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Optimum currency area in South Asia: A state space approach
International Review of Economics and Finance 18 (2009) 502–510
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International Review of Economics and Finance j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i r e f
Optimum currency area in South Asia: A state space approach Nilanjan Banik a, Basudeb Biswas b,⁎,1, Keith R. Criddle c,1 a b c
Department of Economics, Institute for Financial Management and Research, Chennai, Tamil Nadu, 600034, India Department of Economics, Utah State University, Logan, Utah, 84322, USA Department of Fishery Management, University of Alaska Fairbanks, Alaska 99775-7220, USA
a r t i c l e
i n f o
Article history: Received 17 September 2007 Received in revised form 11 March 2008 Accepted 15 April 2008 Available online 16 May 2008 JEL classification: C32 F02 F15
a b s t r a c t This paper is an empirical investigation of the feasibility of an optimum currency area (OCA) in South Asia. Countries are good candidates for forming an OCA if their economies are similarly structured and if their economies share similar responses to exogenous shocks. That is, among other characteristics, good candidates for forming an OCA will share a coincident pattern of economic booms and recessions. We use a state space time series model with a stochastic trend to explore the extent to which the Indices of Industrial Production for South Asian nations share common dynamic responses to exogenous shocks. © 2008 Elsevier Inc. All rights reserved.
Keywords: Optimum currency area South Asia State space modeling
1. Introduction Despite ongoing controversy over the hypothetical and empirical merits of regional trade agreements in and of themselves and in relation to global trade liberalization, over the last two decades, regional trade agreements have gained ever increased prominence. Around 200 regional trade agreements, notified under the General Agreement on Tariffs and Trade (GATT) and the World Trade Organization (WTO), are in force today. Rather than attempting to resolve the controversy regarding the merits of regional trade agreements, we have instead chosen to explore whether economic characteristics of the members of one such regional agreement, the South Asian Association of Regional Cooperation (SAARC), predispose the successful formation of an optimum currency area (OCA). SAARC was initiated in 1985 and includes Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka, as members. Starting 1995, SAARC member countries have increasingly focused on greater economic cooperation. During that year, the SAARC Preferential Trading Arrangement (SAPTA) was formed. In a 1999 report (SAARC, 1999), following a call for “greater coordination of monetary and exchange rate policy”, a tentative roadmap suggested goals of forming a South Asian Custom Union (SACU) as early as 2015, followed by a South Asian Economic Union as early as 2020. Some initial steps were taken in this direction with the establishment of SAARCFINANCE, a network of SAARC central bank governors and finance secretaries and its subsequent formal recognition as a SAARC body at the 11th SAARC summit held in Kathmandu, Nepal in 2002. Beginning on 1 January 2006, the South Asian Free Trade Area (SAFTA) came into effect. SAFTA strengthens the relationships defined under SAPTA and is envisaged
⁎ Corresponding author. Tel.: +1 435 797 2304; fax: +1 435 797 2701. E-mail address: [email protected] (B. Biswas). 1059-0560/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2008.04.004
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as the next step towards formation of the SACU; a step to be followed by additional steps towards formation of a common market, and eventually an economic union. This paper begins with an examination of whether the economic characteristics of SAARC member nations predispose SAARC to form a successful OCA. We then employ a state space time series modeling technique to explore the extent to which historical data suggest the existence of common dynamic factors in the economic behavior of SAARC members. The reason for using state space modeling approach is because vector autoregression (VAR) method as employed by Bayoumi and Eichengreen (1993) and Blanchard and Quah (1989) suffer from the weakness of being dependent upon some ad hoc assumptions, which are employed to identify the structural shocks: supply shocks are expected to affect output in the long-run and demand side shocks have no long-run effect on output. Similarly, the correlation approach employed by Fatás (1997) in trying to ascertain whether a particular shock is symmetric or asymmetric is also naïve in the sense that it assumes that if two economies evolve in the same way, this necessarily means that they are suffering from same type of shock. The state space modeling approach not only overcome the drawbacks present in the previous methodologies but also allow us to decompose shocks into their structural common and specific components. Because the quarterly time series of Index of Industrial Production (IIP) data are not available for all SAARC member countries, we have limited our empirical analyses to a subset of SAARC members: Bangladesh, India and Pakistan. However, because output from these three countries accounts for more than 96% of the SAARC output, models that successfully account for common latent dynamic processes for these three nations can be expected to account for the most significant common latent dynamic processes for the whole region. Our empirical analyses employ a multivariate time series modeling technique based on the principles of linear systems theory (Aoki, 1987). The concept of an OCA was originally developed by Mundell (1961) and extended by McKinnon (1963). A currency area is an area in which exchange rates are fixed or which has a common currency. A currency area becomes optimal when the economic efficiency (measured in terms of benefits less costs) for forming it is maximized. Therefore, much of the debate on the desirability of forming an OCA deals with identification of the characteristics of member nations that would make monetary union more (or less) beneficial. The four categories of factors that have been identified as key determinants of whether members will benefit from joining a monetary union are: (1) the extent of trade, (2) the symmetry of economic activity, (3) country characteristics, and (4) labor mobility. 1.1. Criterion 1: Extent of trade The more the member countries trade among themselves, the more they will value bilateral exchange rate stability. When trading partners are on a floating exchange rate, uncertainty about future exchange rate movements can adversely affect trade in goods, services, and capital. While forward exchange rate markets can reduce the risk of exchange rate uncertainty, such markets are not well developed in South Asia. In contrast, when a group of nations form a monetary union like the European Union (EU), exchange rate uncertainty is eliminated. Empirical studies by Kwan (1994) and Shinji (1996) have reported evidence that suggests that some East Asian nations, who engage in more trade with Japan than with the United States, have pegged their currencies against a basket of currencies that gives more weight to the Japanese yen than to the American dollar, thereby reducing the level of exchange rate uncertainty vis-à-vis the yen, the currency of their leading trade partner. 1.2. Criterion 2: Symmetry of economic activity One objective of traditional macroeconomic policy is to maintain internal balance, i.e., keep the economy near full employment and inflation near zero. Expansionary monetary policies are needed to accomplish these goals during a recession, while contractionary monetary policies are prescribed as a response to heightened inflation. Because members of monetary unions lose their ability to adopt independent monetary policies, countries that share dynamic economic trends are better candidates for forming a successful monetary union than countries with dissimilar economic dynamics. This also means that similar economies can better afford to share a common exchange rate, an important consideration because countries within a monetary union can no longer use exchange rates as an instrument for relative price adjustments. 1.3. Criterion 3: Country characteristics The abundant body of literature that has explored the role of economic characteristics of member nations within OCAs concludes that economies that are similar in size, openness, and development, and are geographical proximate, are more likely to benefit from forming an OCA. McKinnon (1963) points out that the more open the economy, the more it will be inclined to use fixed exchange rates, while flexible exchange rates are more advantageous for fairly closed economies. Mills and Holmes (1999), Lumsdaine and Prasad (2002), and Backus and Kehoe (1992) observed that countries with similar economic profiles are better candidates for forming an OCA. Ghosh and Wolf (1994) explore the welfare loss differences between optimal contiguous and optimal non-contiguous economic unions and find that when contiguous groupings do not share similar dynamic economic processes, the welfare losses associated with sharing a common stabilization policy may outweigh the benefits of adopting a common currency. Artis, Kohler and Melitz (1998) use two criteria, a high level of bilateral trade and symmetry of shocks, to identify four large hypothetical OCAs: one that would include virtually all of western Europe, a second that would encompass all of Mesoamerica and the northern ridge of South America, a third that would occupy most of the Middle East, and a fourth that would encircle the entire ASEAN area, including China and Australia.
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1.4. Criterion 4: Labor mobility and wage flexibility The main cost of a currency union is related to the loss of an independent monetary policy for member countries to smooth-out business cycles. If wages are rigid and if labor mobility is limited, countries that form an OCA will have more difficulty adjusting to demand shifts than countries that have maintained their own national currency and that can devalue (or revalue) their currency. Hence, one important condition for the feasibility of an OCA is that countries affected by asymmetric shocks need substantial flexibility in their labor markets. A high degree of labor mobility may serve as a channel through which adjustment to shocks can occur. Blanchard and Katz (1992) present empirical evidence that labor mobility in the United States has played an important role in adjustment, substituting to a large extent for price flexibility. In the balance of this paper, we will examine similarity in economic structure, trade flow, and labor mobility, as criteria of forming an OCA in South Asia. Our empirical analyses will be based on structural output time series variables for Bangladesh, India and Pakistan and will explore the extent to which these time series share common dynamics in their response to external shocks. By structural time series models we mean models in which the observations are made up of trend, seasonal, cycle and regression components plus random variation. Countries are more likely to form a successful OCA if their economic systems respond similarly over time to external shocks. Section 2 examines the economic structure, trade flows, and labor mobility of South Asian economies. Section 3 introduces the analytic methods that we use to explore the extent to which South Asian economies can be characterized by similarities in their dynamic response to external economic shocks. Section 4 has results and analysis. Conclusions are presented in the last section. 2. Economic characteristics of SAARC nations When compared in terms of their economic structure, namely, savings as a percentage of GDP, demographic profile and labor mobility, SAARC nations have some similarities. The industrial sector constitutes roughly one fourth of GDP in all countries, while the share of agriculture varies from 20.11% in Sri Lanka to 40.75% in Nepal. Although a majority of the populations of each nation live in rural areas, all seven nations are becoming increasingly urbanized. Except for Maldives, saving as a proportion of GDP is also similar across SAARC members. These countries also share a similar demographic profile: in all these nations, age 65 and above is a small percentage of the population (varying between 3.32% in Bangladesh and 6.50% in Sri Lanka) (Table 1). Because these nations share similar demographics with a youthful population, they are not likely to face an aging population in the near future that could put pressure on common fiscal resources. When countries share similar economic characteristics, there are fewer pressures to transfer funds from richer countries to poorer ones and greater harmony in following common fiscal and monetary policies (Rose & Engel, 2000). Although there is a paucity of official data on labor mobility among SAARC member countries, there are many indications that labor mobility is high. There are no legal constraints to labor mobility between India and Nepal or between India and Bhutan. Bangladesh shares a porous border with India that results in a substantial but mostly illegal flow of labor from Bangladesh to India. In addition, following recent reinvigoration of the peace initiative process between India and Pakistan, labor flows increased between these two nations as well. Similarities in economic structure have made integration more politically acceptable. Although intra-regional trade flows in SAARC are low—accounting for less than 5% of the region's overall foreign trade—it is rising. The upward trend in trade is likely to continue as the SAARC economies open up. Presently, because of restrictions on legitimate trade, there exists a considerable amount of extra-legal trade. For example, Taneja (2004) estimates that the magnitudes of legal and extra-legal trade between Bangladesh and India are roughly the same, while extra-legal trade is estimated to be nearly one third of the value of legal trade between India and Sri Lanka. Estimates of the magnitude of extra-legal trade between India and Pakistan vary from $100 million to $1 billion per year (South Asia Development and Cooperation Report, 2001/2). Hence, even without increasing trade, forming an OCA in SAARC is likely to enhance the trade figures by legitimizing much of the current illegal trade of goods and services. Given this general observation about economic characteristics of SAARC nations, we now examine the extent to which output in Bangladesh, India, and Pakistan can be characterized by common latent dynamics. Countries that share similar dynamics in Table 1 Socioeconomic characteristics of SAARC member nations, 2002. Characteristics
Bangladesh
Bhutan
India
Maldives
Nepal
Pakistan
Sri Lanka
Agriculture, value added (% of GDP) Industry, value added (% of GDP) Fertility rate, total (births per woman) Foreign direct investment (% of GDP) GDP growth (annual %) Mortality rate, infant (per 1000 live births) Population ages 0–14 (% of total) Population ages 15–64 (% of total) Population ages 65 and up (% of total) Rural population (% of total population) Gross saving (% of GDP)
22.73 26.41 2.95 0.10 4.42 48.00 36.20 60.42 3.32 76.09 18.37
33.87 37.38 5.10 0.05 7.69 74.00 42.54 53.00 4.11 91.74 NA
22.67 26.60 2.92 0.59 4.59 65.00 32.69 62.08 5.03 71.91 22.45
NA NA 4.00 1.83 5.61 58.00 40.07 55.75 3.83 71.62 46.11
40.75 21.49 4.15 0.18 − 0.61 62.00 40.31 55.69 3.78 85.45 11.79
23.21 23.30 4.50 1.39 2.85 76.00 40.61 56.03 3.32 66.22 14.35
20.11 26.31 2.10 1.46 3.95 16.00 25.48 67.59 6.50 78.92 14.33
Source: World Bank (2004).
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response to external shocks are good candidates for forming an OCA. We will consider IIP as a proxy for output. Changes in the level of output over time are due to permanent and transitory disturbances. There is a general consensus among macroeconomists that the transitory part of the output (also known as cycle) is of a temporary nature and is caused by demand shocks. The trend part of the output (also known as permanent component) is explained by supply shocks and is thought to be of a more enduring nature. 3. Methodology Time series procedures are predicated on the assumption that the observations are generated by a stationary stochastic process. If the data are not stationary, the nonstationarity must be addressed. The appropriate treatment for nonstationarity depends on whether the nonstationarity is caused by a deterministic or stochastic process. The state space time series approach that we employ was first introduced in Aoki (1987) and can be easily adjusted to address deterministic or stochastic nonstationarities. For example, Criddle and Havenner (1991) use a deterministic trend model to represent the observable and latent components of the dynamics of naturally occurring populations subject to capture by commercial fishermen. In contrast, Cerchi and Havenner (1988) use a stochastic trend specification to model stock prices. Whether the cause of nonstationarity is deterministic or stochastic, it is essential that the parameters of the trend process be jointly estimated with the parameters of the time series model. Rather than devise a structural model to account for the nonstationarities evident in the time series of IIP and rather than imposing the strong assumptions associated with differencing each series independently, we have adopted the modeling framework introduced in Cerchi and Havenner (1988). The state space-stochastic trend model can be represented by a system of matrix equations: IIPt = cτ tjt − 1 + ωt τt
+ 1jt
ð1Þ
= aτtjt − 1 + bωt
ð2Þ
ωt = Cztjt − 1 + et: zt
+ 1jt
ð3Þ
= Aztjt − 1 + Bet;
ð4Þ
In each time period, there is a single observation for each of m stochastic time series in IIP. Because even nonlinear stationary time series have linear state space representations (Aoki, 1987), the low frequency dynamic factors that are important in IIP can be modeled using innovation form Eqs. (1) and (2). Eq. (1) uses the matrix c, to map a linear combination of the m elements of IIPt into an n1 element vector of latent state variables, τt. Although the state variables are unobservable ex ante, they can be determined after the model parameters have been estimated. The state variables in Eqs. (1) and (2) are constructed to be minimum sufficient statistics for the past history of the low frequency dynamic relationships that characterize IIP. The vector ωt includes the high frequency dynamics and contemporaneous correlations present in IIP. Eq. (2) describes the low frequency dynamic relationships in state space. Because the number of states, n1, is to be determined and because it is always possible to define new states equal to the lag of other states, the state equation can be written as a first order equation. The matrix a relates the state variables to each other through time, while the matrix b incorporates errors on the m series in IIP into the forecasts of the n1 states in a manner analogous to a Kalman filter. Because the optimal states can be shown to be a linear combination of the observed series, Eqs. ((1) and (2) can be written with the same error vector. Eqs. (3) and (4) represent high frequency dynamics in terms of n2 additional state variables, zt, formed from a linear combination of the residuals to Eqs. ((1) and (2). Again, because number of states, n2, is to be determined, Eq. (4) can be augmented to first order. The vector et is serially uncorrelated, but perhaps contemporaneously correlated. Once the numbers of state variables, n1 and n2, have been determined, the coefficients a, A, b, B, c, C, and covariance matrices can be estimated and the values of the state vectors can be determined. The number of states, n1 and n2, can be less than, equal to, or greater than the number of series modeled depending on the degree to which the series are correlated and on the complexity of the underlying low frequency and high frequency dynamics. The low frequency and high frequency models can be combined for forecasting: τ IIPt = ðc CÞ t = t − 1 + et zt = t − 1
τt = t − 1 zt = t − 1
=
a bC 0 A
τt = t − 1 zt = t − 1
ð5Þ +
b e B t
ð6Þ
Specification of the state space model consists of determining the minimum number of states needed to reflect the latent dynamic processes. Aoki (1987) demonstrates that the minimum number of states is equal to the rank of the matrix of autocorrelations among the modeled time series. If the autocorrelations were observed exactly, the autocorrelation matrix would be characterized by n non-zero singular values. Since the autocorrelations are sample estimates they contain sampling error, as do the singular values of the sample autocorrelation matrix they make up. Following Aoki and Havenner (1991) we selected the number of singular values to be included in the model such that the ratio of the first excluded singular value to largest singular value was on the order of the square root of the number of observations, a criterion analogous to the condition number.
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Fig. 1. Decay trajectories of the six latent state variables in response to an initial shock.
Comprehensive developments and proofs of the solution to this system of matrix equations are developed in Aoki (1987) and summarized and extended in Aoki and Havenner (1991). 4. Results and analysis The data used for analysis consisted of 88 quarterly observations (1980 Q1—2001 Q4) of the IIP for Bangladesh, India and Pakistan. The other four SAARC nations were omitted from the analysis because IIP data were not available for the relevant time period. The data were obtained from International Financial Statistics Yearbook (various issues). Model specification and coefficient estimation were based on the 80 quarterly observations from 1980 Q1 to 1999 Q4. The last eight observations from each time series were reserved for model validation. Eqs. (1) and (2) were specified with a system lag parameter of 1 to capture low frequency dynamics.2 Based on the ratio of singular values, we determined that Eqs. (1) and (2) could be specified with a single state variable. That is, nonstationarities in the IIP for Bangladesh, India, and Pakistan can be characterized by a single shared latent stochastic trend. Eqs. (3) and (4)were
2
The maximal lag on the individual series is equal to the product of the number of series and the length of the system lag.
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specified with a system lag parameter of 4 to capture high frequency dynamics in the residuals of the trend model. Based on the ratio of singular values of the autocorrelation matrix and on the magnitude of AIC and BIC statistics on the individual series, five state variables were selected to represent the high frequency dynamics. Based on this determination, the coefficient estimates for Eqs. (5) and (6) are: 2
3 2 IIP B −48:667 −9:937 3:602 1:840 6 tI 7 10:591 −3:255 4 IIPt 5 = 4 −72:676 3:885 P −56:117 13:047 21:917 −18:020 IIPt 2
0:933 6 6 0 τt + 1 6 60 zt + 1 6 60 40 0
0:019 0:907 0:182 0:167 0:017 0:078
−0:119 0:134 0:049 −0:966 0:026 0:039
0:015 −0:149 0:978 −0:004 −0:017 −0:060
−0:096 0:006 0:006 0:038 −0:954 0:008
2:503 8:125 4:603
3 4:241 τ 3:908 5 t + et zt 5:013
3 2 −0:061 −0:528 7 6 −2:104 −0:050 7 6 6 0:063 7 7 τt + 6 −0:400 6 −0:943 z 0:077 7 7 t 6 4 −1:080 0:078 5 0:949 2:743
−1:053 2:330 −0:914 −0:789 −2:826 0:135
3 0:054 0:662 7 7 −0:131 7 7 10 − 2 et −0:510 7 7 −0:455 5 0:377
The presence of these shared dynamic linkages suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. The dynamic linkages among the state variables are determined by the elements of the matrix
a bC 0 A
The six eigenvalues of this matrix are: Eigenvalues of A
Moduli
0.9334 7.083′ 10−3 ± 0.987i 0.943 ± 0.057i 0.9539
0.9334 0.9869 0.9443 0.9539
Because the moduli of these roots are less than one, the time series are stationary. The persistence of the latent dynamics is represented in Fig. 1 where the value of the states to one and allowing them to decay according to
τt + 1 zt + 1
=
a 0
bC A
τt zt
The decay paths of the state variables suggest that dynamic adjustments to exogenous shocks to IIP are persistent. These six state variables represent the latent dynamic linkages in deviations from long-term trends in the growth of industrial production shared by Bangladesh, India, and Pakistan. While the decay path represented in the uppermost left figure reflects the common stochastic trend, it is not possible to identify the other five states with particular combinations of the input data.
Table 2 Model performance. Bangladesh
India
Pakistan
df SST SSE R2 %AE cv
62 212,703 8444 0.960 0.51% 6.91%
62 452,006 3766 0.992 0.31% 3.41%
62 349,578 20,325 0.942 0.36% 7.85%
df SST SSE R2 %AE cv
8 139,988 1422 0.990 2.90% 4.80%
8 234,144 1117 0.995 2.81% 2.98%
8 98,649 5749 0.942 − 5.68% 8.38%
Sample
Out-of-sample
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Fig. 2. a. IIP: Bangladesh. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with cross-marks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals. b. IIP: India. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with cross-marks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals. c. IIP: Pakistan. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with crossmarks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals.
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Performance of the state space-stochastic trend model of industrial production for Bangladesh, India, and Pakistan is represented in Table 2. The coefficients of variation of the out-of-sample forecasts are slightly smaller than the coefficients of variation for the sample forecasts for Bangladesh and India, while there is a slight increase in the coefficient of variation for the out-of-sample forecasts for Pakistan. Model performance is represented graphically in Fig 2a, b, and c. From our empirical analysis, results suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. The benefit of similarities of economic factors can be leveraged more if some steps are taken at the policy level. Presently, there are no specific provisions in the SAFTA agreement for adoption of measures of deeper integration, such as: (a) granting of transit facilities through their territories for goods coming from the neighboring countries; (b) cooperation for development of transport and other forms of infrastructure; (c) liberalization of investment and trade in services; (d) transfer of fund from the economically advance region to economically poor regions, to help the laggard regions modernize and diversify their economies; (e) reducing the number of negative lists (India's negative list in the context of SAFTA is larger than that in some of its bilateral free trade agreements, and almost four times as large as its latest offer in the negotiation for a free trade area with Association of Southeast Asian Nations); and lastly, (f) further easing of political differences with respect to India and Pakistan. 5. Conclusion In this paper, we attempted to determine to what extent South Asian countries are ready to form an OCA. In general SAARC nations share similar economic characteristics. This initial observation is supported well by our empirical results. Our empirical results suggest that the SAARC region has many characteristics that would be desirable in an OCA. The benefit of similarities of economic factors can be leveraged more with some additional coordination of policies at the government level. We based our empirical analysis by examining how the key economic variable, namely the outputs of three of the seven SAARC countries; Bangladesh, India and Pakistan, respond to external shocks. We found evidence of a shared latent dynamic linkage in long-term trends in the growth of industrial production for Bangladesh, India, and Pakistan and in the higher frequency dynamics about that trend. The presence of these shared dynamic linkages suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. This means that an economic boom (recession) in one of these nations is likely to reverberate throughout the region. Such co-movements of outputs may be due to dependence on common factors, such as geographical proximity and similarity of trade composition. South Asian nations are geographically close and their economies share a similar industry profile. When countries share a similar industry profile and are located closely, then demand shocks in one country may affect other countries in the region. This could also arise if these economies all share a common trade linkage with major export markets. For example, to the extent that Bangladesh, India and Pakistan all engage in trading similar products with the EU, changes in the EU's economic performance would have a similar effect on all three nations because all the closely-related export sectors would be affected in a similar way. The other reason for the presence of common trend and hence co-movements can be explained through intra-industry trade. As far as the trade structure is representative of the output structure, the cycles should become more synchronized because they would be affected by common shocks. This is the argument made by Frankel and Rose (1998): that intra-industry trade increases synchronicity of output. Because we find evidence of latent dynamic linkages in deviations from the long-term stochastic trend in the growth of industrial production for Bangladesh, India, and Pakistan, and because these economies are similar in composition, enjoy considerable labor mobility, and engage in substantial amounts of legal and extra-legal trade, we conclude that they are good candidates for forming an OCA. Acknowledgements We would like to thank three anonymous referees for some valuable comments, especially, on the empirical part of the paper. Research supports from Utah Agricultural Experiment Station under Project No. UTA 00091 is gratefully acknowledged. References Aoki, M. (1987). State space modeling of time series. New York: Springer-Verlag. Aoki, M., & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10, 1−59. Artis, M., Kohler, M., & Melitz, J. (1998). Trade and the number of OCA's in the world. European University Institute Working Paper No. 98/16 (Florence, Italy). Backus, D. K., & Kehoe, P. J. (1992). International evidence on the historical properties of business cycles. American Economic Review, 82, 864−888. Bayoumi, T., & Eichengreen, B. (1993). Shocking aspects of European Monetary Unification. In F. Giavazzi, & F. Torres (Eds.), Adjustment and growth in the European Monetary Union (pp. 193−229). Cambridge: Cambridge University Press. Blanchard, O., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655−673. Blanchard, O. J., & Katz, L. F. (1992). Regional evolutions, Brookings papers on economic activity. Washington D.C.: Brookings Institute. Cerchi, M., & Havenner, A. (1988). Cointegration and stock prices: The random walk on Wall Street revisited. Journal of Economic Dynamics and Control, 12, 333−346. Criddle, K. R., & Havenner, A. (1991). An encompassing approach to modeling fishery dynamics. Natural Resource Modeling, 5, 55−90. Fatás, A. (1997). EMU: countries or regions? Lessons from the EMS experience. European Economic Review, 41, 743−751. Frankel, J. A., & Rose, A. K. (1998). The endogeneity of the optimum currency area criteria. Economic Journal, 108, 1009−1025. Ghosh, A., & Wolf, H. C. (1994). How many monies? A genetic approach to finding optimum currency areas. National Bureau of Economic Research Working Paper No. 4805 (New York). International Financial Statistics Yearbook, International Monetary Fund, Various Issues. Kwan, C. H. (1994). Economic interdependence in the Asia-Pacific region. London: Routledge.
510
N. Banik et al. / International Review of Economics and Finance 18 (2009) 502–510
Lumsdaine, R. L., & Prasad, E. S. (2002). Identifying the common component of international economic fluctuations: A new approach. IZA Discussion Paper No. 487 (IZA, Bonn). McKinnon, R. I. (1963). Optimum currency area. American Economic Review, 53, 717−725. Mills, T. C., & Holmes, M. J. (1999). Common trends and cycles in European industrial production: Exchange rate regimes and economic convergence. The Manchester School, 67, 557−587. Mundell, R. (1961). A theory of optimum currency areas. American Economic Review, 51, 657−665. Rose, A., & Engel, C. (2000). Currency unions and international integration. NBER Working Paper No. 7872 Cambridge: National Bureau of Economic Research. Shinji, T. (1996). The yen and its East Asian neighbors, 1980–95: Cooperation or competition? NBER Working Paper No. 5720 Cambridge: National Bureau of Economic Research. South Asian Association for Regional Cooperation (1999). SAARC vision beyond the year 2000: Report of the SAARC group of eminent persons established by the Ninth SAARC Summit. New Delhi: Shipra Publications. South Asia Development and Cooperation Report, 2001–02, (New Delhi: Research and Information System for the Non-Aligned and Other Developing Countries). Taneja, N. (2004, December). Informal trade in SAARC region. Economic and Political Weekly, 5367−5371. World Bank, 2004, World development indicators, available at http://publications.worldbank.org/WDI accessed January 2005.
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International Review of Economics and Finance j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / i r e f
Optimum currency area in South Asia: A state space approach Nilanjan Banik a, Basudeb Biswas b,⁎,1, Keith R. Criddle c,1 a b c
Department of Economics, Institute for Financial Management and Research, Chennai, Tamil Nadu, 600034, India Department of Economics, Utah State University, Logan, Utah, 84322, USA Department of Fishery Management, University of Alaska Fairbanks, Alaska 99775-7220, USA
a r t i c l e
i n f o
Article history: Received 17 September 2007 Received in revised form 11 March 2008 Accepted 15 April 2008 Available online 16 May 2008 JEL classification: C32 F02 F15
a b s t r a c t This paper is an empirical investigation of the feasibility of an optimum currency area (OCA) in South Asia. Countries are good candidates for forming an OCA if their economies are similarly structured and if their economies share similar responses to exogenous shocks. That is, among other characteristics, good candidates for forming an OCA will share a coincident pattern of economic booms and recessions. We use a state space time series model with a stochastic trend to explore the extent to which the Indices of Industrial Production for South Asian nations share common dynamic responses to exogenous shocks. © 2008 Elsevier Inc. All rights reserved.
Keywords: Optimum currency area South Asia State space modeling
1. Introduction Despite ongoing controversy over the hypothetical and empirical merits of regional trade agreements in and of themselves and in relation to global trade liberalization, over the last two decades, regional trade agreements have gained ever increased prominence. Around 200 regional trade agreements, notified under the General Agreement on Tariffs and Trade (GATT) and the World Trade Organization (WTO), are in force today. Rather than attempting to resolve the controversy regarding the merits of regional trade agreements, we have instead chosen to explore whether economic characteristics of the members of one such regional agreement, the South Asian Association of Regional Cooperation (SAARC), predispose the successful formation of an optimum currency area (OCA). SAARC was initiated in 1985 and includes Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka, as members. Starting 1995, SAARC member countries have increasingly focused on greater economic cooperation. During that year, the SAARC Preferential Trading Arrangement (SAPTA) was formed. In a 1999 report (SAARC, 1999), following a call for “greater coordination of monetary and exchange rate policy”, a tentative roadmap suggested goals of forming a South Asian Custom Union (SACU) as early as 2015, followed by a South Asian Economic Union as early as 2020. Some initial steps were taken in this direction with the establishment of SAARCFINANCE, a network of SAARC central bank governors and finance secretaries and its subsequent formal recognition as a SAARC body at the 11th SAARC summit held in Kathmandu, Nepal in 2002. Beginning on 1 January 2006, the South Asian Free Trade Area (SAFTA) came into effect. SAFTA strengthens the relationships defined under SAPTA and is envisaged
⁎ Corresponding author. Tel.: +1 435 797 2304; fax: +1 435 797 2701. E-mail address: [email protected] (B. Biswas). 1059-0560/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.iref.2008.04.004
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as the next step towards formation of the SACU; a step to be followed by additional steps towards formation of a common market, and eventually an economic union. This paper begins with an examination of whether the economic characteristics of SAARC member nations predispose SAARC to form a successful OCA. We then employ a state space time series modeling technique to explore the extent to which historical data suggest the existence of common dynamic factors in the economic behavior of SAARC members. The reason for using state space modeling approach is because vector autoregression (VAR) method as employed by Bayoumi and Eichengreen (1993) and Blanchard and Quah (1989) suffer from the weakness of being dependent upon some ad hoc assumptions, which are employed to identify the structural shocks: supply shocks are expected to affect output in the long-run and demand side shocks have no long-run effect on output. Similarly, the correlation approach employed by Fatás (1997) in trying to ascertain whether a particular shock is symmetric or asymmetric is also naïve in the sense that it assumes that if two economies evolve in the same way, this necessarily means that they are suffering from same type of shock. The state space modeling approach not only overcome the drawbacks present in the previous methodologies but also allow us to decompose shocks into their structural common and specific components. Because the quarterly time series of Index of Industrial Production (IIP) data are not available for all SAARC member countries, we have limited our empirical analyses to a subset of SAARC members: Bangladesh, India and Pakistan. However, because output from these three countries accounts for more than 96% of the SAARC output, models that successfully account for common latent dynamic processes for these three nations can be expected to account for the most significant common latent dynamic processes for the whole region. Our empirical analyses employ a multivariate time series modeling technique based on the principles of linear systems theory (Aoki, 1987). The concept of an OCA was originally developed by Mundell (1961) and extended by McKinnon (1963). A currency area is an area in which exchange rates are fixed or which has a common currency. A currency area becomes optimal when the economic efficiency (measured in terms of benefits less costs) for forming it is maximized. Therefore, much of the debate on the desirability of forming an OCA deals with identification of the characteristics of member nations that would make monetary union more (or less) beneficial. The four categories of factors that have been identified as key determinants of whether members will benefit from joining a monetary union are: (1) the extent of trade, (2) the symmetry of economic activity, (3) country characteristics, and (4) labor mobility. 1.1. Criterion 1: Extent of trade The more the member countries trade among themselves, the more they will value bilateral exchange rate stability. When trading partners are on a floating exchange rate, uncertainty about future exchange rate movements can adversely affect trade in goods, services, and capital. While forward exchange rate markets can reduce the risk of exchange rate uncertainty, such markets are not well developed in South Asia. In contrast, when a group of nations form a monetary union like the European Union (EU), exchange rate uncertainty is eliminated. Empirical studies by Kwan (1994) and Shinji (1996) have reported evidence that suggests that some East Asian nations, who engage in more trade with Japan than with the United States, have pegged their currencies against a basket of currencies that gives more weight to the Japanese yen than to the American dollar, thereby reducing the level of exchange rate uncertainty vis-à-vis the yen, the currency of their leading trade partner. 1.2. Criterion 2: Symmetry of economic activity One objective of traditional macroeconomic policy is to maintain internal balance, i.e., keep the economy near full employment and inflation near zero. Expansionary monetary policies are needed to accomplish these goals during a recession, while contractionary monetary policies are prescribed as a response to heightened inflation. Because members of monetary unions lose their ability to adopt independent monetary policies, countries that share dynamic economic trends are better candidates for forming a successful monetary union than countries with dissimilar economic dynamics. This also means that similar economies can better afford to share a common exchange rate, an important consideration because countries within a monetary union can no longer use exchange rates as an instrument for relative price adjustments. 1.3. Criterion 3: Country characteristics The abundant body of literature that has explored the role of economic characteristics of member nations within OCAs concludes that economies that are similar in size, openness, and development, and are geographical proximate, are more likely to benefit from forming an OCA. McKinnon (1963) points out that the more open the economy, the more it will be inclined to use fixed exchange rates, while flexible exchange rates are more advantageous for fairly closed economies. Mills and Holmes (1999), Lumsdaine and Prasad (2002), and Backus and Kehoe (1992) observed that countries with similar economic profiles are better candidates for forming an OCA. Ghosh and Wolf (1994) explore the welfare loss differences between optimal contiguous and optimal non-contiguous economic unions and find that when contiguous groupings do not share similar dynamic economic processes, the welfare losses associated with sharing a common stabilization policy may outweigh the benefits of adopting a common currency. Artis, Kohler and Melitz (1998) use two criteria, a high level of bilateral trade and symmetry of shocks, to identify four large hypothetical OCAs: one that would include virtually all of western Europe, a second that would encompass all of Mesoamerica and the northern ridge of South America, a third that would occupy most of the Middle East, and a fourth that would encircle the entire ASEAN area, including China and Australia.
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1.4. Criterion 4: Labor mobility and wage flexibility The main cost of a currency union is related to the loss of an independent monetary policy for member countries to smooth-out business cycles. If wages are rigid and if labor mobility is limited, countries that form an OCA will have more difficulty adjusting to demand shifts than countries that have maintained their own national currency and that can devalue (or revalue) their currency. Hence, one important condition for the feasibility of an OCA is that countries affected by asymmetric shocks need substantial flexibility in their labor markets. A high degree of labor mobility may serve as a channel through which adjustment to shocks can occur. Blanchard and Katz (1992) present empirical evidence that labor mobility in the United States has played an important role in adjustment, substituting to a large extent for price flexibility. In the balance of this paper, we will examine similarity in economic structure, trade flow, and labor mobility, as criteria of forming an OCA in South Asia. Our empirical analyses will be based on structural output time series variables for Bangladesh, India and Pakistan and will explore the extent to which these time series share common dynamics in their response to external shocks. By structural time series models we mean models in which the observations are made up of trend, seasonal, cycle and regression components plus random variation. Countries are more likely to form a successful OCA if their economic systems respond similarly over time to external shocks. Section 2 examines the economic structure, trade flows, and labor mobility of South Asian economies. Section 3 introduces the analytic methods that we use to explore the extent to which South Asian economies can be characterized by similarities in their dynamic response to external economic shocks. Section 4 has results and analysis. Conclusions are presented in the last section. 2. Economic characteristics of SAARC nations When compared in terms of their economic structure, namely, savings as a percentage of GDP, demographic profile and labor mobility, SAARC nations have some similarities. The industrial sector constitutes roughly one fourth of GDP in all countries, while the share of agriculture varies from 20.11% in Sri Lanka to 40.75% in Nepal. Although a majority of the populations of each nation live in rural areas, all seven nations are becoming increasingly urbanized. Except for Maldives, saving as a proportion of GDP is also similar across SAARC members. These countries also share a similar demographic profile: in all these nations, age 65 and above is a small percentage of the population (varying between 3.32% in Bangladesh and 6.50% in Sri Lanka) (Table 1). Because these nations share similar demographics with a youthful population, they are not likely to face an aging population in the near future that could put pressure on common fiscal resources. When countries share similar economic characteristics, there are fewer pressures to transfer funds from richer countries to poorer ones and greater harmony in following common fiscal and monetary policies (Rose & Engel, 2000). Although there is a paucity of official data on labor mobility among SAARC member countries, there are many indications that labor mobility is high. There are no legal constraints to labor mobility between India and Nepal or between India and Bhutan. Bangladesh shares a porous border with India that results in a substantial but mostly illegal flow of labor from Bangladesh to India. In addition, following recent reinvigoration of the peace initiative process between India and Pakistan, labor flows increased between these two nations as well. Similarities in economic structure have made integration more politically acceptable. Although intra-regional trade flows in SAARC are low—accounting for less than 5% of the region's overall foreign trade—it is rising. The upward trend in trade is likely to continue as the SAARC economies open up. Presently, because of restrictions on legitimate trade, there exists a considerable amount of extra-legal trade. For example, Taneja (2004) estimates that the magnitudes of legal and extra-legal trade between Bangladesh and India are roughly the same, while extra-legal trade is estimated to be nearly one third of the value of legal trade between India and Sri Lanka. Estimates of the magnitude of extra-legal trade between India and Pakistan vary from $100 million to $1 billion per year (South Asia Development and Cooperation Report, 2001/2). Hence, even without increasing trade, forming an OCA in SAARC is likely to enhance the trade figures by legitimizing much of the current illegal trade of goods and services. Given this general observation about economic characteristics of SAARC nations, we now examine the extent to which output in Bangladesh, India, and Pakistan can be characterized by common latent dynamics. Countries that share similar dynamics in Table 1 Socioeconomic characteristics of SAARC member nations, 2002. Characteristics
Bangladesh
Bhutan
India
Maldives
Nepal
Pakistan
Sri Lanka
Agriculture, value added (% of GDP) Industry, value added (% of GDP) Fertility rate, total (births per woman) Foreign direct investment (% of GDP) GDP growth (annual %) Mortality rate, infant (per 1000 live births) Population ages 0–14 (% of total) Population ages 15–64 (% of total) Population ages 65 and up (% of total) Rural population (% of total population) Gross saving (% of GDP)
22.73 26.41 2.95 0.10 4.42 48.00 36.20 60.42 3.32 76.09 18.37
33.87 37.38 5.10 0.05 7.69 74.00 42.54 53.00 4.11 91.74 NA
22.67 26.60 2.92 0.59 4.59 65.00 32.69 62.08 5.03 71.91 22.45
NA NA 4.00 1.83 5.61 58.00 40.07 55.75 3.83 71.62 46.11
40.75 21.49 4.15 0.18 − 0.61 62.00 40.31 55.69 3.78 85.45 11.79
23.21 23.30 4.50 1.39 2.85 76.00 40.61 56.03 3.32 66.22 14.35
20.11 26.31 2.10 1.46 3.95 16.00 25.48 67.59 6.50 78.92 14.33
Source: World Bank (2004).
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response to external shocks are good candidates for forming an OCA. We will consider IIP as a proxy for output. Changes in the level of output over time are due to permanent and transitory disturbances. There is a general consensus among macroeconomists that the transitory part of the output (also known as cycle) is of a temporary nature and is caused by demand shocks. The trend part of the output (also known as permanent component) is explained by supply shocks and is thought to be of a more enduring nature. 3. Methodology Time series procedures are predicated on the assumption that the observations are generated by a stationary stochastic process. If the data are not stationary, the nonstationarity must be addressed. The appropriate treatment for nonstationarity depends on whether the nonstationarity is caused by a deterministic or stochastic process. The state space time series approach that we employ was first introduced in Aoki (1987) and can be easily adjusted to address deterministic or stochastic nonstationarities. For example, Criddle and Havenner (1991) use a deterministic trend model to represent the observable and latent components of the dynamics of naturally occurring populations subject to capture by commercial fishermen. In contrast, Cerchi and Havenner (1988) use a stochastic trend specification to model stock prices. Whether the cause of nonstationarity is deterministic or stochastic, it is essential that the parameters of the trend process be jointly estimated with the parameters of the time series model. Rather than devise a structural model to account for the nonstationarities evident in the time series of IIP and rather than imposing the strong assumptions associated with differencing each series independently, we have adopted the modeling framework introduced in Cerchi and Havenner (1988). The state space-stochastic trend model can be represented by a system of matrix equations: IIPt = cτ tjt − 1 + ωt τt
+ 1jt
ð1Þ
= aτtjt − 1 + bωt
ð2Þ
ωt = Cztjt − 1 + et: zt
+ 1jt
ð3Þ
= Aztjt − 1 + Bet;
ð4Þ
In each time period, there is a single observation for each of m stochastic time series in IIP. Because even nonlinear stationary time series have linear state space representations (Aoki, 1987), the low frequency dynamic factors that are important in IIP can be modeled using innovation form Eqs. (1) and (2). Eq. (1) uses the matrix c, to map a linear combination of the m elements of IIPt into an n1 element vector of latent state variables, τt. Although the state variables are unobservable ex ante, they can be determined after the model parameters have been estimated. The state variables in Eqs. (1) and (2) are constructed to be minimum sufficient statistics for the past history of the low frequency dynamic relationships that characterize IIP. The vector ωt includes the high frequency dynamics and contemporaneous correlations present in IIP. Eq. (2) describes the low frequency dynamic relationships in state space. Because the number of states, n1, is to be determined and because it is always possible to define new states equal to the lag of other states, the state equation can be written as a first order equation. The matrix a relates the state variables to each other through time, while the matrix b incorporates errors on the m series in IIP into the forecasts of the n1 states in a manner analogous to a Kalman filter. Because the optimal states can be shown to be a linear combination of the observed series, Eqs. ((1) and (2) can be written with the same error vector. Eqs. (3) and (4) represent high frequency dynamics in terms of n2 additional state variables, zt, formed from a linear combination of the residuals to Eqs. ((1) and (2). Again, because number of states, n2, is to be determined, Eq. (4) can be augmented to first order. The vector et is serially uncorrelated, but perhaps contemporaneously correlated. Once the numbers of state variables, n1 and n2, have been determined, the coefficients a, A, b, B, c, C, and covariance matrices can be estimated and the values of the state vectors can be determined. The number of states, n1 and n2, can be less than, equal to, or greater than the number of series modeled depending on the degree to which the series are correlated and on the complexity of the underlying low frequency and high frequency dynamics. The low frequency and high frequency models can be combined for forecasting: τ IIPt = ðc CÞ t = t − 1 + et zt = t − 1
τt = t − 1 zt = t − 1
=
a bC 0 A
τt = t − 1 zt = t − 1
ð5Þ +
b e B t
ð6Þ
Specification of the state space model consists of determining the minimum number of states needed to reflect the latent dynamic processes. Aoki (1987) demonstrates that the minimum number of states is equal to the rank of the matrix of autocorrelations among the modeled time series. If the autocorrelations were observed exactly, the autocorrelation matrix would be characterized by n non-zero singular values. Since the autocorrelations are sample estimates they contain sampling error, as do the singular values of the sample autocorrelation matrix they make up. Following Aoki and Havenner (1991) we selected the number of singular values to be included in the model such that the ratio of the first excluded singular value to largest singular value was on the order of the square root of the number of observations, a criterion analogous to the condition number.
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Fig. 1. Decay trajectories of the six latent state variables in response to an initial shock.
Comprehensive developments and proofs of the solution to this system of matrix equations are developed in Aoki (1987) and summarized and extended in Aoki and Havenner (1991). 4. Results and analysis The data used for analysis consisted of 88 quarterly observations (1980 Q1—2001 Q4) of the IIP for Bangladesh, India and Pakistan. The other four SAARC nations were omitted from the analysis because IIP data were not available for the relevant time period. The data were obtained from International Financial Statistics Yearbook (various issues). Model specification and coefficient estimation were based on the 80 quarterly observations from 1980 Q1 to 1999 Q4. The last eight observations from each time series were reserved for model validation. Eqs. (1) and (2) were specified with a system lag parameter of 1 to capture low frequency dynamics.2 Based on the ratio of singular values, we determined that Eqs. (1) and (2) could be specified with a single state variable. That is, nonstationarities in the IIP for Bangladesh, India, and Pakistan can be characterized by a single shared latent stochastic trend. Eqs. (3) and (4)were
2
The maximal lag on the individual series is equal to the product of the number of series and the length of the system lag.
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specified with a system lag parameter of 4 to capture high frequency dynamics in the residuals of the trend model. Based on the ratio of singular values of the autocorrelation matrix and on the magnitude of AIC and BIC statistics on the individual series, five state variables were selected to represent the high frequency dynamics. Based on this determination, the coefficient estimates for Eqs. (5) and (6) are: 2
3 2 IIP B −48:667 −9:937 3:602 1:840 6 tI 7 10:591 −3:255 4 IIPt 5 = 4 −72:676 3:885 P −56:117 13:047 21:917 −18:020 IIPt 2
0:933 6 6 0 τt + 1 6 60 zt + 1 6 60 40 0
0:019 0:907 0:182 0:167 0:017 0:078
−0:119 0:134 0:049 −0:966 0:026 0:039
0:015 −0:149 0:978 −0:004 −0:017 −0:060
−0:096 0:006 0:006 0:038 −0:954 0:008
2:503 8:125 4:603
3 4:241 τ 3:908 5 t + et zt 5:013
3 2 −0:061 −0:528 7 6 −2:104 −0:050 7 6 6 0:063 7 7 τt + 6 −0:400 6 −0:943 z 0:077 7 7 t 6 4 −1:080 0:078 5 0:949 2:743
−1:053 2:330 −0:914 −0:789 −2:826 0:135
3 0:054 0:662 7 7 −0:131 7 7 10 − 2 et −0:510 7 7 −0:455 5 0:377
The presence of these shared dynamic linkages suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. The dynamic linkages among the state variables are determined by the elements of the matrix
a bC 0 A
The six eigenvalues of this matrix are: Eigenvalues of A
Moduli
0.9334 7.083′ 10−3 ± 0.987i 0.943 ± 0.057i 0.9539
0.9334 0.9869 0.9443 0.9539
Because the moduli of these roots are less than one, the time series are stationary. The persistence of the latent dynamics is represented in Fig. 1 where the value of the states to one and allowing them to decay according to
τt + 1 zt + 1
=
a 0
bC A
τt zt
The decay paths of the state variables suggest that dynamic adjustments to exogenous shocks to IIP are persistent. These six state variables represent the latent dynamic linkages in deviations from long-term trends in the growth of industrial production shared by Bangladesh, India, and Pakistan. While the decay path represented in the uppermost left figure reflects the common stochastic trend, it is not possible to identify the other five states with particular combinations of the input data.
Table 2 Model performance. Bangladesh
India
Pakistan
df SST SSE R2 %AE cv
62 212,703 8444 0.960 0.51% 6.91%
62 452,006 3766 0.992 0.31% 3.41%
62 349,578 20,325 0.942 0.36% 7.85%
df SST SSE R2 %AE cv
8 139,988 1422 0.990 2.90% 4.80%
8 234,144 1117 0.995 2.81% 2.98%
8 98,649 5749 0.942 − 5.68% 8.38%
Sample
Out-of-sample
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Fig. 2. a. IIP: Bangladesh. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with cross-marks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals. b. IIP: India. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with cross-marks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals. c. IIP: Pakistan. Actual values of IIP are represented as points, estimates from the error-corrected model are represented as a solid line through the points, and residuals are represented with crossmarks. The vertical bar demarcates the sample and out-of-sample observations, estimates, and residuals.
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Performance of the state space-stochastic trend model of industrial production for Bangladesh, India, and Pakistan is represented in Table 2. The coefficients of variation of the out-of-sample forecasts are slightly smaller than the coefficients of variation for the sample forecasts for Bangladesh and India, while there is a slight increase in the coefficient of variation for the out-of-sample forecasts for Pakistan. Model performance is represented graphically in Fig 2a, b, and c. From our empirical analysis, results suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. The benefit of similarities of economic factors can be leveraged more if some steps are taken at the policy level. Presently, there are no specific provisions in the SAFTA agreement for adoption of measures of deeper integration, such as: (a) granting of transit facilities through their territories for goods coming from the neighboring countries; (b) cooperation for development of transport and other forms of infrastructure; (c) liberalization of investment and trade in services; (d) transfer of fund from the economically advance region to economically poor regions, to help the laggard regions modernize and diversify their economies; (e) reducing the number of negative lists (India's negative list in the context of SAFTA is larger than that in some of its bilateral free trade agreements, and almost four times as large as its latest offer in the negotiation for a free trade area with Association of Southeast Asian Nations); and lastly, (f) further easing of political differences with respect to India and Pakistan. 5. Conclusion In this paper, we attempted to determine to what extent South Asian countries are ready to form an OCA. In general SAARC nations share similar economic characteristics. This initial observation is supported well by our empirical results. Our empirical results suggest that the SAARC region has many characteristics that would be desirable in an OCA. The benefit of similarities of economic factors can be leveraged more with some additional coordination of policies at the government level. We based our empirical analysis by examining how the key economic variable, namely the outputs of three of the seven SAARC countries; Bangladesh, India and Pakistan, respond to external shocks. We found evidence of a shared latent dynamic linkage in long-term trends in the growth of industrial production for Bangladesh, India, and Pakistan and in the higher frequency dynamics about that trend. The presence of these shared dynamic linkages suggests that fluctuation in real output of Bangladesh, India and Pakistan are governed by similar dynamic processes. This means that an economic boom (recession) in one of these nations is likely to reverberate throughout the region. Such co-movements of outputs may be due to dependence on common factors, such as geographical proximity and similarity of trade composition. South Asian nations are geographically close and their economies share a similar industry profile. When countries share a similar industry profile and are located closely, then demand shocks in one country may affect other countries in the region. This could also arise if these economies all share a common trade linkage with major export markets. For example, to the extent that Bangladesh, India and Pakistan all engage in trading similar products with the EU, changes in the EU's economic performance would have a similar effect on all three nations because all the closely-related export sectors would be affected in a similar way. The other reason for the presence of common trend and hence co-movements can be explained through intra-industry trade. As far as the trade structure is representative of the output structure, the cycles should become more synchronized because they would be affected by common shocks. This is the argument made by Frankel and Rose (1998): that intra-industry trade increases synchronicity of output. Because we find evidence of latent dynamic linkages in deviations from the long-term stochastic trend in the growth of industrial production for Bangladesh, India, and Pakistan, and because these economies are similar in composition, enjoy considerable labor mobility, and engage in substantial amounts of legal and extra-legal trade, we conclude that they are good candidates for forming an OCA. Acknowledgements We would like to thank three anonymous referees for some valuable comments, especially, on the empirical part of the paper. Research supports from Utah Agricultural Experiment Station under Project No. UTA 00091 is gratefully acknowledged. References Aoki, M. (1987). State space modeling of time series. New York: Springer-Verlag. Aoki, M., & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10, 1−59. Artis, M., Kohler, M., & Melitz, J. (1998). Trade and the number of OCA's in the world. European University Institute Working Paper No. 98/16 (Florence, Italy). Backus, D. K., & Kehoe, P. J. (1992). International evidence on the historical properties of business cycles. American Economic Review, 82, 864−888. Bayoumi, T., & Eichengreen, B. (1993). Shocking aspects of European Monetary Unification. In F. Giavazzi, & F. Torres (Eds.), Adjustment and growth in the European Monetary Union (pp. 193−229). Cambridge: Cambridge University Press. Blanchard, O., & Quah, D. (1989). The dynamic effects of aggregate demand and supply disturbances. American Economic Review, 79, 655−673. Blanchard, O. J., & Katz, L. F. (1992). Regional evolutions, Brookings papers on economic activity. Washington D.C.: Brookings Institute. Cerchi, M., & Havenner, A. (1988). Cointegration and stock prices: The random walk on Wall Street revisited. Journal of Economic Dynamics and Control, 12, 333−346. Criddle, K. R., & Havenner, A. (1991). An encompassing approach to modeling fishery dynamics. Natural Resource Modeling, 5, 55−90. Fatás, A. (1997). EMU: countries or regions? Lessons from the EMS experience. European Economic Review, 41, 743−751. Frankel, J. A., & Rose, A. K. (1998). The endogeneity of the optimum currency area criteria. Economic Journal, 108, 1009−1025. Ghosh, A., & Wolf, H. C. (1994). How many monies? A genetic approach to finding optimum currency areas. National Bureau of Economic Research Working Paper No. 4805 (New York). International Financial Statistics Yearbook, International Monetary Fund, Various Issues. Kwan, C. H. (1994). Economic interdependence in the Asia-Pacific region. London: Routledge.
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Lumsdaine, R. L., & Prasad, E. S. (2002). Identifying the common component of international economic fluctuations: A new approach. IZA Discussion Paper No. 487 (IZA, Bonn). McKinnon, R. I. (1963). Optimum currency area. American Economic Review, 53, 717−725. Mills, T. C., & Holmes, M. J. (1999). Common trends and cycles in European industrial production: Exchange rate regimes and economic convergence. The Manchester School, 67, 557−587. Mundell, R. (1961). A theory of optimum currency areas. American Economic Review, 51, 657−665. Rose, A., & Engel, C. (2000). Currency unions and international integration. NBER Working Paper No. 7872 Cambridge: National Bureau of Economic Research. Shinji, T. (1996). The yen and its East Asian neighbors, 1980–95: Cooperation or competition? NBER Working Paper No. 5720 Cambridge: National Bureau of Economic Research. South Asian Association for Regional Cooperation (1999). SAARC vision beyond the year 2000: Report of the SAARC group of eminent persons established by the Ninth SAARC Summit. New Delhi: Shipra Publications. South Asia Development and Cooperation Report, 2001–02, (New Delhi: Research and Information System for the Non-Aligned and Other Developing Countries). Taneja, N. (2004, December). Informal trade in SAARC region. Economic and Political Weekly, 5367−5371. World Bank, 2004, World development indicators, available at http://publications.worldbank.org/WDI accessed January 2005.