F:\E\ISLAMICUS\2020\NO. 3\MACROECONOMIC INDICATORS CONTRIBUTING TOWARDS.pmd Hamdard Islamicus Vol. XLIII, No. 3 61 MACROECONOMIC INDICATORS CONTRIBUTING TOWARDS EXCHANGE RATE VOLATILITY: EVIDENCE FROM INCOME GROUPS OF THE COUNTRIES AMJAD FAKHER RANA EJAZ ALI KHAN Department of Economics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan. The focus of the study is to explore the macroeconomic factors contributing towards exchange rate volatility in group of economies segregated on the basis of income. Further analysis is extended to identify the role of underlying macroeconomic indicators in explaining the exchange rate behavior at level. The Panel ARDL cointegration and Panel Granger Causality Test are employed on annual data sets for the period 1995-2015. The results of the separate analysis have been compared in first place on the basis of determinants of exchange rate volatility and the determinants of exchange rate level and secondly on the basis of income groups of the countries (high income, middle income and low income). The results provide the evidence that exchange rate behaviour both in terms of volatility and at level is entirely governed by the selected set of macroeconomic indicators in all three panel data sets. The findings of the study validated the fundamentals based macroeconomic models of exchange rate determination and suggested that macroeconomic fundamentals played important role in derivation of exchange rate volatility as well as exchange rate level. Moreover response of exchange rate volatility and exchange rate level to the different macroeconomic indicators has been observed very sensitive to the income level of the countries. The comparison of the factors of exchange rate volatility and exchange rate level will enhance exposure about the policies concerned about exchange rate behaviour. Keywords: Exchange Rate, Exchange Rate Volatility, Macroeconomic Indicators, High Income Countries, Middle Income Countries, Low Income Countries, PMG, Panel Granger Causality JEL Codes F31, E31, E47, E01, N1 62 Macroeconomics Indicators Contributing... Introduction The theoretical and empirical literature asserted on the existence of linkages between exchange rate fluctuations and fundamental macroeconomic indicators. However as per research literature the role of macroeconomic variables in determination of exchange rate dynamics lacked general consensus. Moreover most of the previous research had been devoted to determine the factors that explain and forecast exchange rate equilibrium at level. However a few number of studies have tried to investigate the relationship between exchange rate volatility and macroeconomic indicators Thus it is relevant to see whether volatility in exchange rates is just the result of overshooting more than the changes in the macroeconomic fundamentals. The theory has proposed several structural models that assume the patrons of exchange rate changes affected by macroeconomic indicators like portfolio balance models and monetary exchange rate models. In this respect most influential and mostly used exchange rate models are the monetary models of the exchange rate determination that depend on principle of interest rate parity and the purchasing power parity. The models which are designed to forecast and estimate exchange rate movements in relation to market fundamentals are called fundamentals based exchange rate models. However, Meese and Rogoff (1983) empirically established that exchange rate models based on fundamental macroeconomic variables failed to outperform the random walk models. The missing link between exchange rate and macroeconomic variables as identified by Meese and Rogoff (1983 a,b) got fame in theory as exchange rate disconnect puzzle. Thus, in any case it is worth mentioning that influence of macroeconomic indicators on exchange rate fluctuations is undoubtedly an open issue and controversial as well. Another factor regarding the varying results in this regard is that there exist a number of approaches of exchange rate determination which are suitable in one way and criticized in the other way. Exchange rate as variable itself is proxied by number of ways like in real terms, nominal terms, in terms of volatility, as ratio of domestic currency to foreign currency, as ratio of foreign currency to domestic currency. Withal, selection of macroeconomic variables in this respect varies (Ricci et al., 2013; Rapetti, Skott & Razmi, 2012; Ojo & Allege, 2014). The selection of macroeconomic variable in first place depend on the theory, secondly on the model selection (model being selected for analysis) and finally on the area of study. Besides all of this the main matter of Hamdard Islamicus Vol. XLIII, No. 3 63 research remained confined to the question that which macroeconomic variables drive exchange rate behaviour. In this context the current study is aimed at investigating the determinants of exchange rate volatility and exchange rate level for panel data sets of advanced countries and least developed countries. The study will contribute to the empirical literature by incorporating the exchange rate volatility to the conventional open economy macroeconomic models of exchange rate determination. Moreover, it will try to fill the research gap regarding comparative analysis of the determinants of exchange rate volatility and determinants of exchange rate level in cross country perspective. Further the econometric issues regarding application of panel data methodology have been tried to overcome by application of multiple methodologies with different econometrical assumptions. Literature Review Most of the theoretical and empirical literature regarding exchange rate behavior is subject to determine the macroeconomic fundamentals of exchange rate level. It has created a kind of research gap in investigating the underlying variables which influence the exchange rate volatility. Generally the empirical research regarding exchange rate behaviour can be divided into three major categories with respect to application of different approaches of exchange rate determination. In first place there come empirical studies that have applied monetary models. These studies used monetary fundamentals as the factors of exchange rate level (Frenkel, 1976; Hodrick,1978; Bilson, 1978). Second stream of empirical studies applied optimum currency areas in a way that these studies incorporate trade linkages, economic shocks to output, geographical factors and country size as the determinants of exchange rate level (Aydin, 2010; Prabheesh, Malathy & Madhumathi, 2007; Rapach & Wohar, 2002). Respectively, third category of studies used new open economy macroeconomic theory wherein monetary and non-monetary indicators are modeled to estimate the exchange rate changes (Engel & West, 2005; Kearney & MacDonald, 1990; Mark & Sul, 2001). By the time as concept of variance of exchange rate is getting weightage the researchers has shifted their attention to measure the elements contributing towards volatility. However, literature has identified a wide range of factors of determination of exchange rate by varying techniques, using different kinds of data sets for different economies and group of economies. For 64 Macroeconomics Indicators Contributing... instance, per capita GDP (Cakrani et al., 2013), net oil trade (Aydin, 2010), trade (Choudhri & Khan, 2005; Canzoneri, Cumby & Diba, 1999; Ricci et al., 2013), population growth (Aydin, 2010), remittances (Vitek, 2009; Aydin, 2010), trade openness (Cakrani et al., 2013; Aydin, 2010), foreign aid (Aydin, 2010), government expenditures (Mcpherson & Rakovski, 1998; Ricci et al., 2013), price controls (Ricci et al., 2013), net foeign assets (Vitek, 2009; Ricci et al., 2013; Aydin, 2010), fiscal imbalance (Aydin, 2010), money supply (Kandil & Mirzaie, 2005), foreign direct investment (Christiansen, Prati, Ricci & Tressel, 2009; Ojo & Allege, 2014), foreign assets (Christopoulos et al., 2012; Ricci et al., 2013), inflation (Ramasamy & Abar, 2015; Ojo & Allege, 2014), imports (Benetrix & Lane, 2013; Kemal & Qadir, 2005); exports (Kemal & Qadir, 2005) and financial sector development (Stavarek & Maglietti, 2015). Similarly, the macroeconomic indicators affecting the volatility of exchange rate have been analyzed and identified by a number of studies. They include national output (Calderon, 2004), GDP (Azeez, Kolapo & Ajayi, 2012), economic growth (Ebaidalla, 2014; Arratibel et al., 2011), financial openness (Calderon, 2004), money supply (Bobai et al., 2013), monetary aggregates (Calderon, 2004), central bank independence (Hau, 2002), foreign direct investment (Khraiche & Gaudette, 2013; Bobai et al., 2013), government expenditures (Calderon, 2004), current account deficit (Bobai et al., 2013), terms of trade (Rapetti et al., 2012; Calderon, 2004), trade openness (Hau, 2002; Calderon, 2004; Ebaidalla, 2014), exports (Khan et al., 2014; Zakaria, 2013), imports (Khan et al., 2014) revolution and coups (Hau, 2002), volatility in exports (Supaat, Jiun, Tiong & Robinson, 2003), volatility in imports (Supaat et al., 2003), volatility in money supply (Supaat et al., 2003; Morana, 2009), volatility in output (Supaat et al., 2003; Morana, 2009), volatility in interbank rate (Supaat et al., 2003), volatility in inflation (Morana, 2009), credit (Bobai et al., 2013) exchange rate regimes (Calderon, 2004), exchange rate commitments (Hau, 2002) and inflation (Rapetti et al., 2012; Bobai et al., 2013). A number of studies have given the evidence that there exist the bi-directional causality relationship between exchange rate volatility and macroeconomic indicators. Morana (2009) found bi-directional causality between exchange rate volatility and volatility in output, volatility in inflation and volatility in aggregate demand. Giannellis and Papadopoulos (2011) observed bi-directional causality between exchange rate volatility and interest rate differentials. Arize et al. (2000) found bi-directional causality between exchange rate volatility and exports. Mahmood, Ehsanullah and Hamdard Islamicus Vol. XLIII, No. 3 65 Ahmed (2011) observed bi-directional causality between exchange rate volatility and public investment. Dlamini (2014) observed inflation cause exchange rate volatility. Azid, Jamil, Kousar and Kemal (2005) observed bi-directional causality between real exchange rate and economic growth. Ojo and Allege (2014) observed bi-directional causality between nominal exchange rate and trade openness, inflation and interest rate. Rehman (2014) observed bi-directional causality between exchange rate and total reserves. Madesha et al. (2013) observed bi-directional causality between exchange rate and inflation. Identification of the Research Gap . The literature provides limited evidence on the determinants of exchange rate volatility that has utilized multiple panel data sets and conducted a comparative analysis of the determinants of exchange rate level and exchange rate volatility covering the group of the economies segregated by income. . The exchange rate disconnect puzzle questioned the role of macroeconomic indicators in deriving the exchange rate behaviour. However none of the study has tried to investigate whether the questioned link between exchange rate and macroeconomic variables is sensitive to the choice of exchange rate level and exchange rate volatility as explained variable. . In application of cross country panel econometric analysis some studies assume the coefficient homogeneity (for example application of GMM estimators and panel Granger Causality) while others assume coefficient heterogeneity (for instance panel cointegration techniques) but there are very limited studies which have examined the said relationship in both referred contexts. Research Methodology Model Specification The objective of this study is to identify the relationship between the fundamental macroeconomic indicators and exchange rate volatility as well as exchange rate level. Thus, an open economy macroeconomic model of exchange rate determination is devised. The functional form of the model is given as: 66 Macroeconomics Indicators Contributing... EXRV = f(GDPC, CAB, FDI, FDEV, GEXP, INF, RESERV ,TRADE) ………… (1) EXRL = f(GDPC, CAB, FDI, FDEV, GEXP, INF, RESERV ,TRADE) …………. (2) Where, EXRV = Exchange rate volatility (Volatility series generated by GARCH Model) EXRL = Exchange rate level (Official Exchange Rate level measured as local currency units per US Dollars GDPC = Economic Growth (Measured by GDP per capita) CAB = Current Account Balance FDI = Foreign Direct Investment (Percentage of GDP) FDEV = Financial Development (Credit to Private Sector) GEXP = Government Expenditures (Percentage of GDP) INF = Inflation Rate (Consumer Price Index represented in annual %) RESERV = Total Reserves (Current US Dollars.) TRADE = Trade Openness (Net Trade in goods and services. Data are in current US Dollars) Measuring Volatility Different kinds of distortions like political instability, economic interdependence, rise or fall in oil prices, supply or demand boom or crises, disasters, global or national financial crises and technological advancements caused widespread effects on the financial indicators, such as exchange rates. Thus intensity of these elements is better explained by the volatility in the exchange rate time series rather than that of exchange rate values at current level. The process of capturing the Hamdard Islamicus Vol. XLIII, No. 3 67 shocks and understanding the behaviour of series through driving the variance series is technically called volatility. Through GARCH process the exchange rate series include lagged dependent variable (say EXRt- 1) in the model to capture the elements of heteroskedasticity and autocorrelation and follow the above referred conditionality. In this way GARCH process generate the variance series of exchange rate that is called exchange rate volatility. The exchange rate volatility has been measured by simple GARCH (1,1) model in this study1. Estimation Techniques The current research study has selected three distinct panel data sets of high income countries, middle income countries and low income countries to estimate the connotation of the said relationship further Panel Auto Regressive Distributive Lag (ARDL) and Panel Granger Causality test have been applied for the analysis. Now a days, it has become a tradition to apply the cross country panel data frame work to different theoretical propositions. Panel data estimation, technically, helped to tackle more complex issues that cannot be considered by purely time series analysis. Panel data application allow individual heterogeneity among different units of the data set, moreover, it can remove the effects of omitted variables which are particular to the individual cross section units but remain constant over time. Another advantage of panel data application is that the combination of time series and cross section dimensions in panel data lead to increase the degree of freedom that lead to resolve the issues of multicollinearity. Panel Cointegration The dynamic time series analysis cannot ignore the issue of non stationarity. Three distinct types of unit root tests being differentiated on the basis of their statistical procedure, namely Im, Pesaran and Shin test (IPS), Hadri Langrange Multiplier test (HLM) and Levin, Lin and Chu test (LLC) are applied to check the stationarity. Modeling the Panel Cointegration The long run information in time series can be intact through the process of cointegration. Cointegration not only facilitate in the course of 1. It is followed from the studies (Ebaidalla, 2014; Supaat et al., 2003; Udoh et al., 2012; Yunana et al., 2016). 68 Macroeconomics Indicators Contributing... analysis of long run association among the considered integrated variables, moreover, it reparameterize the relationship into Error Correction Model. This idea was formulized by Granger (1981) and later on by Engle and Granger (1987) along with the estimation procedure and specification test. Panel ARDL Approach Pesaran, Shin and Smith. (1999) incorporated the dynamic heterogeneous panel regression into the error correction model by applying the Auto Regressive Distributive Lag, ARDL (p,q) methodology. Specifically, in cross country analysis where the long run coefficients and the speed of convergence towards the long run are parameters of interest, the panel ARDL methodology is assumed as best choice. An important advantage of the Panel ARDL cointegration methodology is that it can be applied even if the selected variables have different order of integration. In other words, whether variables under consideration are I(0) or I(1). Moreover, both the short run and long run relationship among the selected variables can be estimated simultaneously from the panel data set with large time dimensions and large cross sections. Causal Relationship In addition to understanding the exchange rate behaviour in response to its macroeconomic factors, the potential causal association between exchange rate and macroeconomic factors may be important to cover the scope of the research. In this respect application of Granger Causality test is considered to test the direction of causality among the selected variables. Data Source The annual data set for the panels of high income countries, middle income countries and low income countries covering the time period from 1995 to 2015 is taken from World Development Indicators (WDI) and OECD Countries Data. The countries having gross national per capita income 12376 dollars or more are taken as high income countries. The countries with GNI per capita between 1026 and 12375 dollars are classified as middle income countries and the countries with GNI per Hamdard Islamicus Vol. XLIII, No. 3 69 capita 1025 dollars or less have been selected as low income countries. Further in order to fill the missing values, the data have been retrieved from the official websites of the selected countries, especially websites of concerned country’s central banks and statistical bureaus. Results and Discussions Estimation Results for Panel Data Set of High Income Countries Panel ARDL Results for High Income Countries The result of three panel unit root tests for panel data set of high income countries have been represented in Table 1. The test results indicate that some variables are stationary at level while some are stationary at first difference. The situation allows the application of panel ARDL only. The results of Panel ARDL estimation are shown in Table 2. This technique has an advantage that it does not assume homogeneity of the coefficients. Further it allows the short run parametric coefficients to differ across the cross-sections, however it computes common long run coefficients. The estimation results of exchange rate volatility function for the high income countries shown in table 2 established that there exists a strong long run relationship between exchange rate volatility and the selected set of macroeconomic indicators. Whereas error correction term shows the speed of adjustment which shows value of the coefficient of convergence is -0.1731, that is statistically significant and negative, which, clearly indicates no omitted variable bias. The long run results explain that GDPC, CAB, FDI, FDEV and GEXP have negative impact on exchange rate volatility. Further impact of INF, RESERV and TRADE has been observed positive on EXRV in long run (the findings are in line with Bobai et al., 2013; Devereux & Lane, 2003; Esquivel & Larrain, 2002; Grydaki & Fountas, 2009). Further RESERV and TRADE are found to have positive contribution in EXRV in long run and direction of relationship is not as expected. Most probable justification of the positive long run relationship between total reserves and exchange rate volatility is that high income countries maintained a high level of total reserves and further increase in total reserves causes increase in exchange rate volatility (see for instance, Grossmann, Love & Orlov, 2014). Likewise increase in trade openness also results increase in exchange rate volatility in long run (Ebaidalla, 2014; Kilicarslan, 2018). 70 Macroeconomics Indicators Contributing... Table. 1 Results of Panel Unit Root Tests for High Income Countries Variables Im, Peseran Levin, Lin and Hadri Z-Statistics and Shin (IPS) Chu (LLC) Level First Level First Level First Diff. Diff. Diff. LEXRV -6.299 - -6.281 - 8.434 3.335 (0.000) 12.361 (0.000) 15.973 (0.009) (0.000) (0.000) (0.000) LEXRL -1.357 -2.519 -0.477 -8.631 7.856 13.464 (0.087) (0.005) (0.000) (0.316) (0.000) (0.000) LGDPC 4.389 -4.889 4.502 - 8.994 12.977 (1.000) (0.000) (0.000) 11.326 (1.000) (0.000) (0.000) LCAB -1.743 - -3.168 - 8.373 10.761 (0.041) 17.988 (0.001) 19.591 (0.000) (0.000) (0.000) (0.000) LFDI - - -9.293 - 4.573 49.664 10.561 23.674 (0.000) 23.291 (0.462) (0.000) (0.000) (0.000) (0.000) LFDEV -0.904 - -3.627 - 9.848 22.767 (0.183) 10.821 (0.000) 13.720 (0.000) (0.000) (0.000) (0.000) LGEXP -1.183 - -2.149 - 12.329 13.230 (0.118) 12.843 (0.015) 16.781 (0.000) (0.000) (0.000) (0.000) LINF -7.406 - -9.579 - 8.582 25.246 (0.000) 19.522 (0.000) 21.051 (0.000) (0.000) (0.000) (0.000) LRESERV 0.411 - -0.338 - 11.470 6.600 (0.659) 13.149 (0.367) 17.022 (0.000) (0.000) (0.000) (0.000) LTRADE -0.257 - -1.981 - 12.660 7.317 (0.397) 14.218 (0.023) 15.955 (0.000) (0.000) (0.000) (0.000) Hamdard Islamicus Vol. XLIII, No. 3 71 Note: Values in parentheses are estimated p values. The options used are: individual trend and intercept, lag length chosen by Schwarz automatic selection (Schwarz, 1978), Kernel method of Bartlett (Bartlett, 1948) obtained by special estimation and Bandwidth selection by automatic Newey-West (Newey & West, 1994) Table. 2 Results of Pooled Mean Group Regression of ARDL for Exchange Rate Behaviour in High Income Countries Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics Long Run Results LGDPC -0.0406 -4.85* -0.8731 -15.38* (0.000) (0.000) LCAB -0.0196 -5.02* 0.0633 3.84* (0.000) (0.000) LFDI -0.0119 -5.99* 0.0882 7.53* (0.000) (0.000) LFDEV -0.0317 -1.66*** 0.0035 0.14 (0.098) (0.886) LGEXP -0.3244 -3.81* -0.7850 -6.13* (0.000) (0.000) LINF 0.0503 5.75* 0.1258 6.06* (0.000) (0.000) LRESERV 0.0265 4.93* 0.4193 11.72* (0.000) (0.000) LTRADE 0.0032 5.53* 0.0136 7.50* (0.000) (0.000) Short Run Results ECT -0.1732 -4.93* -0.0141 -1.27 (0.000) (0.205) 72 Macroeconomics Indicators Contributing... Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics LGDPC -0.4759 -1.79*** -0.6523 -12.95* (0.074) (0.000) LCAB -0.0185 -0.28 0.0011 0.21 (0.782) (0.832) LFDI 0.0281 0.98 0.0012 0.70 (0.327) (0.483) LFDEV 0.0958 0.45 0.0216 0.76 (0.656) (0.450) LGEXP -0.2822 -0.34 -0.2055 -4.90* (0.735) (0.000) LINF -0.0234 -0.92 0.0094 3.35* (0.355) (0.001) LRESERV 0.0145 0.17 -0.0107 -1.12 (0.867) (0.264) LTRADE -0.0392 -0.41 0.0084 1.32 (0.679) (0.188) Number of Groups 44 44 No. of Observations 880 880 Log Likelihood 2078.526 2791.97 Note: *, ** and *** represent 1, 5 and 10 percent level of significance respectively. Values in parentheses are estimated p values. The results represented on right hand side in Table 2 are the panel ARDL estimates for exchange rate level function. The results show strong long run association between the exchange rate level and macroeconomic indicators as all the independent variables are significant at level of one percent except financial development. Hamdard Islamicus Vol. XLIII, No. 3 73 Table. 3 Panel Granger Causality Test Results for Exchange Rate Volatility (High Income Countries) Causality Unidirectional No Causality No Causality No Causality No Causality Bidirectional Unidirectional No Causality Obser- vations 836 836 836 836 836 836 836 836 F-Stat 5.24671 0.64414 1.89551 0.62291 0.95645 0.53669 0.09725 0.09906 2.05246 0.04025 7.57219 4.25401 2.25428 4.32820 0.42516 0.71823 Probability 0.0054 0.5254 0.1509 0.5366 0.3847 0.5849 0.9073 0.9057 0.1291 0.9605 0.0006 0.0145 0.1056 0.0135 0.6538 0.4879 Decision Reject Accept Accept Accept Accept Accept Accept Accept Accept Accept Reject Reject Accept Reject Accept Accept Null Hypothesis LGDPC does not Granger Cause LEXRV LEXRV does not Granger Cause LGDPC LCAB does not Granger Cause LEXRV LEXRV does not Granger Cause LCAB LFDI does not Granger Cause LEXRV LEXRV does not Granger Cause LFDI LFDEV does not Granger Cause LEXRV LEXRV does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRV LEXRV does not Granger Cause LGEXP LINF does not Granger Cause LEXRV LEXRV does not Granger Cause LINF LRESERV does not Granger Cause LEXRV LEXRV does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRV LEXRV does not Granger Cause LTRADE 74 Macroeconomics Indicators Contributing... However the effect of GDPC and GEXP on EXRL observed negative and effect of CAB, FDI, INF, RESERV and TRADE observed positive (Calderon, Chong & Loayza, 2002; Eichengreen, 2007; Rashidin, Ullah & Jehangir, 2017; Saeed, Awan, Sial, & Sher, 2012). However negative relationship between EXRL and GDPC and GEXP, whereas, positive relationship between EXRL and INF in high income countries is against the theory (the findings of the study are in line with, Cavallo, Cottani & Kahn, 1990; Ojo & Allege, 2014; Ramasamy & Abar, 2015). One of the main reasons of this theoretical deviation might be the peculiar nature of panel data set of high income countries. All the countries in this group of countries contained high level of per capita income more than 12376 dollars. Panel Granger Causality Test Results for Exchange Rate Volatility (High Income Countries) The results of pair wise granger causality test are given in Table 3. It is observed that there exists bidirectional causality between EXRV and INF (Ali, Mahmood, & Bashir, 2015). However GDPC observed to affect EXRV but EXRV does not granger cause the GDPC. Further RESERV does not granger cause EXRV but EXRV granger cause the total reserves. The results have established that there exists a weak relationship between exchange rate volatility and the macroeconomic indicators. The Results of Panel Granger Causality Test for Exchange Rate Level (High Income Countries) The Table 4 shows the pair wise panel granger causality estimates of exchange rate level function for the panel date set of high income countries. The results indicate bidirectional causation between EXRL and CAB and RESERV (Rehman, 2014). There exists unidirectional causality between EXRL and GEXP. Likewise, TRADE granger cause the exchange rate level but exchange rate level does not cause the changes in trade openness (Vijayakumar, 2014; Yaya & Lu, 2012). The results show the possibility of cause and effect relationship between exchange rate level and macroeconomic indicators in high income countries but the relationship did not seem stronger. Hamdard Islamicus Vol. XLIII, No. 3 75 Table. 4 Panel Granger Causality Test Results for Exchange Rate Level (High Income Countries) Null Hypothesis LGDPC does not Granger Cause LEXRL LEXRL does not Granger Cause LGDPC LCAB does not Granger Cause LEXRL LEXRL does not Granger Cause LCAB LFDI does not Granger Cause LEXRL LEXRL does not Granger Cause LFDI LFDEV does not Granger Cause LEXRL LEXRL does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRL LEXRL does not Granger Cause LGEXP LINF does not Granger Cause LEXRL LEXRL does not Granger Cause LINF LRESERV does not Granger Cause LEXRL LEXRL does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRL LEXRL does not Granger Cause LTRADE Obser- vations 836 836 836 836 836 836 836 836 Decision Accept Accept Reject Reject Accept Accept Accept Accept Accept Reject Accept Accept Reject Reject Reject Accept F-Stat 2.13804 1.36063 5.09020 3.24792 1.71862 0.15544 0.17425 0.72136 1.80705 2.57080 0.54338 1.64994 14.2037 6.17514 3.13850 2.07803 Probability 0.1185 0.2571 0.0064 0.0393 0.1800 0.8561 0.840 0.486 0.164 0.077 0.581 0.192 0.000 0.002 0.043 0.125 Causality No Causality Bidirectional No Causality No Causality unidirectional No Causality Bidirectional unidirectional 76 Macroeconomics Indicators Contributing... Estimation Results for Panel Data Set of Middle Income Countries Panel ARDL Results for Middle Income Countries The results of Panel unit rest tests are represented in Table 5 for middle income countries. The results show that some variables have unit root in the time series at level while others have unit root in the time series at the first difference. Table. 5 Results of Panel Unit Root Tests for Exchange Rate Volatility (Middle Income Countries) Variables Im, Peseran Levin, Lin and Hadri Z-Statistics and Shin (IPS) Chu (LLC) Level First Level First Level First Diff. Diff. Diff. LEXRV 0.705 -22.337 0.023 - 14.994 11.283 (0.759) (0.000) (0.509) 19.417 (0.000) (0.000) (0.000) LEXRL -4.237 -13.493 -2.593 - 16.622 16.314 (0.000) (0.000) (0.004) 41.041 (0.000) (0.000) (0.000) LGDPC 0.829 -8.106 -0.743 - 11.207 15.776 (0.796) (0.000) (0.228) 14.374 (0.000) (0.000)\ (0.000) LCAB -5.038 -21.747 -6.775 - 13.091 12.032 (0.000) (0.000) (0.000) 20.877 (0.000) (0.000) (0.000) LFDI -7.518 -27.920 -7.497 - 10.415 11.223 (0.000) (0.000) (0.000) 27.860 (0.000) (0.000) (0.000) LFDEV 1.182 -16.813 -0.341 - 15.321 9.859 (0.881) (0.000) (0.366) 19.506 (0.000) (0.000) (0.000) LGEXP -3.295 - -2.795 - 13.517 16.119 (0.000) 25.3452 (0.002) 26.364 (0.000) (0.000) (0.000) (0.000) Hamdard Islamicus Vol. XLIII, No. 3 77 LINF - -28.292 - - 16.659 20.370 14.333 (0.000) 17.367 27.993 (0.000) (0.000) (0.000) (0.000) (0.000) LRESERV 2.251 -18.826 2.767 - 11.771 13.783 (0.987) (0.000) (0.997) 21.899 (0.000) (0.000) (0.000) LTRADE -0.501 -18.864 1.949 - 10.628 11.677 (0.691) (0.000) (0.974) 19.768 (0.000) (0.000) (0.000) Note: Values in parentheses are estimated p values. The options used are: individual trend and intercept, lag length chosen by Schwarz automatic selection (Schwarz, 1978), Kernel method of Bartlett (Bartlett, 1948) obtained by special estimation and Bandwidth selection by automatic Newey-West (Newey & West, 1994) The long run and short run results of Pooled Mean Group (PMG) regression with dependent variable exchange rate volatility are given in Table 6 on left hand side. The long run results explain that the exchange rate volatility in middle income countries is entirely determined by the macroeconomic indicators. The macroeconomic variables GDPC and INF have positive effect on EXRV whereas CAB, FDEV, FDI, GEXP and RESERV have negative long run impact on the exchange rate volatility (The findings are in line with Ali et al., 2015; Arabi, 2012; Calderon, 2004; Hviding, Nowak & Ricci, 2004; Kilicarslan, 2018; Serven, 2003). The results of Pooled Mean Group (PMG) estimation of exchange rate level function for panel data set of middle income countries are given on right hand side in table 6. The long run results show strong influence of macroeconomic variables in determination of exchange rate level. The results show that CAB, GEXP and INF have negative impact on the exchange rate movements in the long run. On the other hand impact of GDPC, FDEV and RESERV have been observed statistically significant and positive on the exchange rate level (Chowdhery & Hossein, 2014; Eichengreen, 2007; Iavorschi, 2014; Ojo & Allege, 2014; William & Parsad, 2019). However negative impact of CAB and GEXP on EXRL in middle income countries is against the theory. One of the major reason of this theoretical deviation is that middle income countries are facing persistent current account deficits further governments in these countries are spending more on non-tradable goods (see for instance, Cakrani et 78 Macroeconomics Indicators Contributing... al., 2013; William & Parsad, 2019). Further, the short run analysis shows that impact of GDPC, FDEV and GEXP is negative on exchange rate level and impact of inflation observed positive on exchange rate level. Table. 6 Results of Pooled Mean Group Regression of ARDL for Exchange Rate Behaviour in Middle Income Countries Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics Long Run Results LGDPC 1.9799 10.75* 0.5240 7.05* (0.000) (0.000) LCAB -0.0769 -2.41** -0.3308 -9.01* (0.016) (0.000) LFDI -0.0893 -1.65*** 0.0246 0.73 (0.098) (0.466) LFDEV -0.5446 -4.60* 0.2356 4.35* (0.000) (0.000) LGEXP -1.1526 -4.33* -0.1383 -2.17** (0.000) (0.030) LINF 0.2837 5.34* -0.3016 -10.38* (0.000) (0.000) LRESERV -1.2648 -13.07* 0.1442 3.51* (0.000) (0.000) LTRADE 0.0075 1.26 -0.0063 -1.20 (0.208) (0.231) Short Run Results ECT -0.2414 -5.68* 0.0000 0.14 (0.000) (0.891) LGDPC -1.3417 -1.90** -0.6381 -14.15* (0.057) (0.000) Hamdard Islamicus Vol. XLIII, No. 3 79 Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics LCAB -0.1361 -1.60 0.0047 0.87 (0.109) (0.385) LFDI 0.0517 0.44 0.0015 0.46 (0.658) (0.648) LFDEV -0.4440 -0.98 -0.0881 -3.50* (0.326) (0.000) LGEXP -0.2535 -0.25 -0.1260 -3.73* (0.799) (0.000) LINF -0.0181 -0.23 0.0192 2.90* (0.819) (0.004) LRESERV -0.2804 -1.24 -0.0186 -1.33 (0.217) (0.183) LTRADE 0.3587 1.63 0.0010 0.13 (0.103) (0.897) Number of Groups 68 68 No. of Observations 1360 1360 Log Likelihood -488.1488 3088.973 Note: *, ** and *** represent 1, 5 and 10 percent level of significance respectively. Values in parentheses are estimated p values. Panel Granger Causality Test Results for Exchange Rate Volatility (Middle Income Countries) The estimation results of pair wise panel granger causality test are given in Table 7. The results indicate existence of bidirectional causality between EXRV and GDPC (the findings are in line with Aliyu, 2009; Morana, 2009). The results show government expenditure does not cause the exchange rate volatility while exchange rate volatility granger cause the government expenditures. 80 Macroeconomics Indicators Contributing... Table. 7 Panel Granger Causality Test Results for Exchange Rate Volatility (Middle Income Countries) Null Hypothesis LGDPC does not Granger Cause LEXRV LEXRV does not Granger Cause LGDPC LCAB does not Granger Cause LEXRV LEXRV does not Granger Cause LCAB LFDI does not Granger Cause LEXRV LEXRV does not Granger Cause LFDI LFDEV does not Granger Cause LEXRV LEXRV does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRV LEXRV does not Granger Cause LGEXP LINF does not Granger Cause LEXRV LEXRV does not Granger Cause LINF LRESERV does not Granger Cause LEXRV LEXRV does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRV LEXRV does not Granger Cause LTRADE Obser- vations 1292 1292 1292 1292 1292 1292 1292 1292 F-Stat 7.07957 2.74529 0.19462 0.14831 1.03883 0.65360 1.87629 0.89030 1.16678 4.93765 0.52850 0.34561 1.94341 0.47213 0.40221 1.52973 Probability 0.0009 0.0646 0.8232 0.8622 0.3542 0.5203 0.1536 0.4108 0.3117 0.0073 0.5896 0.7079 0.1436 0.6238 0.6689 0.2170 Decision Reject Reject Accept Accept Accept Accept Accept Accept Accept Reject Accept Accept Accept Accept Accept Accept Causality Bidirectional No Causality No Causality No Causality Unidirectional No Causality No Causality No Causality Hamdard Islamicus Vol. XLIII, No. 3 81 Thus in case of panel data set of middle income countries the causality analysis provides the evidence of poor relationship between exchange rate volatility and macroeconomic variables. Panel Granger Causality Test Results for Exchange Rate Level (Middle Income Countries) The results of pair wise panel granger causality test have been given in Table 8. The results indicate evidence of bidirectional causality between EXRL and CAB, FDEV, INF, RESERV and TRADE. Further FDI and GEXP do not granger cause EXRL however EXRL cause the changes in FDI and GEXP. Table. 8 Panel Granger Causality Test Results for Exchange Rate Level (Middle Income Countries) Obser- vations 1292 1292 1292 1292 1292 Null Hypothesis LGDPC does not Granger Cause LEXRL LEXRL does not Granger Cause LGDPC LCAB does not Granger Cause LEXRL LEXRL does not Granger Cause LCAB LFDI does not Granger Cause LEXRL LEXRL does not Granger Cause LFDI LFDEV does not Granger Cause LEXRL LEXRL does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRL LEXRL does not Granger Cause LGEXP F-Stat 0.28030 1.53020 17.8314 11.4508 0.48296 3.44935 10.46953 1.6480 0.27709 3.20341 Probability 0.7556 0.2169 0.0000 0.0000 0.6171 0.0321 0.0000 0.0000 0.7580 0.0409 Decision Accept Accept Reject Reject Accept Reject Reject Reject Accept Reject Causality No Causality Bidirectional Unidirectional Bidirectional Unidirectional 82 Macroeconomics Indicators Contributing... Thus the causality analysis provides the evidence of strong cause and effect relationship between the exchange rate level and selected set of macroeconomic indicators (Azid et al., 2005; Khan, Sattar & Rehman, 2012; Ojo & Allege, 2014; Rehman, 2014; Sekmen, 2007) Estimation Results for Panel Data Set of Low Income Countries Panel ARDL Results for Low Income Countries The Panel unit root test results for panel data set of low income countries are given in Table 9. It is the case where selected variables have order of integration I(0) and I(1) thus preferred technique of cointegration is the Panel ARDL. The results of panel ARDL cointegration are given in Table 10. The long run results of the panel ARDL show that exchange rate volatility in long run in low income countries is derived by macroeconomic indicators. The macroeconomic variables CAB, FDI and RESERV effect EXRV negatively while GDPC, GEXP, INF and TRADE influence the volatility positively in long run for panel data set of low income countries (Arabi, 2012; Arratibel et al., 2011; Bobai et al., 2013; Domac & Mendoza., 2004; Hviding et al., 2004). However results indicate weak relationship between exchange rate volatility and macroeconomic indicators in short run. Null Hypothesis LINF does not Granger Cause LEXRL LEXRL does not Granger Cause LINF LRESERV does not Granger Cause LEXRL LEXRL does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRL LEXRL does not Granger Cause LTRADE Obser- vations 1292 1292 1292 Probability 0.0000 0.0000 0.0000 0.0000 0.0602 0.0025 Decision Reject Reject Reject Reject Reject Reject F-Stat 11.5110 16.2884 40.0037 0.84273 2.81594 6.01955 Causality Bidirectional Bidirectional Bidirectional Hamdard Islamicus Vol. XLIII, No. 3 83 Table. 9 Results of Panel Unit Root Tests for Exchange Rate Volatility (Low Income Countries) Variables Im, Peseran Levin, Lin and Hadri Z-Statistics and Shin (IPS) Chu (LLC) Level First Level First Level First Diff. Diff. Diff. LEXRV -2.163 -18.110 -2.302 -20.220 8.519 10.860 (0.015) (0.000) (0.011) (0.000) (0.000) (0.000) LEXRL -5.947 -8.083 -13.364 -13.310 10.736 4.341 (0.000) (0.000) (0.004) (0.000) (0.000) (0.000) LGDPC -2.677 -7.702 -2.779 -9.794 7.575 7.796 (0.004) (0.000) (0.003) (0.000) (0.000) (0.000) LCAB -3.912 -15.972 -4.191 -17.691 2.993 1.461 (0.000) (0.000) (0.000) (0.000) (0.001) (0.072) LFDI -5.519 -15.362 -4.865 -13.788 4.796 4.131 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) LFDEV -3.304 -12.521 -2.752 -12.765 7.691 4.918 (0.000) (0.000) (0.003) (0.000) (0.000) (0.000) LGEXP -5.266 -14.286 -5.637 13.918 8.025 13.201 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) LINF -8.823 -15.955 -9.294 -17.094 2.831 43.762 (0.000) (0.000) (0.000) (0.000) (0.033) (0.000) LRESERV 0.763 -14.432 0.919 -16.644 6.357 9.892 (0.987) (0.000) (0.821) (0.000) (0.000) (0.000) LTRADE -2.446 -12.880 -1.177 -7.696 10.976 4.755 (0.007) (0.000) (0.119) (0.000) (0.000) (0.000) Note: Values in parentheses are estimated p values. The options used are: individual trend and intercept, lag length chosen by Schwarz automatic selection (Schwarz, 1978), Kernel method of Bartlett (Bartlett, 1948) obtained by special estimation and Bandwidth selection by automatic Newey-West (Newey & West, 1994) 84 Macroeconomics Indicators Contributing... Table. 10 Results of Pooled Mean Group Regression of ARDL for Exchange Rate Behaviour in Low Income Countries Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics Long Run Results LGDPC 4.6048 7.00* -1.1011 -3.32* (0.000) (0.001) LCAB -0.9513 -5.79* -0.1519 -2.04** (0.000) (0.041) LFDI -0.7053 -2.87* -0.3104 -3.57* (0.004) (0.000) LFDEV 0.1986 0.54 -0.2174 -1.21 (0.587) (0.225) LGEXP 5.1545 5.60* -2.3254 -5.07* (0.000) (0.000) LINF 0.9355 5.45* -0.3146 -4.43* (0.000) (0.000) LRESERV -0.6769 -2.01** 0.6040 3.69* (0.044) (0.000) LTRADE 0.3118 3.67* -0.0111 -0.86 (0.000) (0.389) Short Run Results ECT -0.420 -6.67* -0.0249 -1.38 (0.000) (0.169) LGDPC -4.3478 -2.97* -0.4633 -6.37* (0.003) (0.000) LCAB 0.3978 0.71 -0.0135 -1.85*** (0.479) (0.064) Hamdard Islamicus Vol. XLIII, No. 3 85 Independent Dependent Variable Dependent Variable Variables LEXRV LEXRL Coefficient Z Coefficient Z Statistics Statistics LFDI 0.2036 1.64 0.0033 0.65 (0.101) (0.514) LFDEV -0.4985 -0.80 -0.060 -1.57 (0.424) (0.116) LGEXP -2.1872 -1.69*** 0.0072 0.06 (0.092) (0.952) LINF 0.320 0.21 0.0241 3.24* (0.836) (0.001) LRESERV 0.333 0.45 -0.0098 -0.41 (0.653) (0.679) LTRADE -0.2395 -0.45 0.0402 2.94* (0.651) (0.003) Number of Groups 23 23 No. of Observations 460 460 Log Likelihood -704.5 801.28 Note: *, ** and *** represent 1, 5 and 10 percent level of significance respectively. Values in parentheses are estimated p values. The estimation results of panel ARDL of functional equation of exchange rate level for panel data set of low income countries are given in Table 10. The long run results show strong influence of macroeconomic variables in determination of exchange rate level in low income countries. The results indicate GDPC, CAB, FDI, GEXP and INF have negative impact while total reserves have positive impact on exchange rate level in long run (Acemoglu, Johnson, Thaicharoen & Robinson, 2003; Aizenman & Riera-Crichton, 2008; Gala, 2007; Kamin & Rogers, 2000; Ojo & Allege, 2014; William & Parsad, 2019). Even though except trade and financial development all the macroeconomic indicators have statistically significant impact on exchange 86 Macroeconomics Indicators Contributing... rate level but surprisingly direction of the relationship is against the theory for majority of the indicators, like GDP per capita, current account balance, foreign direct investment and government expenditures posed negative relationship with exchange rate level in long run for panel data set of low income countries. The low income countries contained deteriorated situation of macroeconomic indicators. These countries have low per capita income as low as less than 1026 dollars, high current account deficits, very low foreign direct investment inflows, backward and underdeveloped financial system, high inflation, low exports, high imports, low level of output and inadequate government expenditures. This is the reason that macroeconomic indicators i.e. GDP per capita, current account balance, foreign direct investment, government expenditures posed to have negative relationship with exchange rate level in low income countries. Panel Granger Causality Test Results for Exchange Rate Volatility (Low Income Countries) The estimation results of pair wise panel granger causality test are given in Table 11. The results indicate that there is no causality between EXRV and FDEV, GEXP and TRADE. Table. 11 Panel Granger Causality Test Results for Exchange Rate Null Hypothesis LGDPC does not Granger Cause LEXRV LEXRV does not Granger Cause LGDPC LCAB does not Granger Cause LEXRV LEXRV does not Granger Cause LCAB LFDI does not Granger Cause LEXRV LEXRV does not Granger Cause LFDI Obser- vations 437 437 437 F-Stat 1.92324 3.58848 2.42596 0.8062 2.71282 0.53075 Probability 0.1474 0.0285 0.896 0.9226 0.0675 0.5885 Decision Accept Reject Accept Accept Reject Accept Causality Unidirectional No Causality Unidirectional Hamdard Islamicus Vol. XLIII, No. 3 87 Further causality between exchange rate volatility and GDP per capita, current account balance, foreign direct investment, inflation and total reserves is unidirectional. Thus in case of panel data set of low income countries the causality analysis provides the evidence of poor relationship between exchange rate volatility and macroeconomic variables. Panel Granger Causality Test Results for Exchange Rate Level (Low Income Countries) The results of pair wise panel granger causality test for exchange rate level function are given in Table 12. The results explain that here exist no causality between EXRL and CAB, FDI and TRADE. The results show unidirectional causality between EXRL and GEXP, INF and reserves (Kasman & Ayhan, 2008; Narayan & Smyth, 2004). Further analysis indicates there exist bidirectional causality between EXRL and GDPC and FDEV (Ojo & Allege, 2014; Sekmen, 2007). Null Hypothesis LFDEV does not Granger Cause LEXRV LEXRV does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRV LEXRV does not Granger Cause LGEXP LINF does not Granger Cause LEXRV LEXRV does not Granger Cause LINF LRESERV does not Granger Cause LEXRV LEXRV does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRV LEXRV does not Granger Cause LTRADE Obser- vations 437 437 437 437 437 F-Stat 0.00671 1.56828 0.68491 0.16380 1.81294 2.35491 1.01964 2.53429 0.70876 0.90158 Probability 0.9933 0.2096 0.5047 0.8490 0.1644 0.0961 0.3616 0.0805 0.4928 0.4067 Decision Accept Accept Accept Accept Accept Reject Accept Reject Accept Accept Causality No Causality No Causality Unidirectional Unidirectional No Causality 88 Macroeconomics Indicators Contributing... Table. 12 Panel Granger Causality Test Results for Exchange Rate Level Low Income Countries Null Hypothesis LGDPC does not Granger Cause LEXRL LEXRL does not Granger Cause LGDPC LCAB does not Granger Cause LEXRL LEXRL does not Granger Cause LCAB LFDI does not Granger Cause LEXRL LEXRL does not Granger Cause LFDI LFDEV does not Granger Cause LEXRL LEXRL does not Granger Cause LFDEV LGEXP does not Granger Cause LEXRL LEXRL does not Granger Cause LGEXP LINF does not Granger Cause LEXRL LEXRL does not Granger Cause LINF LRESERV does not Granger Cause LEXRL LEXRL does not Granger Cause LRESERV LTRADE does not Granger Cause LEXRL LEXRL does not Granger Cause LTRADE Obser- vations 437 437 437 437 437 437 437 437 F-Stat 23.8829 4.00942 0.35372 0.71560 0.67921 1.87044 5.81599 5.71254 7.02454 0.58566 2.065461 0.1887 3.57594 0.67188 0.75667 0.48644 Probability 0.0000 0.0188 0.7023 0.4895 0.5076 0.1553 0.0032 0.0036 0.0010 0.5572 0.1280 0.0000 0.0288 0.5113 0.4698 0.6151 Decision Reject Reject Accept Accept Accept Accept Reject Reject Reject Accept Accept Reject Reject Accept Accept Accept Causality Bidirectional No Causality No Causality Bidirectional Unidirectional Unidirectional Unidirectional No Causality Hamdard Islamicus Vol. XLIII, No. 3 89 The causality analysis provides the evidence of cause and effect relationship between exchange rate level and macroeconomic indicators for panel data set of low income countries. The Main Line of Arguments and Findings Ø There exists strong long run relationship between exchange rate volatility and macroeconomic indicators (Arize, Osang & Slottje, 2008; Calderon, 2004; MacDonald, 1999; Morana, 2009; Udoh et al., 2012). However the overall causality analysis provides the evidence of feeble link between exchange rate volatility and macroeconomic indicators. Ø The study provides the evidence of strong relationship between the exchange rate level and selected macroeconomic variables in long run, short run and causality analysis as well (The findings are in line with Engel & West, 2005; Kim & Mo, 1995; Mark & Sul, 2001; Ojo & Alege, 2014; Ramasamy & Abar, 2015; Ricci et al., 2013). Ø In long run period of time exchange rate volatility and exchange rate level both are substantially influenced by the selected set of fundamental macroeconomic indicators. Thus it is quite justified to say that relationship between exchange rate behaviour (in terms of volatility and at level) and macroeconomic indicators is a long run relationship (The findings are in line with Diamandis et al., 2014; Lee-Lee & Hui-Boon, 2007; MacDonald, 1999; Morana, 2009; Vygodina et al., 2008). Ø The pair wise causality analysis also provide the evidence of missing link between exchange rate volatility and macroeconomic variables, however, the study proved the evidence of the existence of cause and effect relationship between exchange rate level and macroeconomic indicators. (see Ahmad & Ali, 1999; Engel & West, 2005; Dlamini, 2014). Ø The study has proved the validity of fundamentals based macroeconomic models of exchange rate determination and negated the possibility of exchange rate disconnect in the current global analysis not only in case of exchange rate level but also for exchange rate volatility. 90 Macroeconomics Indicators Contributing... Ø The direction of the relationship between exchange rate and macroeconomic indicators (both in terms of volatility and level) found sensitive to the income groups (high income countries, middle income countries and low income countries) (The results are in line with Aydin, 2010; Bravo-Ortega & Giovnni, 2006; Christiansen et al., 2009; Devereux & Lane, 2003; Dhasmana, 2012; Diallo, 2015; Santacreu, 2015; Serven, 2003; Senadza & Diaba, 2017). Bibliography Acemoglu, Daron, Simon Johnson, James Robinson, and Yunyong Thaicharoen. “Institutional causes, macroeconomic symptoms: volatility, crises and growth.” Journal of monetary economics 50, no. 1 (2003): 49-123. Ahmad, Eatzaz, and Saima Ahmed Ali. “Exchange rate and inflation dynamics.” The Pakistan Development Review (1999): 235-251. Aizenman, Joshua, and Daniel Riera-Crichton. “Real exchange rate and international reserves in an era of growing financial and trade integration.” The Review of Economics and Statistics 90, no. 4 (2008): 812-815. Ali, Tariq Mahmood, Muhammad Tariq Mahmood, and Tariq Bashir. “Impact of interest rate, inflation and money supply on exchange rate volatility in Pakistan.” World Applied Sciences Journal 33, no. 4 (2015): 620-630. Aliyu, Shehu Usman Rano. “Impact of oil price shock and exchange rate volatility on economic growth in Nigeria: An empirical investigation.” MPRA Paper No. 16319, Munich: University Library of Munich, Germany, 2009: 4-15. Arabi, Khalafalla Ahmed Mohamed. “Estimation of exchange rate volatility via GARCH model case study Sudan (1978-2009).” International Journal of Economics and Finance 4, no. 11 (2012): 183-192. Arize, Augustine C., Thomas Osang, and Daniel J. Slottje. “Exchange-rate volatility and foreign trade: evidence from thirteen LDC’s.” Journal of Business & Economic Statistics 18, no. 1 (2000): 10-17. Arize, Augustine C., Thomas Osang, and Daniel J. Slottje. “Exchange-rate volatility in Latin America and its impact on foreign trade.” International Review of Economics & Finance 17, no. 1 (2008): 33-44. Arratibel, Olga, Davide Furceri, Reiner Martin, and Aleksandra Zdzienicka. “The effect of nominal exchange rate volatility on real macroeconomic performance in the CEE countries.” Economic Systems 35, no. 2 (2011): 261-277. Aydin, Burcu. Exchange rate assessment for sub-Saharan economies. No. 10-162. International Monetary Fund, 2010. Azeez, B. A., Funso T. Kolapo, and L. B. Ajayi. “Effect of exchange rate volatility on macroeconomic performance in Nigeria.” Interdisciplinary journal of contemporary research in business 4, no. 1 (2012): 149-155. Azid, Toseef, Muhammad Jamil, Aneela Kousar, and M. Ali Kemal. “Impact of exchange rate volatility on growth and economic performance: A case study of Hamdard Islamicus Vol. XLIII, No. 3 91 Pakistan, 1973-2003.” The Pakistan development review (2005): 749-775. Babtlett, M. S. “Smoothing periodograms from time-series with continuous spectra.” Nature 161, no. 4096 (1948): 686-687. Benetrix, Agustin S., and Philip R. Lane. “Fiscal shocks and the real exchange rate.” International Journal of Central Banking 9, no. 3 (2013): 6-37. Bilson, John FO. “The monetary approach to the exchange rate: some empirical evidence.” IMF Staff Papers 25, no. 1 (1978): 48-75. Bobai, Francis Danjuma, Shuaibu Ubangida, and Y. S. Umar. “An assessment of exchange rate volatility and inflation in Nigeria.” Journal of Emerging Issues in Economics, Finance and Banking 1, no. 4 (2013): 321-340. Bravo-Ortega, Claudio, and Julian Di Giovanni. “Remoteness and real exchange rate volatility.” IMF Staff Papers 53, no. 1 (2006): 115-132. Cakrani, Edmira, Pranvera Resulaj, and Luciana Koprencka. “Government spending and real exchange rate case of Albania.” European Journal of Sustainable Development 2, no. 4 (2013): 303-310. Calderon, Cesar Augusto, Alberto Chong, and Norman V. Loayza. “Determinants of current account deficits in developing countries.” The BE Journal of Macroeconomics 2, no. 1 (2002). Calderon, Cesar. “Trade openness and real exchange rate volatility: panel data evidence.” Working Paper 294. Central Bank of Chile, 2004. Canzoneri, Matthew B., Robert E. Cumby, and Behzad Diba. “Relative labor productivity and the real exchange rate in the long run: evidence for a panel of OECD countries.” Journal of international economics 47, no. 2 (1999): 245-266. Choudhri, Ehsan U., and Mohsin S. Khan. “Real exchange rates in developing countries: are Balassa-Samuelson effects present?.” IMF Staff Papers 52, no. 3 (2005): 387- 409. Chowdhury, Mohammad, and Md Hossain. “Determinants of exchange rate in Bangladesh: A case study.” Journal of Economics and Sustainable Development 5, no. 1 (2014). Christiansen, Lone Engbo, Mr Alessandro Prati, Mr Luca Antonio Ricci, and Mr Thierry Tressel. External balance in low income countries. Working Paper. No. 9-221. International Monetary Fund, 2009. Christopoulos, Dimitris K., Karine Gente, and Miguel A. León-Ledesma. “Net foreign assets, productivity and real exchange rates in constrained economies.” European Economic Review 56, no. 3 (2012): 295-316. Cottani, Joaquin A., Domingo F. Cavallo, and M. Shahbaz Khan. “Real exchange rate behavior and economic performance in LDCs.” Economic Development and cultural change 39, no. 1 (1990): 61-76. Devereux, Michael B., and Philip R. Lane. “Understanding bilateral exchange rate volatility.” Journal of International Economics 60, no. 1 (2003): 109-132. Dhasmana, Anubha. “India’s real exchange rate and trade balance: Fresh empirical evidence.” IIM Bangalore Research Paper 373 (2012). Diallo, Ibrahima Amadou. “Exchange rate volatility and investment: a panel data cointegration approach.” Expert Journal of Economics 3, no. 2 (2015): 127-135. Diamandis, Panayiotis F., Anastassios A. Drakos, and Georgios P. Kouretas. “Exchange Rates, Fundamentals, and Nonlinearities: A Review and Some Further Evidence 92 Macroeconomics Indicators Contributing... from a Century of Data.” In International Symposia in Economic Theory and Econometrics, vol. 23, pp. 85-124. Emerald Publishing Ltd, 2014. Dlamini, Bongani P. “Exchange rate volatility and its effect on macroeconomic management in Swaziland.” Central Bank of Swaziland, 2014. Domaç, Ilker, and Alfonso Mendoza. Is there room for foreign exchange interventions under an inflation targeting framework? Evidence from Mexico and Turkey. World Bank Policy Research, Working Paper No. 3288. The World Bank, 2004. Dornbusch, Rudiger. “Expectations and exchange rate dynamics.” Journal of political Economy 84, no. 6 (1976): 1161-1176. Ebaidalla, Ebaidalla Mahjoub. “Real exchange rate misalignment and economic performance in Sudan.” African Review of Economics and Finance 6, no. 2 (2014): 115-140. Eichengreen, Barry. “The real exchange rate and economic growth.” Social and Economic Studies 56, no. 4 (2007): 7-20. Engel, Charles, and Kenneth D. West. “Exchange rates and fundamentals.” Journal of political Economy 113, no. 3 (2005): 485-517. Engle, Robert F., and Clive WJ Granger. “Co-integration and error correction: representation, estimation, and testing.” Econometrica: journal of the Econometric Society (1987): 251-276. Esquivel, Gerardo, and Felipe Larraín B. “The impact of G-3 exchange rate volatility on developing countries.” G-24 Discussion Paper Series. United Nations Conference on Trade and Development No. 16. Centre for International Development Harvard University, 2002. Frenkel, Jacob A. “A monetary approach to the exchange rate: doctrinal aspects and empirical evidence.” The scandinavian Journal of economics (1976): 200-224. Gala, Paulo. “Real exchange rate levels and economic development: theoretical analysis and econometric evidence.” Cambridge Journal of economics 32, no. 2 (2007): 273-288. Giannellis, Nikolaos, and Athanasios P. Papadopoulos. “What causes exchange rate volatility? Evidence from selected EMU members and candidates for EMU membership countries.” Journal of International Money and Finance 30, no. 1 (2011): 39-61. Granger, Clive WJ. “Some properties of time series data and their use in econometric model specification.” Journal of econometrics 16, no. 1 (1981): 121-130. Grossmann, Axel, Inessa Love, and Alexei G. Orlov. “The dynamics of exchange rate volatility: A panel VAR approach.” Journal of International Financial Markets, Institutions and Money 33 (2014): 1-27. Grydaki, Maria, and Stilianos Fountas. “Exchange rate volatility and output volatility: a theoretical approach.” Review of International Economics 17, no. 3 (2009): 552- 569. Hadri, Kaddour. “Testing for stationarity in heterogeneous panel data.” The Econometrics Journal 3, no. 2 (2000): 148-161. Hau, Harald. “Real exchange rate volatility and economic openness: theory and evidence.” Journal of money, Credit and Banking (2002): 611-630. Hodrick, Robert J. “An Empirical Analysis of the Monetary Approach to the Determination of the Exchange Rate .” The economics of exchange rates (1978): 97-116. Hamdard Islamicus Vol. XLIII, No. 3 93 Hviding, Ketil, Nowak, Mr M., and Mr Luca Antonio Ricci. Can higher reserves help reduce exchange rate volatility?. IMF Working Paper No. 4-189. International Monetary Fund, 2004. Iavorschi, Mihaela. “The influence of Foreign direct investments and the current account of the balance of payments on the evolution of the Lei/Euro exchange rate in Romania.” Procedia Economics and Finance 16, no. 4 (2014): 448-457. Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin. “Testing for unit roots in heterogeneous panels.” Journal of econometrics 115, no. 1 (2003): 53-74. Kamin, Steve B., and John H. Rogers. “Output and the real exchange rate in developing countries: an application to Mexico.” Journal of development economics 61, no. 1 (2000): 85-109. Kandil, Magda, and Ida Mirzaie. “The effects of exchange rate fluctuations on output and prices: evidence from developing countries.” The Journal of Developing Areas (2005): 189-219. Kasman, Adnan, and Duygu Ayhan. “Foreign exchange reserves and exchange rates in Turkey: Structural breaks, unit roots and cointegration.” Economic Modelling 25, no. 1 (2008): 83-92. Kearney, Colm, and Ronald MacDonald. “Rational expectations, bubbles and monetary models of the exchange rate: the Australian/US dollar rate during the recent float.” Australian Economic Papers 29, no. 54 (1990): 1-20. Kemal, M. Ali, and Usman Qadir. “Real exchange rate, exports, and imports movements: A trivariate analysis.” The Pakistan Development Review (2005): 177-195. Khan, Abdul Jalil, Parvez Azim, and Shabib Haider Syed. “The impact of exchange rate volatility on trade: A panel study on Pakistan’s trading partners.” The Lahore Journal of Economics 19, no. 1 (2014): 31-66. Khan, Rana Ejaz Ali, Rashid Sattar, and Hafeez Rehman. “Effectiveness of exchange rate in Pakistan: Causality analysis.” Pak. J. Commer. Soc. Sci 6, no. 1 (2012): 83-96. Khraiche, Maroula, and Jeffrey Gaudette. “FDI, exchange rate volatility and financial development: regional differences in emerging economies.” Economics Bulletin 33, no. 4 (2013): 3143-3156. Kilicarslan, Zerrin. “Determinants of exchange rate volatility: empirical evidence for Turkey.” Journal of Economics, Finance and Accounting 5, no. 2 (2018): 204- 2013. Kim, Benjamin JC, and Soowon Mo. “Cointegration and the long-run forecast of exchange rates.” Economics letters 48, no. 3-4 (1995): 353-359. LeeLee, Chong, and Tan HuiBoon. “Macroeconomic factors of exchange rate volatility.” Studies in Economics and Finance 24, no. 4 (2007), 266-285. Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu. “Unit root tests in panel data: asymptotic and finite-sample properties.” Journal of econometrics 108, no. 1 (2002): 1-24. MacDonald, Ronald. “Exchange rate behaviour: are fundamentals important?.” The Economic Journal 109, no. 459 (1999): 673-691. Madesha, Wellington, Clainos Chidoko, and James Zivanomoyo. “Empirical test of the relationship between exchange rate and inflation in Zimbabwe.” Journal of economics and sustainable development 4, no. 1 (2013): 52-58. 94 Macroeconomics Indicators Contributing... Mahmood, Iqbal, Major Ehsanullah, and Ahmed Habib. “Exchange rate volatility & macroeconomic variables in Pakistan.” Business management dynamics 1, no. 2 (2011): 11-22. Mark, Nelson C., and Donggyu Sul. “Nominal exchange rates and monetary fundamentals: evidence from a small post-Bretton Woods panel.” Journal of international economics 53, no. 1 (2001): 29-52. McPherson, Malcolm F., and Tzvetana Rakovski. Exchange rates and economic growth in Kenya: an econometric analysis. Papers 651. Harvard Institute for International Development, 1998. Meese, Richard A., and Kenneth Rogoff. “Empirical exchange rate models of the seventies: Do they fit out of sample?.” Journal of international economics 14, no. 1-2 (1983a): 3-24. Meese, Richard, and Kenneth Rogoff. “The out-of-sample failure of empirical exchange rate models: sampling error or misspecification?.” In Exchange rates and international macroeconomics, pp. 67-112. University of Chicago Press, 1983b. Morana, Claudio. “On the macroeconomic causes of exchange rate volatility.” International Journal of Forecasting 25, no. 2 (2009): 328-350. Newey, Whitney K., and Kenneth D. West. “Automatic lag selection in covariance matrix estimation.” The Review of Economic Studies 61, no. 4 (1994): 631-653. Ojo, Ade T., and Philip O. Alege. “Exchange rate fluctuations and macroeconomic performance in sub-Saharan Africa: A dynamic panel cointegration analysis.” Asian Economic and Financial Review 4, no. 11 (2014): 1573-1591. Pesaran, M. Hashem, Yongcheol Shin, and Ron P. Smith. “Pooled mean group estimation of dynamic heterogeneous panels.” Journal of the American statistical Association 94, no. 446 (1999): 621-634. Prabheesh, K. P., D. Malathy, and R. Madhumathi. “Demand for foreign exchange reserves in India: a co-integration approach.” (2007): 36-46. Ramasamy, Ravindran, and Soroush Karimi Abar. “Influence of macroeconomic variables on exchange rates.” Journal of economics, Business and Management 3, no. 2 (2015): 276-281. Rapach, David E., and Mark E. Wohar. “Testing the monetary model of exchange rate determination: new evidence from a century of data.” Journal of International Economics 58, no. 2 (2002): 359-385. Rapetti, Martin, Peter Skott, and Arslan Razmi. “The real exchange rate and economic growth: are developing countries different?.” International Review of Applied Economics 26, no. 6 (2012): 735-753. Rashidin, Md Salamun, Irfan Ullah, and Mahad Jehangir. “The Influence of Balance of Payments and Balance of Trade on Exchange Rate in Developing Countries of Asia: A Case study of Bangladesh, Pakistan and India.” Sonargaon University Journal 1, no. 2 (2017). Rehman, Mushtaq. “Analysis of Exchange Rate Fluctuations: A Study of PKR VS USD.” Journal of Managerial Sciences 8, no. 1 (2014): 41-60. Ricci, Luca Antonio, Gian Maria MilesiFerretti, and Jaewoo Lee. “Real exchange rates and fundamentals: a crosscountry perspective.” Journal of Money, Credit and Banking 45, no. 5 (2013): 845-865. Hamdard Islamicus Vol. XLIII, No. 3 95 Saeed, Ahmed, Rehmat Ullah Awan, Maqbool H. Sial, and Falak Sher. “An econometric analysis of determinants of exchange rate in Pakistan.” International Journal of Business and Social Science 3, no. 6 (2012): 184-196. Santacreu, Ana Maria. “The economic fundamentals of emerging market volatility.” Economic Synopses 2, The Federal Reserve Bank of St. Louis: 2015. Schwarz, Gideon. “Estimating the dimension of a model.” The annals of statistics 6, no. 2 (1978): 461-464. Sekmen, Fuat. “Cointegration and causality among foreign direct investment in tourism sector, GDP, and exchange rate volatility in Turkey.” The Empirical Economics Letters 6, no. 1 (2007): 53-58. Senadza, Bernardin, and Desmond Delali Diaba. “Effect of exchange rate volatility on trade in Sub-Saharan Africa.” Journal of African Trade 4, no. 1-2 (2017): 20-36. Serven, Luis. “Real-exchange-rate uncertainty and private investment in LDCs.” Review of Economics and Statistics 85, no. 1 (2003): 212-218. Stavarek, Daniel, and Cynthia Miglietti. “Effective exchange rates in central and Eastern European Countries: Cyclicality and relationship with macroeconomic fundamentals.” Review of Economic Perspectives 15, no. 2 (2015): 157-177. Supaat, Saktiandi, A. Phang Seow Jiun, NG HENG Tiong, and E. D. W. A. R. D. Robinson. “Investigating the relationship between exchange rate volatility and macroeconomic volatility in Singapore.” MAS Staff Paper 25. Monetary Authority of Singapore, 2003. Udoh, Edet Joshua, Sunday Brownson Akpan, Daniel Etim John, and Inimfon Vincent Patrick. “Cointegration between exchange rate volatility and key macroeconomic fundamentals: evidence from Nigeria.” Modern Economy 3, no. 7 (2012): 846. Vijayakumar, Sinnathurai. “The effects of exchange rate on the trade balance in the Sri Lankan context after post liberalization.” International Journal on Global Business Management and Research 2, no. 2 (2014): 1-12. Vitek, Francis. An assessment of external price competitiveness for Mozambique. Working Paper No. 9-165. International Monetary Fund, 2009. Vygodina, Anna V., Thomas S. Zorn, and Richard DeFusco. “Asymmetry in the effects of economic fundamentals on rising and falling exchange rates.” International Review of Financial Analysis 17, no. 4 (2008): 728-746. Williams, A. Paul, and S. Prasad. “A Study on the Exchange Rate Determinants of Selected Asian Countries’ Currencies.” International Journal of Advanced Science and Technology 28, no. 19 (2019): 1202-1207. Yaya, Mehmet E., and Xiaoxia Lu. “The short-run relationship between real effective exchange rate and balance of trade in China.” International Journal of Applied Economics 9, no. 1 (2012): 15-27. Yunana, T. W., W. R. Mato, Yunana Titus Wuyah, and Wada Rantan Mato. “Economic Implications of Exchange Rate Volatility on Macroeconomic Variables in Nigeria.” International Journal of Original Research 2, no. 3 (2016): 116-123. Zakaria, Zukarnain. “The relationship between export and exchange rate volatility: Empirical evidence based on the trade between Malaysia and its major trading partners.” Journal of Emerging Issues in Economics, Finance and Banking 2, no. 2 (2013): 668-684. .