inflation

Source: World Development Idicators (CD-ROM, 2013)

184

M.S. Hassan et al.

The rest of the study is organised as follows. Section 2 reviews the extant literature. Data sources and empirical strategy are outlined in Section 3. Section 4 reports and interprets the empirical results. Section 5 concludes with a set of recommendations arising out of the findings.

2

Literature review

The impact of macroeconomic variables on inflation, measured by consumer price index (CPI), is a very well researched topic. Given our interest, the primary focus is on the more recent studies that relate to Pakistan and other economies with similar characteristics. Agha and Khan (2006) investigated the relationship between fiscal imbalance and inflation in Pakistan from 1973 to 2003. Using Johansen cointegration approach for a long run relationship and the VECM for the direction of causality, they find cointegration among the series. They also find that bank borrowings significantly affect inflation in Pakistan. Choudhary and Chaudhry (2007) examine the nexus of exchange rate, output and prices in Pakistan; they found cointegration the series in Pakistan. The findings also show that the impact of favourable exchange rate on output is positive, while that of inflation is negative. Bildirici and Ersin (2007) used the FMOLS and the DOLS approaches to investigate the link between inflation and domestic debt for three groups of economies characterised by varying levels of inflation. The results were mixed. The first group was composed of Mexico, Turkey, and Brazil (high inflation). In this group, cost of domestic debt was found to be high. In the second group, they considered Belgium, Canada, and Japan (low inflation rate) and found the cost of domestic debt to be low. The third group, Portugal, Greece, and Spain had low inflation rate. They found that the cost of domestic debt was low. For nations adopting non-Ricardian fiscal policies, they document that the increases in cost of domestic debt was the main reason behind the economic crisis, not the domestic debt itself. Khan and Gill (2010) examined the determinants of inflation in Pakistan using different proxies for inflation, e.g., CPI, WPI, SPI and GDP deflator over the period 1971 to 2005. They found that interest rate had indirect effect on inflation while exchange rate; budget deficit; imports; price support for wheat, sugarcane and cotton; and money supply directly impacted inflation in Pakistan. Olatunji et al. (2010) used Johansen and Juselius (1990) cointegration approach to explore the determinants of inflation in Nigeria. They found that the lagged CPI, total imports, exchange rate, and government expenditures negatively impacted inflation. They also demonstrate that previous year’s agricultural output, exports, crude oil exports, and interest rate indirectly affected inflation rate. Khan and Saqib (2011) applied the GMM approach to examine the relationship between political instability measures and inflation in Pakistan. They found that the former and poor law and order situation contributed to inflationary pressure. Chaudhry et al. (2011) investigated the link between foreign exchange reserves and inflation in Pakistan from 1960 to 2007 using ARDL bounds testing approach and found long run cointegrating relationship between the series. They also showed that the former had indirect link to the latter. Bashir et al. (2011) used the Johansen and Juselius (1990) approach to cointegration and vector error correction model to explore the impact of money supply, government expenditures, government revenue, exports, and GDP on inflation in Pakistan from 1972 to 2010. They found that variables are co-integrated in


185

the long run. They further showed that the impact of money supply, economic growth, imports and government expenditures for Pakistan was positive and robust; that of the government revenue was negative and significant. They found bidirectional causality, in the short run, between money supply and inflation; and economic growth and inflation. They further showed that inflation was also caused by government expenditures and government revenue, in the short run; and exports and imports were being affected by inflation in the short run. Tharaka and Ichihashi (2011) applied vector autoregressive (VAR) model to test the relationship between budget deficit and inflation in Sri Lanka from 1950 to 2010. They found that the former had significant and positive impact on inflation; and the causality between the two is bidirectional. Chou and Tseng (2011) implemented the ARDL bounds testing approach to data from 1982 to 2010 to examine the impact of oil price volatility on inflation in Taiwan. The empirical results confirmed a long run relationship, and found that increase in the global oil prices causes inflation only in the long run. Ahmad et al. (2012) applied simple OLS for the sample period from 1972 to 2009 in order to investigate the impact of domestic debt on inflation in Pakistan. They ran two separate equations for inflation; in the first one they used money supply, total domestic debt, private investment, exports, and government expenditures as forcing agents of inflation; and in the second one they use budget deficit, interest rate on domestic debt, indirect taxes, and exchange rate as the determinants of inflation. They found that money supply, domestic debt, exports and government expenditures have significant but positive impact on inflation; whereas private investment has negative and significant impact on inflation in Pakistan. Moreover; their results indicate that budget deficit, interest rate on domestic debt and indirect taxes have positive and significant impact on inflation. In the same year 2012, the study of Shaari et al. (2012) investigated causal relationship among oil price shock, exchange rate volatility and inflation for Malaysia. After applying VECM model on the monthly data from 2005 to 2011, the study found bidirectional causality between crude oil prices and inflation, and the findings further showed that unidirectional causality ran from exchange rate to inflation in the long run. After discussing the contribution of Shaari et al. (2012), the study of Hassan et al. (2012) after applying ARDL bounds testing approach and VECM in order to explore causality between the pairs of inflation, trade openness, economic growth, money supply, and unemployment rate from 1976 to 2010 for Pakistan. They found that trade openness and money supply have significant and elevating impact on inflation in short run and long run respectively; whereas, economic growth has positive and significant impact on inflation only in the long run, therefore, it showed that as economy grows it organises higher inflation. Moreover, the results of causality showed feedback relationship between money supply and inflation in the short but unidirectional causality was found from economic growth to inflation in long run. Finally, they also found that in the long run money supply, trade openness, economic growth, and unemployment caused inflation. Besides Hassan et al. (2012); for the same year, the study of Aurangzeb and Haq (2012) was also conducted to examine the nexus of inflation,exchange rate, fiscal deficit, GDP, unemployment, and interest rate in Pakistan. After using simple ordinary least square method on the sample period from 1981 to 2010; the empirical results showed that exchange rate, fiscal deficit, unemployment, and interest rate had positive and significant impact on inflation and economic growth had a negative impact on inflation. Furthermore, the study of Asad et al. (2013) was conducted in the year 2013 in which they applied OLS on the annual data series from 1973 to 2007 in order to examine the

186

M.S. Hassan et al.

impact of real effective exchange rate on inflation in Pakistan. Their findings of the study showed that real effective exchange rate has a positive but insignificant impact on inflation in Pakistan.

3

Data source and definition of data

3.1 Data source The annual time series data of inflation, exports, indirect taxes, exchange rate, external debt and crude oil prices over the period of 1976–2011 have been extracted from the world development indicators (WDI CD – ROM, 2013). Moreover, the data has been further transformed into natural log form.

3.2 Model specification To investigate the impact of exports, indirect taxes, exchange rate, external debt and crude prices on inflation, the empirical model, in log-linear form is specified. For purpose of estimation, we transformed each series into natural logarithm. INFt = α 0 + α1 EX t + α 2TAX t + α 3 ERt + α 4 DEt + α 5OPt + εt

(1)

where INFt refers to CPI, proxy for inflation. Likewise, EXt is for real exports per capita; TAXt for indirect tax per capita; ERt for exchange rate (Pak Rupee/US$); DEt for real external debt per capita; OPt is real oil prices; and εt is error term, assumed to be a white noise process. In this study we propose that export, indirect taxes, exchange rate, external debt, and crude oil prices could be possible sources which could determine inflation in Pakistan. The nation has opened up its economy in response to rising tide of globalisation. The economy is relatively export oriented, with a sizeable import component. To maintain sound growth rate, Pakistan needs to import crude oil, which recorded more than two fold rise between 1986 and 2010 (Appendix B). In addition the nation is borrows heavily from international sources, implying swelling external debt and indebtedness. These features make export, import and exchange rate of significant importance. Indirect taxes on goods and services add to the cost of production. Given that such taxes tend to be entirely shifted on to consumers, its magnitude has major implications for buying behaviour of consumers. The prices rise translates into higher inflation. Pakistan, as importer of capital machinery and other inputs, exchange rate also serves as a conduit for imported inflation. External debt denominated in foreign currency is affected by exchange rate can be an important factor of inflation. External debt is the result of continuous borrowings by the government in order to meet both internal and external gaps. As long as the external debt has been allocated to productive sectors and efficiently handled, we expect improvement in the macroeconomic indicators in the country and conversely. Oil price volatility is a major factor in the determination of inflation (Shaari et al., 2012; Chou and Tseng, 2011). Oil is a critical input in the manufacturing sector in Pakistan. High oil prices raise the import bill, these high oil prices also increase the cost of production and thus jacks up inflation in the country. Before we proceed to explore long run equilibrium relationship among exchange rate; exports, indirect taxes, external debt and crude oil prices volatility and inflation, we need to check for stationarity properties of each of the included series.


187

To do so, we apply the ADF and the PP unit root tests. For a long run relationship among the series we will implement the ARDL bounds testing approach developed by Pesaran et al. (2001). Besides exploring long run cointegrating relationship between inflation and its factors, then we would find long run and short run coefficients using same bounds testing approach. Afterwards; we will apply stability test using CUSUM and CUSUM square graphs in order to explore whether predictors of inflation are structurally stability or instable?

3.3 Estimation procedure We apply unit root tests such as ADF and PP developed by Dickey and Fuller (1981) and, Phillip and Parron (1988) to investigate the integrating properties of the variables. The equations of ADF and PP unit root tests are respectively given below: p

ΔWt = γ + β Wt −1 −

∑ α ΔW

t− j

j

+ ε1t

(2)

j =1

ΔZ t = τ + ψt Z t −1 + ε2t

(3)

In second step, the cointegration between inflation and its determinants is examined by using the ARDL bounds testing approach to cointegration developed by Pesaran et al. (2001). This approach is flexible regarding the order of the variables. The ARDL bounds testing applicable if variables are integrated at I(1) or I(0). This approach is suitable for small sample data. The unrestricted version of the ARDL bounds testing approach is given as following: ⎡ ΔINFt ⎤ ⎡ a10 ⎤ ⎡ a11 ⎢ ΔEX ⎥ ⎢ a ⎥ ⎢ a t ⎢ ⎥ ⎢ 20 ⎥ ⎢ 21 ⎢ΔTAX t ⎥ ⎢ a30 ⎥ ⎢ a31 ⎢ ⎥ = ⎢ ⎥+⎢ ⎢ ΔERt ⎥ ⎢ a40 ⎥ ⎢ a41 ⎢ ΔDEt ⎥ ⎢ a50 ⎥ ⎢ a51 ⎢ ⎥ ⎢ ⎥ ⎢ ⎢⎣ ΔOPt ⎥⎦ ⎢⎣ a60 ⎥⎦ ⎢⎣ a61 ⎡ b11 ⎢b ⎢ 21 ⎢b31 +⎢ ⎢b41 ⎢b51 ⎢ ⎢⎣b61

a12 a22 a32 a42 a52 a62

b12 b22

b13 b23

b14 b24

b15 b25

b32 b42 b52 b62

b33 b43 b53 b63

b34 b44 b54 b64

b35 b45 b55 b65

a13 a23 a33 a43 a53 a63

a14 a24 a34 a44 a54 a64

a15 a25 a35 a45 a55 a65

a16 ⎤ ⎡ INFt −1 ⎤ a26 ⎥⎥ ⎢⎢ EX t −1 ⎥⎥ a36 ⎥ ⎢TAX t −1 ⎥ ⎥×⎢ ⎥ a46 ⎥ ⎢ ERt −1 ⎥ a56 ⎥ ⎢ DEt −1 ⎥ ⎥ ⎢ ⎥ a66 ⎦⎥ ⎣⎢ OPt −1 ⎦⎥

b16 ⎤ ⎡ ΔINFt −i ⎤ ⎡ μ1 ⎤ ⎥ ⎢ ΔEX ⎥ ⎢ μ ⎥ b26 ⎥ t −i ⎢ ⎥ ⎢ 2⎥ b36 ⎥ p ⎢ ΔTAX t −i ⎥ ⎢ μ3 ⎥ ⎥× ⎢ ⎥+⎢ ⎥ b46 ⎥ i = m ⎢ ΔERt −i ⎥ ⎢ μ4 ⎥ ⎢ ΔDEt −i ⎥ ⎢ μ5 ⎥ b56 ⎥ ⎥ ⎢ ⎥ ⎢ ⎥ b66 ⎥⎦ ⎢⎣ ΔOPt −i ⎥⎦ ⎢⎣ μ6 ⎥⎦

(4)

∑

Using the above representation (4) we would estimate the results for long run cointegrating relationship between inflation and its factors; if estimated value of F-test which involves comparing F with the critical bounds [upper critical bound (UCB) and the lower critical bound (LCB) generated by Pesaran et al. (2001) will exceed the UCB, we reject the null hypothesis of no cointegration]. On the other hand, if the F-statistic is found to be less than the LCB, the null hypothesis of no cointegration sustains. However, the decision about cointegration is inconclusive when the computed F-statistic lies

188

M.S. Hassan et al.

between the two bounds. To establish the short run dynamics we use the error correction method (ECM) of selected ARDL model. This will improve the consistency of the VAR estimates (Engle and Granger, 1987; Bannerjee et al., 1998). The ECM equation is given as following: ⎡ ΔINFt ⎤ ⎡ d10 ⎤ ⎡ d11 ⎢ ΔEX ⎥ ⎢ d ⎥ ⎢ d t ⎢ ⎥ ⎢ 20 ⎥ ⎢ 21 ⎢ΔTAX t ⎥ ⎢ d30 ⎥ ⎢ d31 ⎢ ⎥ = ⎢ ⎥+⎢ ⎢ ΔERt ⎥ ⎢ d 40 ⎥ ⎢ d 41 ⎢ ΔDEt ⎥ ⎢ d50 ⎥ ⎢ d51 ⎢ ⎥ ⎢ ⎥ ⎢ ⎢⎣ ΔOPt ⎥⎦ ⎢⎣ d 60 ⎥⎦ ⎢⎣ d 61 ⎡ λ1 ECM t −1 ⎤ ⎡ η1 ⎤ ⎢ λ ECM ⎥ ⎢ η ⎥ t −1 ⎢ 2 ⎥ ⎢ 2⎥ ⎢ λ3 ECM t −1 ⎥ ⎢ η3 ⎥ +⎢ ⎥+⎢ ⎥ ⎢ λ4 ECM t −1 ⎥ ⎢ η4 ⎥ ⎢ λ5 ECM t −1 ⎥ ⎢ η5 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢⎣ λ6 ECM t −1 ⎥⎦ ⎢⎣ η6 ⎥⎦

d12 d 22

d13 d 23

d14 d 24

d15 d 25

d32 d 42 d52

d33 d 43 d53

d34 d 44 d54

d35 d 45 d55

d 62

d 63

d 64

d 65

d16 ⎤ d 26 ⎥⎥ d36 ⎥ p ⎥× d 46 ⎥ i = n d56 ⎥ ⎥ d 66 ⎦⎥

∑

⎡ ΔINFt −i ⎤ ⎢ ΔEX ⎥ t −i ⎢ ⎥ ⎢ ΔTAX t −i ⎥ ⎢ ⎥ ⎢ ΔERt −i ⎥ ⎢ ΔDEt −i ⎥ ⎢ ⎥ ⎣⎢ ΔOPt −i ⎦⎥

(5)

where ∆ refers to the difference operator and ECMt–1 is lagged error term. The statistical significance of lagged error term shows the speed of adjustment from short run towards long run equilibrium path. Negative and significant lagged error term helps to confirm a long run relationship among the variables. Table 1

INFt EXt TAXt ERt DEt OPt

Descriptive statistics Mean

Median

Maximum

Minimum

Std. dev.

Sum

Sum sq. dev.

8.524167 13.86028 19.79222 4.653056 7.499722 18.21222

8.055 14.02 19.585 3.59 2.985 8.82

20.12 17.36 27.32 10.1 49.57 77.27

2.91 9.24 10.89 1.17 0.73 4.94

3.741966 2.386136 4.090974 3.233332 12.44888 18.81455

306.87 498.97 712.52 167.51 269.99 655.64

490.0809 199.2775 585.7624 365.9054 5424.115 12389.56

Table 2

Unit root analysis At level

At first difference

ADF test

PP test

t-statistic

t-statistic

INFt

–1.4856

–2.0834*

EXt

–2.2649

–1.6758

TAXt

–2.1377

–2.0906

∆TAXt

–4.9475***

–5.9708***

ERt

–1.8397

–1.5527

∆ERt

–3.6640***

–3.6795***

DEt

–1.6644

1.3024

∆DEt

–4.2862***

–3.0557**

OPt

1.9884

1.9884

∆OPt

–6.8410***

–6.8410***

Variables

ADF test

PP test

t-statistic

t-statistic

∆INFt

–3.0658**

–7.5627***

∆EXt

–4.7837***

–5.5029***

Variables

Notes: *, ** and ***indicate significant at the 10, 5 and 1% levels respectively.


4

189

Results and discussions

The descriptive statistics are presented in Table 1. The next step is to find the integrating properties of variables. If the variables are stationary at level then these variables have temporary effect on macro economy and vice versa. In doing so, we have applied two unit root tests, the ADF and PP unit root test. The results are reported in Table 2. Results reported in Table 2 suggest that all the series are non-stationary at levels but stationary in first difference. In other words, inflation, exports, indirect tax, exchange rate, external debt and crude oil price series exhibit I(1) properties. In order for us to proceed with ARDL bounds testing, we need to select an appropriate lag order of the variables. Lag length is determined using the Akaike’s information criterion which is superior to Schwartz Bayesian criterion. The results reported in Table 3 indicate that the appropriate lag order is 2. Table 3

Lag length criteria VAR lag order selection criteria

Lags

Log L

LR

FPE

AIC

SIC

HQ

0

–561.6369

NA

12778088

33.39040

33.65976

33.48226

1

–348.5920

338.3653

397.2490

22.97600

24.86151

23.61901

2

–282.8190

81.24911*

85.02097*

21.22464*

24.72630*

22.41881*

Notes: *indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: final prediction error AIC: Akaike information criterion SIC: Schwarz information criterion HQ: Hannan-Quinn information criterion.

After finding the order of integration and optimal lag length of the variables, we would like to find a long run and stable relationship among inflation, exports, indirect taxes, exchange rate, external debt, and crude oil prices using the ARDL bounds testing approach developed by Pesaran et al. (2001). The estimated results are reported in Table4. We note that the computed F-statistic of Table 4 [7.4528 > 4.4271] is greater than UCB at 5% level of significance. This shows that there is a cointegration among inflation, exports, indirect taxes, exchange rate, external debt, and crude oil prices in long run in case of Pakistan. The diagnostic analysis confirms that there is no serial correlation or heteroskedisticity in the model. The error term is normally distributed and there is no evidence of model misspecification for inflation. Furthermore, the stability of CUSUM and CUSUM square has also confirmed that there is not any structural break over time for inflation model. After finding the long run and stable relationship among the variables, we investigate the impact of exports, indirect taxes, exchange rate, external debt and crude oil prices on inflation. The results are reported in Table 5. We find that exports per capita, indirect taxes, exchange rate and crude oil prices have significant and positive impact on inflation in long run. This means that a 1% increase in exports per capita, indirect taxes, exchange rate and crude oil prices will affect inflation by 1.71%, 0.24%, 1.87%, and 0.20% respectively, all else is same. An increase in exports adds to the income share of both employees and employers which encourages them to consume more. The increase in demand leads to higher price feeding into inflation. Higher indirect taxes and oil prices

190

M.S. Hassan et al.

raise the cost of production causing cost push inflation. The positive and significant impact of exports and taxes on inflation is consistent with the findings of Ahmad et al. (2012). The positive and significant impact of crude oil prices on inflation is in line with the findings of Shaari et al. (2012) and Chou and Tseng (2011). Exchange rate acts as a conduit for imported inflation. We find that per capita debt and inflation are inversely related, but the coefficient is statistically insignificant in Pakistan, in the long run. An interpretation of this is that external debt, i.e., foreign borrowing is not finding productive utilisation. Promotion of industrial activities in the right sectors in Pakistan would put a damper on inflation. In summary, exchange rate and exports contribute to inflation whereas exports per capita, per capita indirect taxes, exchange rate and crude oil prices have also significant and positive impact on inflation in long run in Pakistan. The above discussed estimates for ARDL bounds testing analysis and long run coefficients have been reported in Tables 4 and 5 respectively. Table 4

The ARDL bounds testing analysis INFt = f(EXt, TAXt, ERt, DEt, OPt)

Estimated model Optimal lags

(1,1,1,0,1,0)

F-statistics

7.4528**

W-statistics

44.7169** Critical bounds for F-statistics

Significance level

Critical bounds for W-statistics

Lower critical bound

Upper critical bound

Lower critical bound

Upper critical bound

5%

3.0181

4.4271

18.1088

26.5628

10%

2.5255

3.7369

15.1532

22.4216

Diagnostic tests R2

0.7737

Serial correlation

0.0411 [0.839]

Adjusted – R2

0.6954

Functional form

1.4861 [0.223]

F-statistics D-H statistic

9.8767

Normality

0.1524[0.927]

0.2584 [0.796]

Heteroscedasticity

0.1471[0.701]

Stable

CUSUM square

Stable

CUSUM

Notes: *, ** and ***demonstrate significance level at 10%; 5% and 1% respectively. [ ] represents probability values. Table 5

Estimated long run coefficients using the ARDL approach Dependant variable: INFt

Variable

Coefficient

EXt

1.7050***

TAXt

0.2438*

ERt

1.8671***

0.2680

6.9665

[0.000]

DEt

–0.0070

0.0766

–0.0912

[0.928]

0.2010***

0.0513

3.9198

[0.001]

–32.7920***

5.6839

–5.7693

[0.000]

OPt C

Std. error

t-statistic

Prob. value

0.3414

4.9936

[0.000]

0.1343

1.8154

[0.081]

Note: * and *** represent significance at 10% and 1% levels of respectively.


191

Afterwards, the short run results reported in Table 6 show that exports have positive impact, and is statistically significant. The impact of indirect taxes is negative but statistically insignificant. Exchange rate aggravates inflation significantly. External debt and crude oil prices also contribute to inflation. A 1% increase in exports, exchange rate, external debt and crude oil prices leads to an increase in inflation by 1.02%, 1.93%, 0.58%, and 0.21% respectively in the short run. The lagged error term has negative sign and it is statistically significant at the 1% level. The estimate of lagged error term shows that short run deviations will be corrected by 103.13% toward long run equilibrium path. This also confirms that our established long run relationship between inflation and its determinants is stable and robust. The estimated results for short run coefficients have been presented in Table 6. Table 6

Error correction representation for the selected ARDL model Dependant variable: ∆LINFt

Variable

Coefficient

∆EXt ∆TAXt ∆ERt ∆DEt ∆OPt ECMt–1

t-statistic

Prob. value

1.0222**

0.3981

2.5679

[0.016]

–0.2786 1.9255*** 0.5754*** 0.2072*** –1.0313***

0.1950 0.3558 0.1270 0.0460 0.1262

–1.4289 5.4114 4.5346 4.5075 –8.1699

[0.164] [0.000] [0.000] [0.000] [0.000]

R-square S.E. of regression Mean of dependent variable Residual sum of squares Akaike information criterion DW – Statistic

Std. error

0.8061

R-bar-squared

0.7390

2.0653 –0.2483 110.9071 –81.3349 1.9438

F-statistic. Prob. (F-statistic) S.D. of dependent variable Equation log-likelihood Schwarz Bayesian criterion

18.0180*** [0.000] 4.0428 –71.3349 –89.2525

Notes: The asterisk, *** and **represent significance at 1% and 5% levels respectively. Figure 2

Stability test for inflation, (a) CUSUM (b) CUSUM-SQUARE (see online version for colours) Plot of Cumulative Sum of Recursive Residuals 20

10

0

-10

-20 19 76

19 85

19 94

20 03

20 11

T he straig ht lines re pre sent critical b ound s at 5% sig nificance leve l

(a)

192

M.S. Hassan et al.

Figure 2

Stability test for inflation, (a) CUSUM (b) CUSUM-SQUARE (continued) (see online version for colours) Plot of Cumulative Sum of Squares of Recursive Residuals 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 1976

1985

1994

2003

2011

T he straight lines represent critical bounds at 5% significance level

(b)

We examined parameter stability using the CUSUM and CUSUM square graphs. The results reported in Figure 2 show that the respective graphs lie within the5% critical bands, suggesting that both the long- and the short run estimates are structurally stable over the sample period in Pakistan.

5

Conclusions and policy implications

This paper explores a long run relationship among exports, indirect taxes, exchange rate, external debt, crude oil prices and inflation in Pakistan using data from 1976 to 2011. The workhorse for the purpose is the ARDL bounds testing approach to cointegration. ADF and PP unit root test to test show that the series are I(1). To check for parameters stability in the long- and the short run, we applied the CUSUM and CUSUMsq tests. We find cointegration among the series: and exports, indirect taxes, external debt, exchange rate and crude oil prices each have a positive impact on inflation. The findings are consistent with Ahmad et al. (2012), Shaari et al. (2012) and Chou and Tseng (2011), Aurangzeb and Haq (2012), Khan and Gill (2010). We also find the parameters to be stable, as confirmed by CUSUM and CUSUMsq tests. We find that the impact of export on inflation is positive and significant suggesting that increased exports exacerbate the supply-demand gap which triggers inflation. To address this Pakistan might consider focusing more on meeting domestic demand; while at the same time use resources judiciously to boost production; export them to earn the much needed foreign currency. Investment should be geared to sectors that meet the efficiency criteria. In the short run, this will alleviate inflationary pressure and ensure smooth supply of goods and services to meet the growing demand. The net effect would be a much subdued and manageable inflation. We find that exchange rate is a significant determinant of inflation in Pakistan. So, government will need a more comprehensive trade policy in an effort to stabilise exchange rate which can go a long way towards achieving the goals of price stability. Last but not least, our analysis points to the role of


193

oil price volatility on inflation in Pakistan. To reduce dependence on imported oil, and still pursue economic growth objective, encouraging alternative energy sources as an option should be actively considered. At present, hydrocarbon is the major ingredient in producing electricity. Striking a balance among energy use pattern, alleviation of poverty through sustained economic growth and at the same time addressing environmental concerns will be both tricky and challenging; and yet they must be done for a sustainable growth trajectory in the long run.

References Agha, A.I. and Khan, M.S. (2006) ‘An empirical analysis of fiscal imbalances and inflation in Pakistan’, SBP Research Bulletin, Vol. 2, No. 2, pp.343–362. Ahmad, M.J., Sheikh, M.R. and Tariq, K. (2012) ‘Domestic debt and inflationary effects: an evidence from Pakistan’, International Journal of Humanities and Social Science, Vol. 2, No. 18, pp.256–263. Asad, I., Ahmad, N. and Hussain, Z. (2013) ‘Impact of real effective exchange rate on inflation in Pakistan’, Asian Economic and Financial Review, Vol. 2, No. 8, pp.983–990. Aurangzeb and Haq, A.U. (2012) ‘Determinants of inflation in Pakistan’, Universal Journal of Management and Social Sciences, Vol. 2, No. 4, pp.89–96. Bannerjee, A., Dolado, J. and Mestre, R. (1998) ‘Error-correction mechanism tests for co-integration in single equation framework’, Time Series Analysis, Vol. 19, No. 3, pp.267–283. Bashir, F., Nawaz, S., Yasin, K., Khursheed, U., Khan, J. and Qureshi, M.J. (2011) ‘Determinants of Inflation in Pakistan: an econometric analysis using Johansen co-integration approach’, Australian Journal of Business and Management Research, Vol. 1, No. 5, pp.71–82. Bildirici, M. and Ersin, O.O. (2007) ‘Domestic debt, inflation and economic crises: a panel cointegration application to emerging and developed economies’, Applied Econometrics and International Development, Vol. 7, No. 1, pp.31–47. Chaudhry, M.I., Akhtar, M.H. and Mahmood, K. (2011) ‘Foreign exchange reserves and inflation in Pakistan: evidence from ARDL modelling approach’, International Journal of Economics and Finance, Vol. 3, No. 1, pp.69–76. Chou, K.W. and Tseng, Y.H. (2011) ‘Pass-through of oil prices to CPI inflation in Taiwan’, International Research Journal of Finance and Economics, Vol. 6, No. 9, pp.73–83. Choudhary, M.A.S. and Chaudhry, M.A. (2007) ‘Effects of the exchange rate on output and price level: evidence from the Pakistani economy’, The Lahore Journal of Economics, Vol. 12, No. 1, pp.49–77. Dickey, D.A. and Fuller, W.A. (1981) ‘Likelihood ratio statistics for autoregressive time series with a unit root’, Econometrica, Vol. 49, No. 4, pp.1057–1072. Engel, R.F. and Granger, C.W. (1987) ‘Cointegration and error correction: representation, estimation and testing’, Econometrica, Vol. 55, No. 2, pp.251–276. Hassan, M.S., Ahmad, I. and Mahmood, H. (2012) ‘Does growth led inflation & locus critique exit in Pakistan? A time series analysis’, World Applied Sciences Journal, Vol. 20, No. 7, pp.917–926. Johansen, S. and Juselius, K. (1990) ‘Maximum likelihood estimation and inference in cointegration – with application to the demand for money’, Oxford Bulletin of Economics and Statistics, Vol. 52, No. 2, pp.169–210. Khan, R. and Gill, A.R. (2010) ‘Determinants of inflation: a case of Pakistan (1970–2007)’, Journal of Economics, Vol. 1, No. 1, pp.45–51. Khan, S.U. and Saqib, O.F. (2011) ‘Political instability and inflation in Pakistan’, Journal of Asian Economics, Vol. 22, No. 6, pp.540–549.

194

M.S. Hassan et al.

Olatunji, G.B., Omotesho, O.A., Ayinde, O.E. and Ayindo, K. (2010) ‘Determinants of inflation in Nigeria: a co-integration approach’, 3rd Conference of African Association of Agricultural Economists, pp.1–12. Pesaran, M.H., Shin, Y. and Smith, R.J. (2001) ‘Bounds testing approaches to the analysis of level of relationship’, Journal of Applied Econometrics, Vol. 16, No. 3, pp.289–326. Phillips, P. and Perron (1988) ‘Testing for a unit root in time series regression’, Biomatrika, Vol. 75, No. 2, pp.335–346. Shaari, M.S., Hussain, N.E. and Abdullah, H. (2012) ‘The effects of oil price shocks and exchange rate volatility on inflation: evidence from Malaysia’, International Business Research, Vol. 5, No. 9, pp.106–112. Shahbaz, M., Wahid, A.N.M. and Kalim, R. (2010) ‘Is inflation regressive or progressive? Long run and short run evidence from Pakistan’, International Journal of Management Studies, Vol. 17, No. 2, pp.47–72. Tharaka, N.D. and Ichihashi, M. (2011) How does the budget deficit affect inflation in Sri Lanka, pp.739–8529, IDEC Discussion Paper 2012, Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi Hiroshima, Hiroshima Japan. World Bank (2013) World Development Indicators (WDI) Online Database, The World Bank, Washington D.C. USA.

Appendix A Figure 2

Histogram chart (see online version for colours)

Histogram of Residuals and the Normal Density 0.4

0.3

0.2

0.1

0.0 -8

-7

-6

-5

-4

-3

-2

-1

0

1

2

Sample from 1976 to 2011

3

4

5

6

7

8


195

Appendix B Table A1

Daily import of crude oil by Pakistan: 1986–2010

Year

Imports

Percentage change

Year

Imports

Percentage change

1986

74.24

NA

1999

89.52

10.16%

1987

74.5

0.35%

2000

140.34

56.76%

1988

76.11

2.16%

2001

141.5

0.83%

1989

75

–1.46%

2002

147.86

4.49%

1990

69.33

–7.55%

2003

161.7

9.36%

1991

82.23

18.60%

2004

165.09

2.10%

1992

81.31

–1.12%

2005

171.12

3.65%

1993

80.04

–1.57%

2006

167.12

–2.34%

1994

83.91

4.84%

2007

169

1.12%

1995

77.36

–7.81%

2008

177

4.73%

1996

84.39

9.09%

2009

151.16

–14.60%

2010

151.16

0.00%

1997

91.68

8.64%

1998

81.26

–11.37%

Source: United States Energy Information Administration