Energy Consumption and Economic Growth

7 downloads 2028 Views 249KB Size Report
Apr 8, 2015 - Northern Cyprus, International Journal of Green Energy, DOI: ... should be independently verified with primary sources of information.
This article was downloaded by: [COMSATS Headquarters] On: 09 June 2015, At: 23:21 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Green Energy Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ljge20

Immigration and Electricity Consumption: The Case of Northern Cyprus a

Mete Feridun & Muhammad Shahbaz

b

a

Department of International Business University of Greenwich Business School University of Greenwich, London, UK b

School of Business and Economics GIFT University Gujranwala, Pakistan Accepted author version posted online: 08 Apr 2015.

Click for updates To cite this article: Mete Feridun & Muhammad Shahbaz (2015): Immigration and Electricity Consumption: The Case of Northern Cyprus, International Journal of Green Energy, DOI: 10.1080/15435075.2014.912654 To link to this article: http://dx.doi.org/10.1080/15435075.2014.912654

Disclaimer: This is a version of an unedited manuscript that has been accepted for publication. As a service to authors and researchers we are providing this version of the accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proof will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to this version also.

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

ACCEPTED MANUSCRIPT Immigration and Electricity Consumption: The Case of Northern Cyprus Mete Feridun Department of International Business, University of Greenwich Business School, University of Greenwich, London, UK

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

E-mail: [email protected] Muhammad Shahbaz School of Business and Economics, GIFT University, Gujranwala, Pakistan Email: [email protected] Abstract: The present article uses the Autoregressive Distributed Lag (ARDL) bounds testing procedure to identify the impact of immigration and economic growth on electricity consumption in the case of North Cyprus using annual data from 1977 to 2007. The results suggest that both economic growth and immigration are in a long run equilibrium relationship with electricity consumption. Key words: Electricity consumption, immigration, North Cyprus, causality JEL codes: F15, B28

I. Introduction In the last decade, the issue of uncontrolled influx of immigrants has become one of the biggest public policy issues in North Cyprus. According to the results of the General Population and Housing Unit Census dated 2007, the population is around 270,000. However, it is widely

ACCEPTED MANUSCRIPT 1

ACCEPTED MANUSCRIPT observed that the population and the demographic structure in North Cyprus has increased drastically in the last decade due to both legal and illegal immigration1 . As a result, immigration and immigration

policy remains a major public issue in North Cyprus and the social and

economic implications, including the implications in terms of electricity consumption, have been receiving increasing attention2 .

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

As Ozerdem and Biricik (2011) explain, the existing electricity production system in North Cyprus already suffers from several major problems ranging from frequent voltage fluctuation to electricity losses in power system up to around 20% of the total generation. As the authors point out, the total electricity generation capacity of the North Cyprus electricity authority (KIB-TEK) is 346,3 MW, and it is fully dependent on oil and

petroleum products. Currently, electricity is

generated by four power stations throughout the country, namely Teknecik (Steam Turbine, Gas Turbine, Diesel Generator); Dikmen (Gas Turbine); Kalecik (Diesel Generator) and Guzelyurt (Photovoltaic Plant) and no external electricity is purchased from overseas (See Ozerdem and Bildirici, 2011). Against this background, the purpose of this article is to analyze the long run equilibrium relationship between immigration on electricity consumption in North Cyprus. Although the relationship between economics and electricity consumption has been studied extensively in the existing literature (see, for example, Kraft and Kraft 1978;

Akarca and Long 1980; Erol and

1

See, Feridun (1998) for a detailed historical analysis of the population and the demographic structure in North Cyprus. 2

See Hatay (2005) for further discussion on the immigration issue in North Cyprus.

ACCEPTED MANUSCRIPT 2

ACCEPTED MANUSCRIPT Yu

1987;

Stern 1993; Asafu-Adjaye 2000; Narayan and Smyth 2007; Reynolds and

Kolodzieji 2008; Wolde-Rufael 2009; Apergis and Payne 2009a,b; and Bowden and Payne 2009), the relationship between immigration and electricity consumption has not been studied in the existing literature even. From a theoretical standpoint, the direction of the causal relationship between electricity

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

consumption and immigration is not very straightforward. There are two opposing views on this issue. First view states that as the population increases through immigration, the need to consume more energy increases. On the other hand, increased use of energy may lead to more efficient production and, hence, to faster growth, which attracts more immigrants. Both of these views, however, cannot be generalized for all the countries and for all types of energy usage. This, indeed, warrants further research in this area. In this respect, an investigation of the impact of economic growth and immigration on electricity consumption in the case of North Cyprus may be of interest to both policy makers and practitioners. The present article uses the Autoregressive Distributed Lag (ARDL) bounds testing procedure to identify the long run equilibrium relationship between electricity consumption and two variables, economic growth and immigration, in the case of North Cyprus using annual data from 1977 to 2007. The rest of the article is structured as follows. Section II reviews the literature. Section III introduces the data and methodology. Section IV presents the empirical results, and Section V points out the conclusions that emerge from the study.

ACCEPTED MANUSCRIPT 3

ACCEPTED MANUSCRIPT II. Literature Review The existing studies in the energy consumption literature include different methodologies and different countries to investigate the impact of immigration on electricity consumption. The early research in this area has focused on bi-variate models. Kraft and Kraft (1978) in their pioneering

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

work have found unidirectional causality from GNP to energy consumption using annual US data. However this result has been criticized by many researchers. Akarca and Long (1980) have tested the same hypotheses by using extra two years of data and found no such relationship. Erol and Yu (1987) have examined the GDP-Energy Consumption relationship for England, France, Italy, Germany, Canada and Japan and found mixed results. They found bi-directional relationship for Japan, unidirectional, from energy consumption to GDP, for Canada, and the opposite direction for Germany. They found no causality for the other two countries. These studies have all used bi-variate models. More recent studies have extended the multivariate models including different variables and have used more sophisticated econometric techniques. Stern (1993) used multivariate framework including capital and labor force into the model of energy consumption and GDP. He found that the causality runs in the opposite direction. Asafu-Adjaye (2000) estimated the causal relationship between energy consumption and income for India, Indonesia, Philippines, and Thailand. He finds, using cointegration and error-correction techniques, unidirectional Granger causality from income to energy for India and and Indonesia, but bi-directional causality for the other two countries. Soytas and Sari (2003) have used Cointegration and Vector Error Correction techniques to assess Granger causality between energy consumption and GDP growth in G7

ACCEPTED MANUSCRIPT 4

ACCEPTED MANUSCRIPT countries and emerging markets. They find different directional causality for different countries. Same authors include carbon dioxide emissions into their framework controlling for gross fixed capital formation and labor and analyze the relationship for Turkey (Soytas and Sari (2009)). They find that carbon emissions Granger cause energy consumption and there is no long run relationship between income and carbon emissions. On the other hand Narayan and Smyth

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

(2007) apply similar techniques to G7 countries and they find that Capital formation and Energy consumption affects real GDP positively in the long run. Wolde-Rufael (2009) applies both Granger and Variance Decomposition (VD) Analysis in a multivariate framework also including labor and capital formation. Granger results indicate that in 17 African countries energy consumption causes growth, but according to VD analysis energy consumption is no more effective than the labor and capital formation. This shows different methods can lead to different results for the same data. Apergis and Payne (2009a,b) uses Panel Cointegration and Error correction models to analyze the relationship between energy consumption and growth using labor force and real gross fixed capital formation in their multivariate framework. They find both short run and long run causality from energy consumption to economic growth for 6 central American countries using 1980-2004 data (Apergis and Payne, 2009a), but no unidirectional long run relationship for 11 Commonwealth of Independent States using 1991-2005 annual data (Apergis and Payne, 2009b). The difference in results could be due to different geographic locations or simply due to small number of observations in the data. Bowden and Payne (2009) uses sectoral primary energy consumption measures to test the same hypotheses for the 1949-2006 U.S. annual data. The relatively long data range and various energy measures give the authors more flexibility in

ACCEPTED MANUSCRIPT 5

ACCEPTED MANUSCRIPT testing the hypotheses. They find no granger causality between GDP and total and transportation Energy Consumption, but find Bi-directional link between real GDP and commercial and residential energy consumption. Finally Reynolds and Kolodzieji (2008) uses bi-variate model and analyzes the relationship between growth and three types of energy including oil, gas and natural gas for Soviet Union. They find that decline in oil production caused the decline in GDP

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

and hence impacted the fall of Former Soviet Union. However, the direction of causality is reversed for the other two types of energy use, namely from GDP to coal and gas consumption.

III. Data and Methodology The time series data used in the present analysis is in annual frequency and spans the period from 1977 to 2007. Data on economic growth and electricity consumption has been obtained from the TRNC State Planning Organization web site http://www.devplan.org. Economic growth (denoted by GNP) is proxied by GNP in Turkish Liras and the electricity consumption (denoted by EC) is proxied by electricity consumption per capita in kilowatt per hour (KWH). GNP has been used as this is the only available economic output indicator calculated by the State Planning Organization. On the other hand, data on immigration, which is defined as the number of foreigners in North Cyprus, has been obtained from the TRNC Ministry of Tourism, Environment and Culture. Immigration is proxied by the number of foreigners staying in North Cyprus at the end of each year, is denoted by IMMIG and is estimated as the difference between annual foreigner arrivals and departures from North Cyprus so that a positive number indicates immigration and a negative number indicates emigration.

ACCEPTED MANUSCRIPT 6

ACCEPTED MANUSCRIPT In order to investigate the existence of a long run equilibrium relationship between EC and GNP, the present analysis uses the Autoregressive Distributed Lag (ARDL) bounds testing procedure introduced by Pesaran at el (2001). The ARDL has several advantages over other techniques of cointegration. For instance, it can be applied irrespective of whether the variable are I(0), I(1) or fractionally cointegrated (Pesaran and Pesaran 1997). Moreover, model takes sufficient number

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

of lags to capture the data generating process in general to specific modeling framework. In addition, the Error Correction Model (ECM) can be derived from ARDL through a simple linear transformation. ECM integrates short run adjustments with long run equilibrium without losing long run information. Last but not least, in the context of the present study where the number of observations is limited, small sample properties of ARDL approach makes it a convenient econometric methodology as its small sample properties are far superior to that of the Johensen and Juselius’s cointegration technique (Pesaran and Shin 1999). The ARDL approach involves two steps for estimating long run relationship (Pesaran et al., 2001). The first step is to investigate the existence of long run relationship among all variables in the equation under estimation. The second step is estimate the long run and short run bi-causal relationship of running actors. We run second step only if we find a long run relationship in the first step (Narayan and Smith, 2005). In the present article, the existence of the long run relationships between EC and IMMIG are based on the following proposition that electricity consumption (EC) is a function of immigration (IMMIG) and economic growth (Y), which can be represented as follows: EC = f(IMMIG, Y)

(1)

ACCEPTED MANUSCRIPT 7

ACCEPTED MANUSCRIPT Within the ARDL bounds testing procedure, this can be shown in the form of the unrestricted error correction model for each variable as follows:

m

m

m

i =1

i =0

i =0

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

∆LEC = α  + α 1t + ∑ α 2 ∆LEC t −i + ∑ α 3 ∆LGNPt −i + ∑ α 4 ∆IMMIG + α 5 LEC t −1 +

α 6 LGNPt −1 + α 7 IMMIGt −1 + η i

(2)

where LEC and LGNP are electricity consumption (EC) and economic growth (GNP) in natural logs, respectively, and t is time trend variable. Immigration (IMMIG) is not represented in logarithms as it contains negative numbers. On the other hand, η is the error term in the model. The first part of the equation with α 2 , α 3 , α 4 represents the short run dynamics of the model whereas the second part with α 5 , α 6 , α 7 represent the long run coefficients. The null hypothesis in the Equation 8 is α 5 = α 6 = α 7 = 0 , which means the non-existence of the long run relationship. The ARDL bounds testing procedure starts with conducting the bounds test for the null hypothesis of no cointegration. The calculated F-statistic is compared with the critical value tabulated by Pesaran et al. (2001). If the F-test statistic exceeds the upper critical value, the null hypothesis of no long run relationship can be rejected regardless of whether the underlying orders of integration of the variables are I(0) or I(1) . Similarly, if the F-test statistic falls below the lower critical value, the null hypothesis is not rejected. However, if the sample F-test statistic falls between these two bounds, the result is inconclusive. When the order of integration of the

ACCEPTED MANUSCRIPT 8

ACCEPTED MANUSCRIPT variables is known and all the variables are I(1), the decision is made based on the upper bounds. Similarly, if all the variables are I(0), then the decision is made based on the lower bounds. The model can be selected using the lag length criteria like Schwartz-Bayesian Criteria (SBC) and Hannan-Quinn (HQ) information criterion.

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

IV. Empirical Results The initial step for ARDL approach to apply is the selection of appropriate lag length. Considering the small sample data set we can not take lag more than 2 on basis of minimum value of AIC and SBC. Literature reveals that the calculation of ARDL F-statistics is quite sensitive to the selection of lag order in the model (see, for instance, Bahmani-Oskooee and Brooks 1999; Bahmani-Oskooee et al 2006 and Bahmani-Oskooee and Harvey 2006). As can be seen in Table 1, several selection criteria have been considered. Based on the results, the appropriate lag length is selected as 1 year. The results of the ARDL bounds tests shown in Table 2, indeed suggest the rejection of the null hypothesis of no long run relationship at the 1% level of statistical significance when EC is treated as the dependent variable and GNP and IMMIG are treated as its long run forcing variable at both 1 and 2 years lag lengths.


ACCEPTED MANUSCRIPT 9

ACCEPTED MANUSCRIPT As can be seen from the table, the estimated F-statistic is greater than the upper bound critical values suggested by both Pesaran et al (2001) and Narayan (2005) at the 1% level in the case where EC is the dependent variable. As a result, it can be concluded that there exists a long run equilibrium relationship between EC, IMMIG and GNP.

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Table 3 and Table 4 present the long run and short run coefficients, respectively. As can be seen from Table 3, both GNP and IMMIG are significant in the long run with a positive sign, suggesting that increased economic growth and immigration leads to an increased level of energy consumption. GNP has a coefficient of 0.308078 is highly significant, which means that economic growth has a positive impact on electricity consumption. Similarly, IMMIG has a coefficient of 0.102939, which is also highly significant, which suggests that immigration has a positive impact on electricity consumption. This lends support to our theoretical expectation that immigration causes higher energy consumption in North Cyprus.
Turning to short run results, we have similar findings. However, the short-run coefficient of IMMIG is not significant. Signs of the coefficients remain the same, suggesting that both variables have a positive impact on electricity consumption in the short run as well. The coefficient of the error correction term EC t-1

is -0.0741, which is negative and significant. This

further confirms the existence of a stable long-run

relationship among the variables. The

ACCEPTED MANUSCRIPT 10

ACCEPTED MANUSCRIPT coefficient implies that deviation from the long-term energy consumption is corrected by 7.4% in the following year. During the period under study, several events have taken place which might have caused structural breaks in the time series data used in the present study. These structural breaks may be associated with a change in government policy regarding immigration, banking crisis of 2000-

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

2001 and so forth. The existence of cointegration does not necessary imply that the estimated coefficients are stable. If the coefficients are unstable the results will be unreliable. In order to test for long-run parameter stability, Pesaran and Pesaran (1997) suggest applying the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of recursive residuals of square (CUSUMSQ) tests proposed by Brown et al (1975) to the residuals of the estimated ECMs to test for parameter constancy. In both CUSUM and CUSUMSQ, the related null hypothesis is that all coefficients are stable. The CUSUM test uses the cumulative sum of recursive residuals based on the first observations and is updated recursively and plotted against break point. The test is more suitable for detecting systematic changes in the regression coefficients. The CUSUMSQ makes use of the squared recursive residuals and follows the same procedure. However, it is more useful in situations where the departure from the constancy of the regression coefficients is haphazard and sudden3 . If the plot of the CUSUM and CUSUMSQ stays within the 5 percent critical bounds the null hypothesis that the coefficients are stable cannot be rejected. If however, either of the parallel lines are crossed then the null hypothesis of parameter stability is rejected at the 5 percent significance level.

3

See Pesaran and Pesaran (1997: p. 117).

ACCEPTED MANUSCRIPT 11

ACCEPTED MANUSCRIPT
Figure 1 and Figure 2 plot the CUSUM and CUSUMSQ tests of the estimated models where the straight lines represent critical bounds at 5% significance level. The plots of the CUSUM and CUSUMSQ statistics are generally confined within the 5 percent critical value bounds, indicating the absence of any instability of the coefficients, thus providing evidence that the

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

parameters of the model do not suffer from any structural instability over the period of the study.

V. Conclusions The present article used the Autoregressive Distributed Lag (ARDL) bounds testing procedure to identify the impact of immigration and economic growth on electricity consumption in the case of North Cyprus using annual data from 1977 to 2007. The results suggest that both economic growth and immigration are in a long run equilibrium relationship with electricity consumption. These findings suggest some very important local policy implications for North Cyprus, which is an isolated economy with almost no alternative energy resources. This study shows that immigration and economic growth will lead to more energy consumption. Although it is estimated that the number of Turkish immigrants in North Cyprus is around 230,000-240,000 as of 2007 4 , as Mehmet et al (2007) argue “the exact figure of foreign workers is unknown due to

4

See http://www.ykp.org.cy/population/kibrisinkuzeyindekinufus.pdf

ACCEPTED MANUSCRIPT 12

ACCEPTED MANUSCRIPT large numbers of illegal workers entering North Cyprus without any passport formality, typically as tourists. 5 Given that the energy resources are limited in a small island economy, an increase in the number of dependents such as children and unemployed housewives means that a more energy resources is needed, which is expected to have important implications in terms of electricity consumption

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

if no counter macroeconomic strategies are devised. Considering the fact that electricity sources are limited and energy prices in North Cyprus is one of the most expensive compared to other European Union nations and it is state owned, it is imperative that the policy makers provide better infrastructure at affordable prices. As Ozerdem and Biricik (2011) point out, the energy problem of North Cyprus can be solved by building a base power station or through a subsea cable connection from Turkey. The present article justifies the need for the recent project undertaken by Turkey’s Electricity Transfer Incorporated Company (TEIAS), which would transfer 200 megawatts of electricity via cables under the sea from Turkey to North Cyprus. .References Akarca, A.T., Long, T.V., 1980. On the relationship between energy and GNP: a reexamination. Journal of Energy and Development 5, 326-331. Apergis, N., and Payne, J.E., 2009a. Energy consumption and economic growth in

5

Mehmet et al (2007: p.55).

ACCEPTED MANUSCRIPT 13

ACCEPTED MANUSCRIPT Central America: Evidence from a panel cointegration and error correction model. Energy Economics 31, 211-216. Apergis, N., and Payne, J.E., 2009b. Energy consumption and economic growth: Evidence from the Commonwealth of Independent States. Energy Economics 31, 641-647.

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Asafu-ADjaye, J., 2000. The Relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries.Energy Economics 22, 615-625. Bowden, N., Payne, J.E. 2009. The causal relationship between US energy consumption and real output: A disaggregated analysis. Journal of Policy Modeling 31, 180188. Erol, U., Yu, ESH, 1987. On the causal relationship between eenrgy and income for industrialized countries. Journal of Energy and Development 13, 113-122. Groenewold, N. and Tang, S. H. K. (2007), “Killing the Goose that Lays the Golden Egg: Institutional Change and Economic Growth in Hong Kong”, Economic Inquiry, OnlineEarly Articles. Published article online: 22-Feb-2007 Kraft, J., Kraft, A., 1978. On the relationship between energy and GNP. Journal of Energy Development 3, 401-403.

ACCEPTED MANUSCRIPT 14

ACCEPTED MANUSCRIPT Lin,H;

Lung-Tai Yeh, Shih-Chien Chien, Bivariate Cointegration Between Energy Consumption

and Development Factors: A Case Study of Pakistan, International Journal of Green Energy Vol. 10, Iss. 7, 2013 2. Narayan, P.K., Smyth, R., 2005. Electricity consumption, employment and real income in Australia: evidence from multivariate Granger causality tests. Energy Policy 33,

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

1109-1116. Ozerdem, O. C. and Biricik, S. (2011) Overview of Energy System and major Power Quality Problems in North Cyprus, International Journal on “Technical and Physical Problems of Engineering”, 3(3), 71-75 Pesaran and Shin (1999) Pesaran, M.H. and Y. Shin, An autoregressive distributed lag modelling approach to cointegration analysis. In: S. Strom, Editor, Econometrics and Economic Theory in 20th Century: The Ragnar Frisch Centennial Symposium,

Cambridge University Press,

Cambridge (1999) Chapter 11. Pesaran, M.H., Shin, Y. and Smith, R.J. (2001). “Bounds Testing Approaches to the Analysis of Level Relationships”, Journal of Applied Econometrics, Vol 16, 289–326. Reynolds, D.B., Kolodziej, M. 2008. Former Soviet Union oil production and GDP decline: Granger causality and the multi-cycle Hubbert curve. Energy Economics 30, 271-289. Soytas, U., Sari, R., 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Economics 25, 33-37.

ACCEPTED MANUSCRIPT 15

ACCEPTED MANUSCRIPT Soytas, U., Sari, R., 2009. Energy Consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member. Ecological Economics 68, 1667-1675. Stern, D.I., 1993. Energy use and economic growth in the USA: a multivariate approach.

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Energy Economics 15, 137-150. Wolde-Rufael, Y., 2004. Energy Consumption and economic growth: The Experience of African countries revisited. Energy Economics 31, 217-224. Zaman, K. Muhammad Mushtaq Khan, Zohra Saleem , International Journal of Green Energy, Vol. 8, Iss. 8, 2011

ACCEPTED MANUSCRIPT 16

ACCEPTED MANUSCRIPT

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Table 1 Lag Order Selection Criteria Lag

LogL

LR

FPE

AIC

SC

HQ

0

-574.5505

NA

1.06e+13

38.50337

38.64349

38.54819

1

-488.0314

6.05e+10*

33.33543*

33.89590*

33.51473*

149.9665*

* indicates lag order selected by the criterion. LR: sequential modified LR test statistic, FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan-Quinn information criterion.

ACCEPTED MANUSCRIPT 17

ACCEPTED MANUSCRIPT

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Table 2 ARDL Bound Testing Procedure for Cointegration Model Estimation

F-Statistic

EC| IMMIG, GNP

14.098

GNP|EC, IMMIG

9.898

IMMIG|EC, GNP

3.513

Critical

Pesaran et al (2001) a

Narayan (2005) b

Values Lower

Upper

Lower

Upper

Bound

Bound

Bound

Bound

1%

8.740

9.630

10.605

11.650

5%

6.560

7.300

7.360

8.265

10 %

5.590

6.260

6.010

6.780

We decided about lag length on the basis of SIC. * indicates significance at 1% level with unrestricted intercept and trend a

Critical values are obtained from Pesaran et al (2001).

ACCEPTED MANUSCRIPT 18

ACCEPTED MANUSCRIPT Critical Values are obtained from Narayan (2005)

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

b

ACCEPTED MANUSCRIPT 19

ACCEPTED MANUSCRIPT

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Table 3 Long Run Results Coefficient

t-values

p - values

Constant

0.23340

3.02370

0.0056

EC t-1

0.861242

18.93981

0.0000

GNP

0.308078

2.176046

0.0388

IMMIG

0.102939

2.100132

0.0456

R2 = 0.9674, Adjusted R2 = 0.9636, Akaike criterion = -1.0171, Schwarz criterion = -0.8302 Durbin-Watson = 2.100, F-statistic = 257.275 (0.0548)

ACCEPTED MANUSCRIPT 20

ACCEPTED MANUSCRIPT Table 4 Short Run Results

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

Dependent Variable: ΔEC

Variable

Coefficient

Std. Error

t-values

p - values

Constant

0.0497

0.0200

2.4814

0.0199

ΔGNP

0.8251

0.2267

3.6391

0.0012

ΔIMMIG

0.0489

0.0569

-0.8589

0.3982

EC t-1

-0.0741

0.0311

-2.3816

0.0248

R2 = 0.2887, Adjusted R2 = 0.2066, S.E. of regression = 0.1271, Akaike info criterion = -1.1633, Schwarz criterion = -0.9765, F-statistic = 3.5175, Prob(F-statistic) = 0.0290, Durbin-Watson stat = 2.0559

ACCEPTED MANUSCRIPT 21

ACCEPTED MANUSCRIPT Figure 1. CUSUM Plot 12 8 4

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

0 -4 -8 -12

1992 1994 1996 1998 2000 2002 2004 2006 5% Significance

CUSUM

ACCEPTED MANUSCRIPT 22

ACCEPTED MANUSCRIPT Figure 2. CUSUMSQ Plot 1.6 1.2 0.8

Downloaded by [COMSATS Headquarters] at 23:21 09 June 2015

0.4 0.0 -0.4

1992 1994 1996 1998 2000 2002 2004 2006 CUSUM of Squares

5% Significance

ACCEPTED MANUSCRIPT 23