School of Public Policy and Governance Tata Institute of Social Science, Hyderabad STUDENT WORKING PAPER SERIES NO. 1, DECEMBER 2015
Causal Linkage between FDI Inflow and Exchange Rate : An Empirical Analysis for India
Madhabendra Sinha
 
School of Public Policy and Governance Tata Institute of Social Sciences, Hyderabad Roda Mistry College of Social Work and Research Centre, Opposite Biodiversity Park, Gachibowli, Hyderabad, Telangana - 500008 Email : sppg-si@tiss.edu Website : http://goo.gl/mQGBpF
SPPG TISS - HYDERABAD
1
About Student Working Paper Series The Student Working Paper Series, is an attempt by the School of Public Policy and Governance, at Tata Institute of Social Sciences, Hyderabad to assimilate papers being worked upon the topics that will help enrich the public discourses by improving upon the clarity, accuracy and sophistication of discussions on the nation's Public Policy. About School of Public Policy and Governance The School of Public Policy and Governance (SPPG) is a novel research based teaching and training space designed to equip young professionals to contribute to the policy area research. SPPG provides opportunities to its students to think beyond conventional models of growth and development, and encourages them to generate ideas for developing institutional frameworks for accountable governance and the establishment of a socially equitable society. Its programs and activities are designed to create an environment for the well-trained scholars to access and collect information about contemporary policies and activities surrounding them so that they can produce timely research and undertake analysis on key topics of Public Policy. SPPG TISS - HYDERABAD
2
Causal Linkage between FDI Inflow and Exchange Rate: An Empirical Analysis for India - Madhabendra Sinha1
Abstract The study is an attempt to investigate the causal relationship between FDI inflows and exchange rate empirically in India. FDI may affect equilibrium real exchange rate in both ways i.e., appreciation or depreciation of domestic currency depending on the use of these inflows. On the other hand impact of exchange rate fluctuations on FDI arises due to change in the relative production costs across countries and a higher cost of financing the investment project.In our empirical study we use annual data of FDI inflow and different measures of exchange rate like REER, NEER and exchange rate of Indian rupee vs US dollar; and the data has been collected from DIPP and RBI respectively. The findings suggest that there is not any causality between FDI inflow and real exchange rate in India.
Keywords: Foreign Direct Investment, Real exchange rate, Cointegration test.
1
The author is currently a Research Scholar in Economics at University of Calcutta, recently qualified for the degree of M.Phil. in Economics (2013-2015). His research interests include International Economics, Open Economy Macroeconomics, Development Economics and Applied Econometrics etc. The author is thankful to Dr. Panchanan Das, Associate Professor of Economics, University of Calcutta for valuable comments on early versions of this paper. He is indebted to the participants in Graduate Seminar at School of Public Policy and Governance, Tata Institute of Social Sciences, Hyderabad, India in 2015. Important comments of Dr. Chinmay Tumbe of TISS Hyderabad are gratefully acknowledged. The usual disclaimer applies. Email: madhabendras@gmail.com.
SPPG TISS - HYDERABAD
3
1. Introduction In the growth process role of FDI has been a debatable issue in the transitional as well as developing economies like India. Low technological base of production is another factor impinging upon growth of developing countries. In this context FDI may mitigate these constraints to growth to some extent. FDI brings capital with foreign technology and modern managerial techniques and organizational structures (Prakash and Balakrishnan, 2006). UNCTAD (2015) reports that global FDI inflows has been decreased by 16 per cent in 2014, mostly because of the fragility of the global economy, policy uncertainty for investors, elevated geopolitical risks etc; whereas the developing countries accounted for a record 55 per cent of global FDI inflows reached their highest level ever at US$ 681 billion with a 2 per cent rise, exceeding flows to developed economies, which experiences falling FDI inflows by 28 per cent. UNCTAD (2015) also pointed out that starting from a baseline of less than US$ 1 billion in 1990; India ranked 8th position and recorded the increasing FDI inflows by 22 per cent to US$ 34 billion than previous year in 2014. India is also experiencing a remarkable export growth since liberalization in different sectors. In the context of economic reforms in India in the form of integration of the domestic economy to the global one, the role of foreign capital in improving economic growth has been a serious cause of concern. This recent wave of FDI inflows may result in some of the unfavorable side effects of FDI in the country. One of these effects has been referred to as ‘the real exchange rate problem’ by Corden (1994) i.e., there is a possibility that capital inflows give rise to appreciation of the real exchange rate (the relative price of traded to non-traded goods) with adverse consequences for production of traded goods in the domestic economy. Theoretically FDI can affect equilibrium real exchange rate in both ways, either appreciation or depreciation of domestic currency depending on the use of the inflows. On another side fluctuations of the exchange rate are having an impact on FDI. First, the exchange rate movements need to be associated with a change in the relative costs of production across countries, and thus should not be accompanied by an offsetting increase in the costs of production in the destination market for investment capital. Second, the importance of the ‘relative SPPG TISS - HYDERABAD
4
wage’ channel may be diminished if the exchange rate movements are anticipated. Anticipated exchange rate moves may be reflected in a higher cost of financing the investment project, since interest rate parity conditions equalize risk-adjusted expected rates of returns across countries. By this argument, stronger FDI implications from exchange rate movements arise when these are unanticipated and not otherwise reflected in the expected costs of project finance for the FDI. In this context the proposed study is an attempt to look into the relationship between FDI Inflow and Exchange Rate in India.
We know FDI is an important source of capital financing in capital deficit developing countries. There is a possibility that capital inflows give raise to appreciation of the real exchange rate i.e. the relative price of traded to non-traded goods, with adverse consequences for traded goods production in the domestic economy. According to Baffes et al (1999), if FDI is used to finance imports, it does not affect equilibrium real exchange rate; however, its use for domestic non-tradable will lead to the appreciation of domestic currency. Rodrik (2007) observed that real exchange rate overvaluations weaken longterm economic growth, particularly for developing countries, in that in those countries, tradable goods production suffers disproportionately from weak institutions and market failures. This emphasizes the importance of the implications of remittances for real exchange rate movements. In fact, exchange rate misalignment is a common feature in most developing countries now. Since early 1980s, real exchange rate misalignment has become a standard concept in international macroeconomic theory and policy (Razin & Collins, 1997) and therefore, exchange rate management is a challenging macroeconomic policy issue in the modern literature on the real exchange rate.
The dependent economy model, commonly known as the ‘Salter-Swan-CordenDornbusch paradigm’ (Salter, 1959; Swan, 1961; Corden, 1960; and Dornbusch, 1980), provided a theoretical base of empirical analysis regarding the impact of foreign capital on the real exchange rate in developing economies. Within this theoretical model, an
SPPG TISS - HYDERABAD
5
increase in capital inflows to a sector of the economy increases the marginal product of labor, and hence the real wage, in the sector, drawing resources out of other tradable sectors (resource movement effect). A higher real household income triggers an expansion in aggregate demand, which for exogenously given prices of tradable goods, culminates in higher relative prices of non-tradable goods (spending effect) which causes further movement of resources toward this sector. A rise in the relative price of non-tradable goods corresponds to a real exchange rate appreciation. Acosta et al. (2007) developed a dynamic stochastic general equilibrium model considering an additional mechanism as an increase in household income results in a decrease in the labor supply. A shrinking labor supply is associated with higher wages (in terms of the price of tradable output), that in turn leads to higher production costs and a further contraction of the tradable sector. Both the real exchange rate and the ratio of tradable to non-tradable output therefore serve as summary indicators of the outcome of macroeconomic adjustments that occur following an increase in capital inflow i.e., Dutch disease effects viz. the spending effect and resource movement effect.
Here we should mention a remarkable theoretical investigation by Chakrabarti and Scholnick (2002), which refers that owing to inelasticity in expectations, investors do not revise their expectations of future exchange rates to the full extent of changes in current exchange rate. Thus, if they believe that a devaluation of a foreign currency will be followed by a mean reversion of the exchange rate, this implies that immediately after devaluation the foreign currency would be temporarily ‘cheap’ (temporary change in foreign currency value). As a consequence, ceteris paribus, FDI would flow to the country under these circumstances because foreign assets currently appear to be cheap relative to their expected future income stream.
The appreciation and depreciation of currency does have an impact on the price of exports and imports making their comparative position and competitiveness in international markets fluctuate sometimes towards advantage to the home country and
SPPG TISS - HYDERABAD
6
sometimes disadvantage. Aliber (1970) argued that firms which come from countries that have strong currency are able to financially support their foreign direct investments in a much better manner than those firms which come from countries that have a inherently weak currency. The link between the interest rate and exchange rate also makes it more beneficial for a firm to go in for a FDI as the currency appreciates. Similarly the reduction in competitiveness of exports also may make the country look for better ways of entering international business. Thus, there appears to be a sure link between the exchange rate and outward FDI, and the relation is expected to be positive for outflows and negative for inflows as shown in the model. The real effective exchange rate index of the Indian economy which is calculated as a weighted average of top currency basket is taken as a representative of the variable exchange rate.
Another literature survey suggests that theoretical predictions on the effects of exchange rate volatility on FDI flows are diverse. Dixit and Pindyck (1994,1995), Pindyck (1998), Campa (1993) and Rivoli and Salorio (1996) claim that the changing value of real options, stemming from unexpected business uncertainty about the financial market, is the driving force behind FDI. One implication of this theory is that, given the sunk cost nature of local fixed costs, MNEs can withhold FDI if exchange rate uncertainty increases. Exchange rate volatility leads to uncertainty about the return, thereby increasing the value of delaying FDI. This option theory-based argument is valid even for risk neutral MNEs, as the sunk cost is the main determinant of the option value of holding investment. By contrast, Devereux and Engel (2001) suggested that FDI can be better facilitated under a flexible exchange rate than under a fixed exchange rate, particularly when firms price their investments in the currency of the local market. This is consistent with the fact that floating regimes generally stimulate production by all firms, including the subsidiaries of MNEs. Itagaki (1980) shows that an increase in exchange rate volatility may motivate MNEs to invest abroad as a way of hedging (by the value of assets and repatriated profit streams from abroad) against a short position (value of domestic assets relative to foreign liabilities) on their balance sheets. Goldberg and Kolstad (1995) shows
SPPG TISS - HYDERABAD
7
that exchange rate volatility can increase the share of risk-averse MNEs’ production capacity that is located abroad if production costs are positively correlated with revenues from these foreign markets. On the other hand, Aizenman and Marion (2004) proposed that the response of MNEs to exchange rate volatility would differ depending on real or nominal shocks, whereas Russ’s (2007) general equilibrium model suggests that an MNE’s response to volatility will differ depending on whether this volatility arises from shocks in the source country or the host country.
The predictions of the effect of exchange rate volatility on FDI flow differ depending on the hypotheses. The effect would be negative according to Dixit-Pindyck’s option theory and/or Aizenman’s (1992) inflexibility of production structure hypothesis. By contrast, it would be positive according to Devereux and Engel’s (2001) pricing-to-market hypothesis and/or Itagaki’s (1981) hedging hypothesis. The share of productive capacity located abroad would also be positively affected by the increased volatility if Goldberg and Kolstad’s (1995) theory of risk-averse foreign investor behavior is valid.
We know the rate of growth of FDI has dramatically increased in India compared to that of the early 1990s. This indicates the rising competitiveness of India in attracting FDI which demands empirical research since it would be vital to investigate which factors that contributed to the augmentation of competitiveness. Existing literatures concentrate mainly on cross country panel data analysis. The present study empirically intends to investigate relationship between FDI inflows and exchange rate movements empirically in India by using time series data. The causal relationship between FDI and exchange rate is complicated, which necessitates paying an attention towards finding the causal links between FDI and exchange rate in India. There are few empirical studies, which have found the relationship between inflow and outflow of FDI and exchange rate. Most of them suggested that exchange rate is a determinants of FDI (Froot and Stein (1991), Klein and Rosengren (1994), Cushman (1985), Campa (1993), Goldberg and Kolstad (1995), Blonigen (1997), Chakrabati and Scholnick (2002) etc.), and few of them explained
SPPG TISS - HYDERABAD
8
that FDI causes exchange rate (Corden (1994), Calvo et al (1996), Edwards (2000), Baffes et al.(1999), Biswas and Dasgupta (2012) etc.). But in the present study bi-directional causality between FDI and exchange rate can be investigated by using annual data from 1970 to 2013. The rest of the paper is structured as follows. Section 2 provides an overview of the scenario of the FDI inflow in India followed by a discussion about the India’s foreign exchange rate movements in Section 3 and remittance inflows in India as determinants of exchange rate in Section 4. Hypotheses taken of the study have been presented in Section 5, followed by data base and methodology presentation in Section 6 with some sub-sections and finally the paper is concluded in Section 8 after the discussion of Empirical finding in Section 7.
2. Scenario of the FDI inflow in India FDI inflow has started slowly in India following the new economic reform. It was very low after the liberalization process. The inflow was within the range of less than just US$ 1 billion till 1994-95 because of limited and restricted opening up of the economy. After that it has gradually gained its momentum. It has doubled by the year 2002 because of further impetus to the reform process. The enormous growth actually has occurred during the last decade due to the sector specific and target oriented FDI policy intervention by the government. The process of liberalization slowed down in late nineties, but deepened again in the new millennium. The FDI inflow in India suitably reflects the gaining of pace of the reform measures. Rao and Dhar (2011) noted that average reported FDI equity inflows during 1991-92 to 1999-00 was US $ 1.72 billion, but the same increased to US $ 2.85 billion during 2000-01 to 2004-05. In line with further economic reforms and emergence of the Indo-centric regional trade agreements (RTAs), an unprecedented level of FDI inflow has been observed afterwards, taking the corresponding figure to US $ 19.73 billion during 2005-06 t o 2 0 0 9 -10.
A number of capital account refor m measures have
b e e n undertaken over the period, which considerably liberalized FDI inflow in the country. However, owing to Indian indirect taxes and transportation infrastructure FDI SPPG TISS - HYDERABAD
9
flows has been lower vis-Ă -vis the Chinese experience (Shah and Patnaik, 2005).
Table 1: Financial Years Wise FDI Inflow in India Sl. No.
Financial Year (April To March)
Total FDI flow in US$ Million
Total FDI Flows % Growth Over Previous
1
2000-01
4,029
-
2
2001-02
6,130
(+) 52 %
3
2002-03
5,035
(-) 18 %
4
2003-04
4,322
(-) 14 %
5
2004-05
6,051
(+) 40 %
6
2005-06
8,961
(+) 48 %
7
2006-07
22,826
(+) 146 %
8
2007-08
34,843
(+) 53 %
9
2008-09
41,873
(+) 20 %
10
2009-10 (P) (+)
37,745
(-) 10 %
11
2010-11 (P) (+)
34,847
(-) 08 %
12
2011-12 (P)
46,556
(+) 34 %
13
2012-13 (P)
36,860
(-) 21%
14
2013-14 (P) (Apr-Oct, 2013)
18,934
-
Source: DIPP, Ministry of Commerce and Industry, Govt. of India
Table 1 shows that flows of FDI received in India during April 2000 to October 2013 i.e. 309,012 US $ million. From the year 2000 to 2002 FDI inflow in India has shown a increasing trend. This may be the result of Foreign Exchange Management Act (FEMA) which is introduced in 1999. Further it follow negative trend up to period 2003-2004. But from the year 2004-05 to 2008-09 investment into India once again start growing. The highest FDI inflows growth in the country in 2006-2007 year was 146%. Further, FDI inflows rose by 34 % to US$ 46,556 million during 2011-12. Last year April 2012-13 has shown negative growth rate i.e. 21% to US$ 36,860 million while the cumulative amount of FDI equity inflows from April 2000 to October 2013 stood at US$ 309,012 billion, according to the latest data released by the Department of Industrial Policy and Promotion (DIPP). The Indian experience of FDI inflow has been represented in the following figures 1 by using yearly data from 1970 to 2013.
SPPG TISS - HYDERABAD
10
Figure 1
Source: UNCTAD STAT Data Base, Yearly Data from 1970-71 to 2013-14.
3 Foreign Exchange Rate and its Experiences in India The exchange rate is a key financial variable that affects decisions made by foreign exchange investors, exporters, importers, bankers, businesses, financial institutions, policymakers and tourists in the developed as well as developing world. Exchange rate fluctuations affect the value of international investment portfolios, competitiveness of exports and imports, value of international reserves, currency value of debt payments, and the cost to tourists in terms of the value of their currency. Movements in exchange rates thus have important implications for the economy's business cycle, trade and capital flows and are therefore crucial for understanding financial developments and changes in economic policy also with the reverse effect on it from the same macroeconomic factors.
SPPG TISS - HYDERABAD
11
Figure 2
" Source: UNCTAD STAT Data Base, Yearly Data from 1980 to 2012.
India has been operating on a managed floating exchange rate regime from March1993, marking the start of an era of a market determined exchange rate regime of the rupee with provision for timely intervention by the central bank. As has been the experience with the exchange rate regimes the world over, the Reserve Bank as the central bank of the country has been actively participating in the market dynamics with a view to signaling its stance and maintaining orderly conditions in the foreign exchange mark. As a result of calibrated and gradual capital account openness, the financial markets, particularly foreign exchange market, in India have also become increasingly integrated with the global network since 2003-04. This is reflected in the extent and magnitude of capital that has flown to India in recent years. Major decisions taken by Government of India, on account of Indian Exchange Rate are as Follows:
1975: To ensure stability of the Rupee, and avoid the weaknesses associated with a single currency peg, the Rupee was pegged to a basket of currencies. Currency selection and weight assignment was left to the discretion of the RBI and not publicly announced. 1978: RBI allowed the domestic banks to undertake intra-day trading in foreign exchange. 1978-1992: As trading volumes increased, the 'Guidelines for Internal Control over Foreign Exchange Business' were framed in 1981. The foreign exchange market was still highly regulated with several restrictions on external transactions, entry barriers and SPPG TISS - HYDERABAD
12
transactions costs Foreign exchange transactions were controlled through the Foreign Exchange Regulations Act (FERA). These restrictions resulted in an extremely efficient unofficial parallel (hawala) market for foreign exchange. 1990-1991: Balance of Payments crisis. July 1991: To stabilize the foreign exchange market, a two step downward exchange rate adjustment was done (9% and 11%). This was a decisive end to the pegged exchange rate regime. March 1992: To ease the transition to a market determined exchange rate system, the Liberalized Exchange Rate Management System (LERMS) was put in place, which used a dual exchange rate system. This was mostly a transitional system. March 1993: The dual rates converged, and the market determined exchange rate regime was introduced. All foreign exchange receipts could now be converted at market determined exchange rates.
3.1 Nominal Effective Exchange Rate and Real Effective Exchange Rate The indices of Nominal Effective Exchange Rate (NEER) and Real Effective Exchange Rate (REER) are used as indicators of external competitiveness. NEER is the weighted average of bilateral nominal exchange rates of the home currency in terms of foreign currencies. Conceptually, the REER, defined as a weighted average of nominal exchange rates adjusted for relative price differential between the domestic and foreign countries, relates to the purchasing power parity (PPP) hypothesis. The rupee is considered to be fairly valued if the REER is close to 100 or the base-year value. Other things remaining same, higher domestic inflation vis-Ă -vis its trade partners will reflect in appreciation of the REER and hence there is a view that the nominal exchange rate should depreciate to keep it at base-year levels. The Reserve Bank of India (RBI) has been constructing five-country and thirty six country indices of NEER and REER as part of its communication policy and to aid researchers and analysts. Theses indices are published in the Bank's monthly Bulletin.
SPPG TISS - HYDERABAD
13
Reserve Bank has now decided to replace its existing 5-country indices with new sixcurrency indices of NEER/REER. The thirty six country indices have also been revised and replaced with new 36-currency indices of NEER/REER. In our proposed study we use both Real Effective Exchange Rate (REER) and Nominal Effective Exchange Rate (NEER) trade based.
4. Remittance Inflows and Exchange Rate in India The international migrant remittances have been becoming an important source of capital in terms of both growth rate and magnitude, exceeding the inflows of foreign aid and private capital in many countries including India within the last two decades. Estimated value of remittance received by developing countries is US $ 221 billion in 2006, increased by 132 per cent as compared to 2001 and 1.9 per cent of total income in emerging economies in 2008 (World Bank, 2008). Currently remittance inflows account one-third of total financial flows to the developing world. These type of inflows are directly received by the families of remitters, which impact directly on poverty reduction and high rate of growth (Adams and Page, 2005; Acosta et al., 2008) in receiving countries. Remittances lead to curtail the current account deficits of the receiving country as the countries are treated as unrequited current private transfers in the balance of payments (BOP) accounts. However, remittances are having important implications for equilibrium real exchange rate. If the inflows are largely spent on non-tradable goods, it may result in favour of the appreciation of real exchange rate. Amuedo-Dorantes and Pozo (2004) and López et al. (2007) showed that like any other massive capital inflow, rising levels of remittances inflows can appreciate the real exchange rate and therefore generate a resource allocation from the tradable to the non-tradable sector (Acosta et al., 2007). This type of phenomenon is commonly known as the ‘Dutch disease effects’ literature. In case of India, remittances from overseas comprise of the inflows towards family maintenances and the funds withdrawn domestically from the Non-Resident Indians (NRI) rupee deposits [NRERA (Non-Resident External Rupee Account) and NRO (Non-Resident
SPPG TISS - HYDERABAD
14
Ordinary) deposit schemes]. The remittances of migrant workers’ to India increased steadily from 1970s, remained more-or-less flat in the 1980s and started picking up sharply with the revolution of information technology in the 1990s. Since the policy of economic reform, foreign remittances have become an important component of India’s overall balance of payments and within a decade India wins the record of the world’s largest recipient of remittances. Reserve Bank of India (RBI) reported that, remittances from overseas Indians were a modest US$ 3.8 billion in 1991. The remittances also had risen steadily in the last two decades. The figure increased to US$ 22 billion in 2003 from US$ 12.4 billion in 1996 and then jumped to US$ 24.5 billion in 2005 with a small decrease in 2004 (US$ 20.5 billion). After that remittances grow from US$ 44.6 billion in 2008 to US$ 52.1 billion in 2009 and US$ 68 billion approximately. However we will not take remittance in account as a significant determinant of exchange rate as our focus is to find out the causal linkage between FDI inflow and exchange rate in India.
5. Hypotheses As the foreign exchange rate is a major macroeconomic variable to be affected by the foreign capital flows in the economy simply as the exchange rate is the price of domestic currency in terms of foreign currency. It is indicative of economy’s external competitiveness and a reflection of balance of payments position. Exchange rate fluctuations can have a deep impact on the banking and financial sectors of the economy when the fluctuations are high and sudden. An inflow of capital flows causes the real exchange rate to appreciate. This is because an inflow of capital flows means an increase in the amount of dollars vis-à-vis the Indian rupee. The supply of dollars increases as a result of which the dollar weakens vis-à-vis the Indian rupee. This means the Indian rupee appreciates in value. An appreciation in Indian rupee has a negative impact on exports as our exports become relatively more expensive. This in turn impacts the aggregate demand of the economy and hence exchange rate is an important macroeconomic variable for the growth and development of the economy.
SPPG TISS - HYDERABAD
15
In this background the two hypotheses to be tested for achieving the above mentioned objective of the study are categorized as following: (a) FDI inflow do not cause any change in exchange rate and; (b) Changes in exchange rate do not have any effect on FDI inflow.
6. Data Base and Methodology 6.1 Variables, Data Source and Period of the Study In this empirical investigation to find relationship between FDI inflows and Exchange rate in terms of its different measures like REER, NEER and exchange rate of Indian Rupee vs US Dollar, we use data of FDI inflow in India and different measures of Indian’s Foreign Exchange Rate have been treated as the variables of the study. The FDI (FDI_UN) data has been collected from UNCTAD STAT Data Base of United Nations Conference on Trade and Development. From the various issues of the Hand Book of Statistics for Indian Economy published by Reserve Bank of India, we get the India’s annual real as well as nominal effective exchange rate (trade based) and also the exchange rate of Indian Rupee compared to US economy for the period 1970s to 2013. 6.2 Econometric Methodology In case of macroeconomic time series like GDP, Export, Import, FDI, Exchange Rate etc. relating to our study may contain either deterministic trend or stochastic trend or both. Implications of them are qualitatively different. The time series with deterministic trend follows trend stationary process (TSP), while a non-stationary time series showing stochastic trend is a difference stationary process (DSP). The issue whether a macroeconomic time series is of DSP or TSP is extremely important because the dynamic properties of the two processes are different (Nelson and Plosser, 1982). While the former is predictable, the latter is completely unpredictable. In a series following TSP, cyclical fluctuations are temporary around a stable trend, while for DSP any random shock to the series has a permanent effect. The cyclical components of a TSP originate from the residuals of a regression of the series on the variable time, and a DSP involves regression
SPPG TISS - HYDERABAD
16
of a series on its own lagged values and time. A TSP has a trend in the mean but no trend in the variance, but a DSP has a trend in the variance with or without trend in the mean and here it should be mentioned that a random walk without drift has no trend in the mean values of the variable. The most widely used model to take over stochastic trend is autoregressive of order p [AR(p)]:
X = α + β1 X t −1 + β 2 X t − 2 + β 3 X t −3 + ............ + β p X t − p + ε t " t
(1)
Xt gives values in log form in time t and εt is a stationary series with mean zero and variance σ2. This model can generate the trend behaviour of macroeconomic time series and the randomly fluctuating behaviour of their growth rates. If, for example, Xt is generated by the model:
"X t = α + X t −1 + ε t
(2)
Equation (2) is AR(1) with β1=1, accumulating Xt starting with an initial value X0 we get, t
X t = X 0 + αt + ∑ ε j j =1 "
(3)
The Equation (3) has the same form as the conventional log-linear trend equation, excepting for the fact that the disturbance is not stationary. One important property of time series data, not usually present in cross-sectional data, is the existence of correlation across observations. Income today, for example, is highly correlated with income of the last year. Thus Xt tends to exhibit trend behaviour and to be highly correlated over time. The non-stationary time series containing a unit root will give a stochastic trend. If β1 = 1 for the AR(1) model, then Xt has a unit root and exhibit trend behaviour, especially when α ≠ 0. Unit root series contain a so called stochastic trend.
SPPG TISS - HYDERABAD
17
The Augmented Dickey-Fuller (ADF) test is performed for unit root hypothesis. The more appropriate model for testing a unit root is the AR(p) with deterministic trend:
ΔX = α + ρX t −1 + η1 ΔX t −1 + η 2 ΔX t − 2 + ........... + η p −1 ΔX t − p +1 + δt + ε t " t , (4) A series belongs to the class DSP exhibiting stochastic trend if ρ =0, δ=0, and the TSP class if ρ < 0. If ρ = 0, then Xt contains a unit root. In this case we cannot perform hypothesis testing by utilising the usual distributions appropriate for least square. Thus we have to follow ADF test. If the t-statistics on ρ are less negative than the Dickey-Fuller critical value, we conclude that the series Xt has a unit root. To test whether the series has a unit root, we have to choose lag length (p). Many sophisticated statistical criteria and testing methods are available to determine the appropriate lag length in an AR(p) model. But we have performed a simple route by choosing a maximum lag length and then sequentially drop lag lengths if the relevant coefficients are insignificant. The maximum lag length is chosen by following Schwert (1989) rule:
Pmax = integer part of [12(T/100).25].
(5)
AIC is also used for selecting the appropriate lag length. By following such criteria, the maximum lag length is found to be 1. Thus our model would be: "ΔX t = α + ρX t −1 + η1 ΔX t −1 + δt + ε t ,
(6)
The stochastic properties of the time series used in this study have been examined by carrying out Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) unit root tests. Both the intercept and trend components have been incorporated in the ADF estimated relation as following: P
ΔX t = φ 0 + βt + ρX t −1 + ∑ γ i ΔX t −1 + ε t i =1 "
SPPG TISS - HYDERABAD
(7)
18
The ADF statistic is the t-value associated with the estimated coefficient of ρ, the probability distribution of which is a functional of the Weiner process, the process used in explaining Brownian motion of a particle with large number of molecular shocks (Maddala and Kim, 1998). The PP test is the non-parametric extension of the DF unit root test by adding a correction factor to the DF t statistic. The tests have been performed for all the logarithmic series and their first differences. The choice of lag length is very much crucial at this stage and the number of lags used in the ADF regressions is selected by the Akaike (1969) Information Criterion (AIC). We have applied cointegration theory developed in Engle and Granger (1987) by utilising the methodology developed by Johansen and Juselius (1990). The concept of cointegration, first developed in Granger (1981), is relevant to the problem of the determination of long-run equilibrium relationships in economics in a sense that the variables move together over time so that short-term disturbances from the long-term trend will be corrected. Engle and Granger (1987) have shown that if two time series are cointegrated there will be a causal relation in at least one direction. Furthermore, the Granger Representation Theorem demonstrates how to model cointegrated I(1) series in the form of vector autoregression (VAR). In particular, the VAR can be constructed either in terms of the levels (logarithmic values) of the data, the I(1) variables; or in terms of their first differences, the I(0) variables, with the addition of an error correction mechanism (ECM, which is first used by Sargan (1984) and later popularised by Engle and Grager (1987)) to capture the short-run dynamics. If the data are I(1) but not cointegrated, causality tests cannot accurately be performed unless the data series are transformed into stationary series. For two variables Y and X, the model can be presented either of the following form:
p
r
ln X t = θ + ∑ π i ln X t −i + ∑ φ j ln Yt − j + vt i =1 j =1 "
SPPG TISS - HYDERABAD
(8)
19
m
n
ln Yt = α + ∑ β i ln X t −i + ∑ γ j ln Yt − j + u t i =1 j =1 "
(9)
or, p
r
Δ ln X t = θ + ∑ π i Δ ln X t −i + ∑ φ j Δ ln Yt − j + λECM t −1 + vt i =1 j =1 " m
(8.a)
n
Δ ln Yt = α + ∑ β i Δ ln X t −i + ∑ γ j Δ ln Yt − j + δECM t −1 + u t i =1 j =1 "
(9.a)
Where ut and vt are zero-mean, serially uncorrelated, random disturbances, errorcorrection mechanism is denoted by ECM. If the data are I(1) but not cointegrated, valid tests may be done by using the first differences without the error correction term:
p
r
Δ ln X t = θ + ∑ π i Δ ln X t −i + ∑ φ j Δ ln Yt − j + vt i =1 j =1 " m
(10)
n
Δ ln Yt = α + ∑ β i Δ ln X t −i + ∑ γ j Δ ln Yt − j + u t i =1 j =1 "
(11)
When the variables are not cointegrated then we have to examine the short run dynamic relationships between them by utilising the unrestricted VAR structure shown as the following two equations. By incorporating one period lag as suggested by the minimum AIC rule, the bi-variate VAR used in this study takes the following form:
"Δ ln X t = α 0 + α 1 Δ ln X t −1 + α 2 Δ ln Yt −1 + ε 1t
(12)
"Δ ln Yt = β 0 + β1 Δ ln X t −1 + β 2 Δ ln Yt −1 + ε 2t
(13)
The lagged terms of ΔXt and ΔYt appeared as explanatory variables, in the VAR structure indicate short run dynamics or cause and effect relationship between the two series. Thus, if the lagged coefficients of ΔYt appear to be significant in the regression of ΔXt this SPPG TISS - HYDERABAD
20
means that Y affects X. Similarly, the opposite holds if the lagged coefficients of ΔXt are significant in ΔYt. If none of the lagged coefficient is significant anywhere this implies that there is no cause and effect relationship between the two series. On the basis of theoretical econometric framework suitable for time series data analyzed as above, we will try to do the empirical analysis of the broad area of study about FDI, Exchange Rate and Economic growth by using Indian data in the following chapters.
7. Empirical Findings 7.1 Unit Root Test Table 2: Estimated statistics of unit root tests Series
Augmented Dickey-Fuller Test Statistics
Phillips Perron Test Statistics
Level
First Difference
Level
First Difference
FDI_UN
3.13
-7.59***
-2.09
-7.59***
LNREER_TR
-1.1
-4.82***
-1.12
-5.51***
LNNEER_TR
-1.39
-4.19**
-1.73
-4.14**
LNEXR_US
-0.97
-4.01**
-1.38
-3.97**
***, ** and * denote the level of significance at 1%, 5%, and 10%, respectively Source: Author’s estimation by using data from UNCTAD (2013) and RBI (2014)
The Table 2 presents the ADF and PP test statistics for testing unit roots of the series for foreign direct investment (FDI_UN), trade based real effective exchange rate (REER_TR), trade base nominal effective exchange rate (NEER_TR) and exchange rate of Indian Rupee vs US Dollar (EXR_US). The null hypothesis of the presence of unit roots is not rejected in the original series indicating that none of the series is stationary. However, the presence of unit roots is conclusively rejected in the first differences of the series for all the variables in both models. 7.2 Cointegration Test The dynamic relationships between the FDI and each indicator of the Indian foreign exchange rate as mentioned above have been estimated by using cointegration theory
SPPG TISS - HYDERABAD
21
developed in Engle and Granger (1987). The ADF and PP unit root tests suggest that the FDI and each indicator of exchange rate are integrated in order one. Therefore, they may have a common trend and it is reasonable to search for a possible cointegrating relationship between each type of exchange rate and FDI series.
Table 3: Estimated Statistics of Johansen Cointegration Test Unrestricted Cointegration Rank Test
Trace
Maximum Eigenvalue
Hypothesized No. of CE(s)
Eigenvalue
Statistic
5% Critical Value
Prob.**
None
0.341664
29.65751
47.85613
0.7363
At most 1
0.230959
14.19004
29.79707
0.8298
At most 2
0.109340
4.473435
15.49471
0.8619
At most 3
0.005098
0.189108
3.841466
0.6637
None
0.341664
15.46747
27.58434
0.7104
At most 1
0.230959
9.716605
21.13162
0.7708
At most 2
0.109340
4.284327
14.26460
0.8282
At most 3
0.005098
0.189108
3.841466
0.6637
Trace test indicates no cointegration at the 0.05 level Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
Source: Authorâ&#x20AC;&#x2122;s estimation by using data from UNCTAD and RBI
The above Table 3 reports the estimated results of Johansenâ&#x20AC;&#x2122;s cointegration tests. Both the LR test statistic and the eigenvalues are used for testing cointegration relation between the series of price indices. The upper panel shows the trace or LR test statistic and the lower panel provides maximum eigenvalues in testing the hypothesis of presence of cointegrating relation, against the alternative hypothesis of full rank. The likelihood values are lower than the critical values at 5 percent for the null hypotheses that the presence of no cointegrating equations and at most one cointegrating equations in the model. It suggests that the either type of FDI and different measures of Indian exchange rate are indeed not cointegrated. There has been no long run relationship between them of any type in India as per available data series. As the FDI is not cointegrated with the different measures of exchange rate, now we have to examine the short run dynamics
SPPG TISS - HYDERABAD
22
between them by utilising the unrestricted VAR structure by incorporating one period lag as suggested by the minimum AIC rule, the bi-variate VAR is to be used in this study has been explained briefly as earlier in methodology.
The following Table 4 presents the estimated results of unrestricted VAR for estimating short run relationships of REER_TR, NEER_TR and EXR_US respectively with FDI_UN. The figures in [] brackets indicate t values. The estimated results suggest no short run causal relationship between them. Table 4: Estimated Coefficients in Unrestricted VAR D(FDI_UN)
D(LNREER_TR)
D(LNNEER_TR)
D(LNEXR_US)
-0.156546
8.10E-07
5.41E-07
-6.25E-07
[-0.94728]
[ 0.42766]
[ 0.27049]
[-0.25296]
6488.382
-0.013328
-0.351216
0.497629
[ 0.21857]
[-0.03917]
[-0.97837]
[ 1.12208]
6465.569
0.163718
0.665130
-0.298788
[ 0.21908]
[ 0.48403]
[ 1.86373]
[-0.67769]
-18643.25
0.047429
0.077594
0.413718
[-1.09159]
[ 0.24230]
[ 0.37570]
[ 1.62145]
2076.153
-0.010519
-0.021705
0.028714
[ 1.90602]
[-0.84259]
[-1.64777]
[ 1.76453]
R-squared
0.183952
0.020733
0.139530
0.162973
Adj. R-squared
0.081946
-0.101675
0.031971
0.058344
Sum sq. resids
8.92E+08
0.117193
0.130466
0.199123
S.E. equation
5280.180
0.060517
0.063852
0.078883
F-statistic
1.803340
0.169379
1.297247
1.557633
Log likelihood
-366.9683
53.96405
51.97909
44.15720
Akaike AIC
20.10640
-2.646705
-2.53941
-2.116605
Schwarz SC
20.32409
-2.429014
-2.321719
-1.898914
D(FDI_UN(-1))
D(LNREER_TR(-1))
D(LNNEER_TR(-1))
D(LNEXR_US(-1))
C
t-statistics in [ ] Source: Authorâ&#x20AC;&#x2122;s estimation by using data from UNCTAD and RBI.
The estimated coefficients of unrestricted VAR have been shown from table 4. According to this result there has been even no short run dynamic relationship between the FDI inflow and either type of measures of exchange rate in India. So empirically the FDI as a
SPPG TISS - HYDERABAD
23
determinant of exchange and also converse dynamic relationship are completely oppose theories.
8. Concluding Remarks The paper investigated empirically that the relationship between rising capital inflow in terms of FDI and the equilibrium foreign exchange rate in India during 1970 to 2013. The broad area of the study is relating to the theoretical background of open economy macroeconomics with special emphasis on inflow of foreign capital and its effectiveness on different macroeconomic variables in the era of globalization, where we have got some traditional views, which suggests some positive as well as negative impacts in different conditions specially through the channel of foreign trade and exchange rate. As we deal with time series data, we have examined the stochastic behaviour of the relevant macroeconomic time series. The major findings includes that all of the data series of the variables taken in our study are non-stationary and most of those series are stationary at first differences. We carry out cointegration test followed by VAR. Our empirical results highlight that the increase in the inward FDI flows is not a significant responsible factor of such fluctuations of real exchange rate. Further the study shows that the FDI inflows are not influenced by Indian foreign exchange rate.
The because of the findings may be explained by the way that FDI has not been used in the growth enhancing as well as export promoting sectors in India. Thus, it may be imperative for the government of India to make a policy for attracting FDI in such a way that it could enhance economic growth. Despite FDI has a potential role in economic development, the post-reform development in India which does not follow this kind of hypothesis. Another way of explanation may be that remittance inflow in India is more effective than FDI inflow in total capital inflow in India and use of remittances in tradable sectors is remarkably less than another type like FDI. So remittance inflows may be a significant factor of Indian foreign exchange rate.
SPPG TISS - HYDERABAD
24
It is thus imperative for the union government to create the preconditions for FDI to flow in and work its wonders. A liberal and competitive investment climate creates the basis for FDI to enter and raise the potential for productivity growth in the host economy, but improvements will only occur if the domestic actors are capable of responding to the new incentives. The key policy measures are thus to improve the education and infrastructure so as to increase the domestic absorptive capacity of the fruits of FDI. For FDI to be a noteworthy provider to economic growth, India would do better by focusing on improving infrastructure, human resources, developing local entrepreneurship, creating a stable macroeconomic framework and conditions favorable for productive investments to augment the process of development.
SPPG TISS - HYDERABAD
25
Bibliography Acosta, P. Lartey, E. and Mandelman, F. (2007). “Remittances and the Dutch Disease”, Working Paper, Federal Reserve Bank of Atlanta. Acosta, P. Calderón, C. Fajnzylber, P. and López, H. (2008). “What is the Impact of International Migrant Remittances on Poverty and Inequality in Latin America?”, World Development, Vol. 36, No. 1, pp. 89-114. Adams, R. and Page, J. (2005). “Do International Migration and Remittances Reduce Poverty in Developing Countries?”, World Development, Vol. 33, No. 10, pp. 1645-1669. Aliber, R. Z. (1970). “A theory of foreign direct investment”, in Kindleberger, C.P. (Ed.), The International Corporation, MIT Press, Cambridge, MA. Aizenman, J. (1992). “Exchange rate flexibility, volatility and patterns of domestic and foreign direct investment”, IMF Staff Paper 39, pp. 890-922. Aizenman, J. and Marion, N. (2004). “The merits of horizontal versus vertical FDI in the presence of uncertainty”, Journal of International Economics, Vol. 62, pp. 125-148. Akaike, H. (1969). “Fitting autoregressive models for prediction”, Annals of the Institute of Statistical Mathematics, Vol. 21, pp. 243-47. Amuedo-Dorantes, C. and Pozo, S. (2004). “Workers’ Remittances and the Real Exchange Rate: A Paradox of Gifts”, World Development, Vol. 32, No. 8, pp. 1407-1417. Baffes, J., Elbadawi, I. A. and Connel, O’S. A. (1999). “Single Equation estimation of the Equilibrium Real Exchange Rate, in Exchange Rate Misalignment: Concepts and measurement for developing countries”, (Eds.) P. J. Montiel, L. E. Hinkle, New York, Oxford University Press, pp. 405-464. Balassa, B. (1964). “The Purchasing Power Parity Doctrine: A Reappraisal”, Journal of Political Economy, Vol. 72, No. 6, pp. 584-596. Benassy-Quere, A., Fontagne, L. and Lahreche-Revil, A. (2001). “Exchange rate strategies in the competition for attracting foreign direct investment”, Journal of Japanese and International Economics, Vol. 15, No. 2, pp.178-198. Bhaumik, S. K. (2015). “Principles of Econometrics”, Oxford University Press.
SPPG TISS - HYDERABAD
26
Biswas, S. and Dasgupta, B. (2012). “Real Exchange Rate Response to Inward Foreign Direct Investment in Liberalized India”, Int. Journal of Economics and Management, Vol. 6, No. 2, pp. 321 – 345. Blonigen, B. A. (1997). “Firm-Specific Assets and the Link between Exchange Rates and Foreign Direct Investment”, American Economic Review, Vol. 87, No. 3, pp.447-465. Calvo, G. A., Leiderman L. and Reinhart C. M. (1996). “Inflows of Capital to Developing Countries in the 1990s,” Journal of Economic Perspectives, Vol. 10, No. 2, pp. 123-139. Campa, J.M., (1993). “Entry by Foreign Firms in the United States under Exchange Rate Uncertainty”, Review of Economics and Statistics, Vol. 75, pp. 614-22. Chakraborty, D., Mukherjee, J. and Sinha, T. (2012). “Is there any Long-run Relationship between India’s Current and Capital Account Balance? A Time Series Analysis”, Global Business Review, Vol. 13, No. 3, pp. 433-447. Chakrabarti, R. and Scholnick, B. (2002). “Exchange Rate Expectations and Foreign Direct Investment Flows”, Weltwirtschaftliches Archiv, Vol. 138, No. 1, 1-21. Corden, W. M. (1960). “The Geometric Representation of Policies to Attain Internal and External Balance”, Review of Economic Studies, Vol. 28, No. 1, pp. 1-22. Corden, W. M. (1994). “Economic Policy, Exchange Rates and the International System”, Oxford University Press, Oxford. Cushman, D.O. (1985). “Real Exchange Rate Risk, Expectations, and the Level of Direct Investment”, Review of Economics and Statistics, Vol. 67, pp. 297-308. Dickey, D. and W. Fuller. (1981). “The likelihood ratio statistic for autorregresive time series with a unit roots”, Econométrica, Vol. 49, pp. 1057-1072. Dixit, A. and Pindyck, R. (1995). “The optimal approach to capital investment”, Harvard Business Review, Vol. May/Jun, pp. 105-15. Dixit, A. and Pindyck, R. (1994), “Investment under Uncertainty”, Princeton University Press, Princeton, NJ. Devereux, M.B. and Engel, C. (2001). “The optimal choice of exchange rate regime: Price-setting rules and internationalised production”, Working Paper No. 6992, NBER. Department of Industrial Policy and Promotion (DIPP), Government of India.
SPPG TISS - HYDERABAD
27
Dornbusch, R. (1980). “Home Goods and Traded Goods: The Dependent Economy Model”, Open Economy Macroeconomics, Basic Books, New York, NY, Chapter 6. Economic Survey, Govt. of India, 2009-10, 2010-11, 2011-12, 2012-13, 2013-14. Edwards, S. (1989). “Real Exchange rates, Devaluation and Adjustment: Exchange rate?”, Policy in Developing countries, MIT Press: Cambridge, Massachusetts. Edwards, S. (1994). “Real and Monetary Determinants of Real Exchange Rate Behaviour: Theory and Evidence from Developing Countries, in Estimating Equilibrium Exchange Rates”, (Eds) J. Williamson, Washington D.C. Institute for International Economics, pp. 61-91. Edwards, S. (2000). “Capital Flows, Real Exchange Rates and Capital Controls: Some Latin American Experiences,” in Sebastian Edwards (ed), Capital Flows and the Emerging Economies: Theory, Evidence and Controversies, The University of Chicago Press, Chicago. Elbadawi, Ibrahim. A., and Soto, R. (1997). “Real Exchange Rates and Macroeconomic Adjustment in Sub-Saharan African and other Developing Countries”, Journal of African Economics, Vol. 6, No. 3, pp. 74-120. Engle, R. F. and Granger, C. W. (1987). “Cointegration and Error Correction: Representation, Estimation and Testing”, Econometrica, Vol. 55, No. 2, pp. 251-76. Froot, K. A. and Jeremy, C. S. (1991). “Exchange rate and Foreign Direct Investment: A Imperfect Capital Market Approach”, The Quarterly Journal of Economics, Vol. 196, pp. 1191-1218. Goldberg, L.S. and Kolstad, C.D. (1995). “Foreign direct investment, exchange rate volatility and demand uncertainty”, International Economic Review, Vol. 36, pp. 855-873. Granger, C. (1981). “Some Properties of Time Series Data and their use in Econometric Model Specification”, Journal of Econometrics, 16, 121-30. Hand Book of Statistics on Indian Economy, (2006 to 2014), Reserve of India. Hasnat, B. (1999). “Exchange Rate Misalignment and Foreign Direct Investment”, Atlantic Economic Journal, Vol. 27, No. 3. Itagaki, T. (1981). “The theory of the multinational firm under exchange rate uncertainty” Canadian Journal of Economics, Vol. 14, pp. 276-97.
SPPG TISS - HYDERABAD
28
Johansen, S. and Juselius, K. (1990). “Maximum Likelihood Estimation and Inference on Cointegration with applications to the Demand for Money”, Oxford Bulletin of Economics and Statistics, Vol. 52, pp. 169-210. Klein, M.W. and Rosengren, E.S. (1994). ‘The Real Exchange Rate and Foreign Direct Investment in the United States: Relative Wealth vs. Relative Wage Effects’, Journal of International Economics, Vol. 36, pp. 373-389. López, H., Molina, L., and Bussolo, M. (2007). “Remittances and the Real Exchange Rate”, World Bank Policy Research Working Paper No. 4213. Montiel, P. J. (1999). “Determinants of the Long-Run Equilibrium Real Exchange Rate: An Analytical Model, in Exchange Rate Misalignment, Concepts and Measurements for Developing Countries (Eds)”, L. Hinkle, P. A. Montel, A World Bank Research Publication, Oxford University Press, pp. 219-263. Maddala, G. S. and Kim, I. M. (1998). “Unit Roots, Cointegration, and Structural Change”, Cambridge University Press, UK. Nelson, C. R. and C. I. Plosser, (1982). “Trends and Random Walks in Macroeconomic Time Series: Some Evidence and Implications”, Journal of Monetary Economics, Vol. 10, pp. 139-62. Perron, P. (1990). “Testing for a unit root in a time series with a changing mean”, Journal of Business and Economic Statistics, Vol. 8, pp. 153-162. Pindyck, R.S. (1998), “Irreversible investment, capacity choice, and the value of the firm”, American Economic Review, Vol. 78, pp. 969-985. Prakash, S. and Balakrishnan, B. (2006). “Managerial Approach to Conceptualization of Development and Growth-Convergence of Macro to Micro Theory”, Business Perspectives, Vol. 8, No. 1. Rao, K. S. C. and Dhar, B. (2011). “India’s FDI Inflows- Trend and Concepts”, Working Paper No. 01, Institute for Studies in Industrial Development. Razin, O. and Collins, S. M. (1997). “Real Exchange Rate Misalignments and Growth, in the Economics of Globalization: Policy Perspectives from Public Economics (Eds)”, A. Razin, E. Sadka, Cambridge University Press, Cambridge, pp. 59-81. Rivoli, P., Salorio, E., (1996). “Foreign direct investment under uncertainty”, Journal of Business Studies, Second Quarter, pp. 335-357
SPPG TISS - HYDERABAD
29
Rodriguez, C. A. (1989). “Macroeconomic policies for structural adjustment”, World Bank Working Paper Series No. 247. Rodrik, D. (2007). “The Real Exchange Rate and Economic Growth: Theory and Evidence”, Mimeo, Harvard University. Russ, K. N. (2007), “The endogeneity of the exchange rate as a determinant of FDI: A model of entry and multinational firms”, Journal of International Economics, Vol. 71, No. 2, pp. 344-372. Salter, W. E. G. (1959). “Internal and External Balance: The Role of Price and Expenditure Effects”, Economic Record, Vol. 35, No. 71, pp. 226-238. Sargan, J.D. (1984). “Wages and Prices in the United Kingdom: A study in Econometric Methodology”, in K.F. Wallis and D.F. Hendry, eds., Quantitative Economics and Econometric Analysis, Basil Blackwell, Oxford, U.K. Schwert, W. (1989). “Tests for Unit Roots: A Monte Carlo Investigation”, Journal of Business and Economic Statistics, Vol. 7, No. 147-59. Shah, A. and Patnaik, I. (2005). “India’s experience with capital flows: The Elusive quest for a sustainable current account deficit”, Working Paper No. 11387, National Bureau of Economic Research. Sinha, M. (2014). “Impact of Foreign Direct Investment on International Trade: A case Study of India”, presented at 51st Annual Conference of The Indian Econometric Society (TIES) organized by Department of Economics, Punjabi University, Patiala, 12-14 December, 2014. Sinha, M. (2015). “FDI Inflow and Indian Trade Performance: An Empirical Analysis”, presented at Two-Day International Seminar on “Contemporary Issues of Economic Development with special reference to Public Finance and Policy” organized by the Department of Economics with Rural Development, Vidyasagar University jointly with CTRPFP (CSSSC), 17-18 February, 2015. Swan, T. (1961). “Economic Control in a Dependent Economy”, Economic Record, 36, No. 73, pp. 51-66.
Vol.
United Nations Conference on Trade and Development (UNCTAD) (2015). “World Investment Report 2015, Reforming International Investment Governance”, Geneva. Williamson, J. (1994). “Estimating Equilibrium Exchange Rates”, Institute for International Economics, Washington, D.C.
SPPG TISS - HYDERABAD
30
Editorial Board Co-ordinator: Shreya Dixit Editors: Abhay Yadav, Abhishek Acharya, Akheela Ashraf, Ashwin Jangalapalli, Gopal Gajbhiye, Krishna Teja Inapudi, Rajasindhura Aravalli, Rajeev Agur, and Yeshwanth Kumar
SPPG TISS - HYDERABAD
31