The impact of oil price on the stability of money demand in Saudi Arabia: New evidence from ARDL bounds tests

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1 The impact of oil price on the stability of money demand in Saudi Arabia: New evidence from ARDL bounds tests Dr. Karim Barkat College of Business and Economics, Qatar University Dr. Mouyad AL SAMARA College of Business and Economics, Qatar University Abstract: This paper investigates the long and the short run effects of oil price movements on the stability of money demand function in Saudi Arabia over 1990:Q1-2014:Q4. In sake of examine the money demand function in Saudi Arabia, we use the scale and opportunity cost variable in addition to the oil price. This variable is extremely important to shed lights on the potential impact of oil price on the money demand behavior in an oil rich country based on Islamic monetary system. To this end this paper employs Autoregressive distributed lags ARDL bounds tests for cointegration proposed by Pesaran and Smith (2001). The empirical results show that there is a robust evidence for a stable long run relationship between real money demand, real GDP, consumer price index, and oil price. Moreover, oil price has a positive long and short run effect on the demand for money which supports the effect of income. In contrast, consumer price index has a negative impact which maintains the opportunity cost of holding money. However, error correction term provides a significant evidence that the money demand will converge to its equilibrium level reacting to a shortrun shock. Finally, since the findings of CUSUM and CUSUMQ tests infer a stable money demand function, money is still matter for Saudi Arabia in conducting an efficient monetary policy. Key words: Money Demand Stability, linear ARDL and oil price JEL Classification : C33, E31, E41, E51 1

2 1. Introduction Over the last three decades, the stability of money demand function has acknowledged a special importance in the monetary analysis to formulate and implement an optimal monetary policy. Several reasons have underlined this standing for the empirical monetary analysis in many developed, emerging and developing economies: First of all, money demand function has been emphasized as an essential link between the monetary and real spheres of the economy. This link is very crucial in the sense of understanding how the economic performance is depend on the monetary aggregate changes. Secondly, the money demand function represents a marvelous framework for the monetary policy strategy to control and forecast the final objective of the Central Bank represented by the price stability. In particularly, the well specified and stable money demand function reveals obviously the transmission mechanism of the monetary policy and show how nominal and real variables are related. Thirdly, the money demand function links the monetary part of the economy (Money liquidity (LM)) to the real part (Investment-Saving (IS)). This enables to understand how monetary and non-monetary shocks are transmitted to the aggregate money demand. It is worth mentioning that there has been a growing consensus among economists about the importance of well-specified and stable money demand function as a prerequisite criteria for a good effectiveness of monetary policies. Poole (1970) mentioned that the stability of money demand has a significant importance to how monetary policy should be conducted. He recommended that the monetary authorities targets depends on whether the LM and/or IS curves are unstable or not. For instance, if the LM is unstable in this case the interest rate should be the target and if the IS curve is unstable then the money supply should be the target. Finally, the stable money demand function provides policymakers by a real indicator of how monetary policy should be design and conduct in the economy. In this respect the stability of money demand equation plays an important role in determining which monetary policy tools can be the best choice and target for Central Banks. To this end, monetary policy would target the interest rate if the aggregate demand shocks come from changes in money demand, i.e. (LM curve) is unstable. In contrast, money supply will be targeted when investment-saving relationship (IS curve) is unstable (Kumar et al. (2013) and Rao & Kumar (2009)). In the fact that the recent development in financial innovation, the increase of financial integration and the high instability in the world economy related the commodities prices have an important effects on both the level and the stability of the money demand. However, in the context of SA, the stability of the money demand equation has been miss-specified in the literature and to our knowledge this is the first comprehensive study that attempts to investigate the key determinants of the money demand in this country using linear ARDL. Since Friedman (1959) suggested a fixed growth rate of money supply to sustain price stability, the stable money demand has long been an important framework for monetary policy. It is worth to mention that the characteristics of this stability are highly country and situation dependent. 2

3 However, the recent implementation of financial reforms, deregulation and financial product innovations have led to an upsurge of empirical studies investigating the impact of these reforms on the stability of the money demand function. Overall, in developed countries, the money demand function becomes unstable after financial reforms which raised the doubts about the validity of the monetary policy, (see, for instance, Caporate and Gil-Alana, 2005 and Haug, 1999, 2006). The aim of this paper is to investigate if the real money demand reacts to its determinants. Our investigation is conducting on the Saudi Arabia case over the 1990q1-2014q4 period. Several reasons make Saudi Arabia an interesting and original case to this type of analysis. First, as a member of G-20 of the largest economies, Saudi Arabia is the world s leading producer and exporter of oil and the world s second biggest reserves of oil IMF,(2014). Second, Given that the economy of Saudi Arabia is based mainly on oil production, oil exports accounts for over 90% of total exports and nearly 80% of government revenues. Third, real GDP is extremely driven by oil production and oil revenue which makes the contribution of the oil sector to the economic growth dominating the non-oil sectors. Furthermore, Saudi Arabia economy is characterized by a fixed exchange rate regime with respect to US dollar as a nominal anchor to sustain the price stability. Finally, given the restricted monetary policy and the required economic diversification, the country is extremely vulnerable to real shocks that might have important effects on the stability of MDF under a fixed exchange rate regime. The empirical analysis of the MDF under these critical conditions is crucial to understand the potential effect of the scale and the opportunity cost variables on the demand for real money balances. There is only few empirical study on the MDF for Saudi Arabia, for instance Darrat (1984) examined the relationship between real money demand, inflation rate and oil price change over 1962 to Abdulkheir (2013) also provided a cointegration evidence on MDF in Saudi Arabia for the periods by employing the vector error correction model (VECM). In this respect our empirical analysis is completely new in terms of increase the frequency of the data used from annual to quarterly, focusing on the role of the oil price in the demand for money in SA Furthermore, this paper contributes to the existing monetary literature in many ways. First, it investigates a new specification of money demand equation by adding oil price to the money function. Second, in contrast to the majority of studies investigating the stability of the demand function this paper is different from the existent empirical studies which have used standard econometric approach, by using the ARDL Bounds test for cointegration as proposed by Pesaran et al. (2001). 3

4 2. Literature review Over the last three decades, economists and researchers have used different econometrics methods to test the stability of money demand function in both developed and developing countries. The empirical literature review has mainly used two types of cointegration approaches: the first one is the linear symmetric cointegration such as Johansen Maximum Likelihood (JML), Autoregressive Distributed Lags (ARDL) of Pesaran et al. (2001) and cointegration with structural break developed by Gregory and Hansen (1996)). The second type has focused mainly on the nonlinear cointegration relationship between the selected variables. Among the well-known nonlinear cointegration methods there in nonlinear ARDL model as proposed by Shin et al. (2014). This techniques is rarely used in money demand analysis and based essentially on the asymmetric response of the dependent variable to the dependent variables. In addition to nonlinear ARDL, Threshold VAR (TVAR) and Smooth Transition Autoregressive (STAR) are two methods to investigate the nonlinear cointegration. Most of the previous studies have used the symmetric analysis based on the linear cointegration. First, Johansen Maximum Likelihood (JML) cointegration approach has been used to test the long run relationship between money demand function and its determining factors and after testing for residuals stability using CUSUM and squared CUSUM test the results is interpreted as stability of the money demand function. (see for example, Dreger and Wolters, 2014; Hossain, 2010; Siddiki, 2010; Rao and Kumar, 2009a; Bahmani-Oskooee and Gelan, 2009; Hamori and Hamori 2008). Second, in the same sense many have employed the bounds testing Autoregressive Distributed Lags (ARDL) cointegration approach of Pesaran et al. (2001), (see for example, Ben-Salha and Jaidi, 2014; Kumar and Rao, 2012; Baharumshah et al. 2009; Tuck, 2007; Bahmani-Oskooee and Tanku, 2006; Akinlo, 2005). Tuck (2007) examined the money demand function for five Southeast Asian countries, Malaysia, Thailand, Singapore, the Philippines, and Indonesia. Using the ARDL approach the author found a stable relationship between the real M2 and real expenditure components, the exchange rate, and the inflation rate for three cases out of five, Malaysia, Philippines and Singapore. Using the same approach Baharumshah et al. (2009) examine the relationship between the demand for money (M2) and its determinants (real GDP, inflation, stock prices and foreign interest rates) in China. The authors confirmed in their empirical findings the presence of a stable money demand equation. More recently, Ben-Salha and Jaidi (2014) provide more evidence of a stable long run relationship between broad money demand in Tunisia and its explanatory factors. Ben-Salha and Jaidi (2014) have also shown the importance of the major components of real income such as expenditure on consumption and expenditure on investment and interest rate in determining the money demand. Third, few economists have examined a possible shift in the cointegration vector as proposed noted by Gregory and Hansen (1996) (GH henceforth). GH (1996) propose three tests to check the null hypothesis of the absence of cointegration against the alternative one of cointegration in the presence of level or regime shifts. Their results show that, the conventional ADF test fails to 4

5 reject the null hypothesis of no cointegration, in contrast by allowing for intercept and regime shifts they found strong evidence for refusal of the null. This method has been used recently by many researchers to examine the money demand stability with a possible shift in the cointegration vector (see for example, Sani et al., 2014; Nyong, 2014; Kumar et al., 2013; Kumar, 2011; Chukwu et al., 2010; Rao and Kumar, 2009a and 2009b). Kumar (2009a) have examined the of the money demand stability in Bangladesh by using the GH approach. The author provided strong evidence for the existence of a long run relationship with shift in level (in the intercept) and for a stable narrow money demand function. In a second paper, Kumar et al. (2013) argued that by including shift in the cointegration vector of the narrow money demand function, the relationship still stable for the Nigerian economy. Their empirical analysis revealed how real exchange rate and inflation are useful to capture the cost of holding money and finally they concluded that money supply is still relevant for the Nigerian monetary policy. In addition the CUSUM and squared CUSUMS tests showed that the estimated parameters are stable over all the period.. An overview about the main findings of the recent empirical literature that have investigated the stability of the money demand function as well as the econometric approach used are reported in Table (1). 5

6 Cointegration Methods Authors Country and time span Money measure Methodology* Results Hamdi et al (2015) GCC countries M2 FMOLS, PDOLS and PMGE Stable Ben-Salha and Jaidi (2014) Tunisia M2 ARDL Stable Sani et al. (2014) Nigeria 1991:Q1-2013:Q4 M2 Cointegration (GH) WSB Stable WSB Dreger and Wolters (2014) Euro area M3 Cointegration Stable Kumar et al. (2013) Nigeria M1 Cointegration WSB Stable WSB Kumar and Rao (2012) 14 Asian countries M1 GETS approach (ECM & ARDL) Stable Linear Cointegration Methods Kumar (2011) 20 developing countries M1 GETS approach: ARDL WSB Stable Kumar et al. (2010) 11 OECD Countries M1 Panel Data WSB Stable WSB Hossain (2010) Bangladesh Real M2 JML Stable WSB Chukwu et al. (2010) Nigeria M2 Cointegration and structural breaks Stable Siddiki (2010) Bangladesh M2 JML Stable Rao and Kumar ( 2009,a) Bangladesh Real M1 Cointegration WSB Unstable Rao and Kumar ( 2009,b) Asian developing countries Real M1 Cointegration WSB Stable WSB Singh and Pandey (2009) India M3 Cointegration WSB Stable WSB Yu and Gan (2009) ASEAN-5 M2 EG Stable Darrat and Al-Sowaid (2009) Bahrain, UAE and Qatar M1,M2 Johansen and Juselius (1990, JJ) Cointegration M1 Baharumshah et al. (2009) China 1990: :04 M2 ARDL Stable Bahmani-Oskooee and Gelan (2009) 21 African Countries M2 JML Cointegration Inoue and Hamori (2008) India M3 Cointegration test Cointegration Hamori (2008) Sub-Saharan African M2 Non-stationary panel data analysis Cointegration Hamori and Hamori (2008) 11 EU countries M3 JML Stable Owoye and Onafowora (2007) Nigeria M2 JML Stable Bahmani-Oskooee and Wang (2007) China M1, M2 ARDL Only M1 stable Nwafor et al (2007) Nigeria 1986Q3 2005Q4 M2 VAR Stable Narayan (2007) Indonesia M2 JML Cointegration Tuck (2007) ASEAN-5 M2 ARDL Stable Bahmani-Oskooee & Tanku (2006) LCDs Real M2 ARDL Cointegration Bahmani-Oskooee & Rehman (2005) Asian developing countries M1, M2 JML Unstable Akinlo (2005) Nigeria 1970: :04 M2 ARDL Stable Nell (2003) South Africa M1, M2 and M3 EG M3 stable, M1 and M2 unstable Bahmani-Oskooee & Rehman (2002) ASEAN-5 M2 ADDL Stable Sriram (2002) Malaysia 1973:1-1995:12 M2 ECM Stable Bahmani-Oskooee and Bohl (2000) Germany (After the unification) M3 ARDL Not stable Bahmani-Oskooee and Barry (2000) Russia M2 ARDL Not stable *: ARDL: Auto Regressive Distributed Lag, GH: Gregory and Hansen (1996), JML: Johansen Maximum Likelihood, VAR: Vector Auto Regression, WSB: With Structural Break, EG (Engle and Granger cointegration), STR (Smooth Transition) 6

7 2. Methodology and model structure of the ARDL Cointegration It is common in the empirical literature to use the Error Correction Model (ECM) to analyze the relationship between variables which are integrated of order 0 and/or 1. Pesaran and al. (2001) have developed a linear cointegration autoregressive distributed lag (ARDL) to evaluate simultaneously the long run and the short run effects. The ARDL model has a number of key advantages. First, it can be used to test for cointegration among variables in the case of I (0) and/or I (1) but not I (2). Secondly, ARDL approach performs well in the small samples compared to the other multivariate cointegration method (Narayan and Smyth, 2005, Onafowora and Owoye, 2014). Thirdly, the test is based on a single ARDL equation, rather than on a VAR, as in Johansen and Juselius (1990), thus reducing the number of parameters to be estimated. Fourthly, it is applicable even when the explanatory variables are endogenous (Pesaran et al. 2001). The general equation of ARDL (p, q) bounds test for cointegration is as follow: p 1 y t = α 0 + ρy t 1 + θx t 1 + τw t + α i y t i + δ i x t i + μ t (1) i=1 Where: w t is a vector of deterministic variables and μ t is an iid stochastic process. We say that the two variables y t and x t in eq. (1) are not cointegrated if ρ= θ=0. To test the presence of cointegration Pesaran et al. (2001) have proposed the F-test. The decision is based on two critical bounds; the upper and the lower one. When the F-statistic is greater than the upper bound the null hypothesis of absence of a levels relationship is rejected. This means that there is a long run relationship between y t and x t. The ARDL model in equation (1) assumes a linear combination of y t and x t which indicates a symmetric adjustment in the long and the short run. q 1 i=0 7

8 3. Data, Model and Empirical Results The empirical analysis depends on quarterly data for Saudi Arabia over the period 1990q1 to 2014q4. The data are collected from the data base of World Development Indicators (WDI) and the International Monetary Fund (IMF). More precisely, the variables used are the following: the real money demand balance (RMD) measured as money supply (M2) deflated by real GDP deflator, the oil price and the inflation rate All the selected variables are expressed in natural logarithm. Following Goldfeld (1992) and Hossain (2010) the analysis is performed on the following general empirical model: RRR t = f(ss, OO) 3.1 Money Demand Model The empirical analysis based mainly on a well-known practice in formulating the money demand function to be included a measures of scale variable such as (real output) and opportunity cost variable such as (interest rate). The precise select of these variables depends critically on a country s level of financial development and monetary policy transmission mechanisms. Following Ben-Salha and Jaidi (2014), Hossain (2010) and Kumar (2008) the typical form of the money demand equation is given as follow : Ln M t = α + β 1 Ln Y t + β 2 i t + μ t Since the specification of the money demand function is highly country dependent, this empirical analysis will perform for the money demand in SA taking into account two important facts: First, given the existence of emerging financial markets, interest rate doesn t hold as a true opportunity cost variable for holding money. To this end, the inflation rate is used to measure the opportunity cost variable (Bahmani-Oskooee and Rehman, 2005; Bahmani-Oskooee and Tanku, 2006). Second, given the country s level of foreign exchange market and the hoc financial crisis, it is crucial to reflect the impact of currency substitution that occurred following exchange rate chock. Therefore, the money demand equation for the SA economy will be as follow: Ln M t = α + β 1 Ln Y t + β 2 π t + β e EE t + μ t (6) The eq. (6) states the standard function of money demand in which the real money demand is mainly determined by the scale variable (SV), such as real income, and the opportunity cost (OC) of holding money, such as inflation rate or interest rate. Given that the oil production has played a crucial role in the economy of Saudi Arabia and the existence of a fixed exchange rate regime, the oil price (OP) and the inflation (π) are expected to have important effects on the demand for real balances as presented in equation 7. 8

9 lrrr t = f( RRRR t, CCC t, EE t, OO t ) (7) As a starting point it is necessary to verify that all variables are not integrated of order two (I(2)), otherwise the ARDL approach is not applicable. Consequently, we use Augmented Ducky Fuller (ADF) test to examine the stationarity of the selected variables. Table (2) shows the unit root test results which reveal that all the variables are stationary in first differences (i.e. I (1)) and therefore we can continue with testing for cointegration in the ARDL framework. Table (2) ADF Unit root tests Level First difference ADF test results Intercept Trend Intercept Trend RRR ** -3.80** RRRR *** -8.07*** CCC *** -7.05*** OO *** -7.07*** EX *** -7.97*** Note: The optimal lag structure of the ADF test is chosen based on the Akaike Information Criterion, *, * *, * * *, indicate the significance at 10%, 5% and 1% respectively. 3.2 ARDL model: The second step is to examine the long run relationship among the variables by using the linear ARDL bounds test for cointegration as presented in eq. (1). It is important to mention that we exclude the exchange rate variable form the selected model because it provids insignificant results RRR t = cccc + α 1 RRRR t 1 + α 2 RRRR t 1 + α 3 OO t 1 + α 4 CCC t 1 + a i RRR t i p 1 q q p 1 i=1 + γ i RRRR t i + c i OO t i + e i CCC t i + μ t i=1 i=0 i=0 (8) The estimated results of the long run relationship for the symmetric model (eq. (8)), presented in table (4), show that the coefficient of the lag of the real money demand is non-significant (α 1 = 0.020; p vvvvv = 0.18 ). Furthermore, the bounds test of cointegration F-PSS presented in table (3) reveals that the null hypothesis of no cointegration cannot be rejected (F-PSS= 1.13). This confirms the conclusion that the long run cointegration relationship is absent. 9

10 Table (3) Bounds test for cointegration in the linear and the nonlinear specifications Dependent variable: lnrmd F-statistic 95% lower bound 95% upper bound Result Linear ARDL cointegration Note: Bound test at 5% #The exact specification of the asymmetric ARDL model is presented analytically in Table 3 and 4. Table 4: Linear ARDL estimation 10

11 Table 4: Linear ARDL estimation Dependent variable: lnrmd variable Coefficient P_value T-statistic Constant 3.22*** LLLLL (t 1) LLLLLL (t 1) 1.61*** LLLLL (t 1) -0.17* LLLLL (t 2) ** LLLLLL (t 2) 0.85** LLLLL (t 1) -0, LLLLL (t 2) -0.73*** LLLL (t 1) 0.12** LLLL (t 2) 0.14*** Cointegration test statistics F_PSS =4.18 Statistics and diagnostics X 2 SS = (0.38) 2 X NNNN = 3.65 (0.16) ECt= p_value= The empirical results show that there is a robust evidence for a stable long run relationship between real money demand, real GDP, consumer price index, and oil price. Moreover, oil price has a positive long and short run effect on the demand for money which supports the income effect s. In contrast, consumer price index has a negative impact which maintains the opportunity cost of holding money. 11

12 Figure (1) CUSUM and CUSUMQ stability tests CUSUM 5% Significance CUSUM of Squares 5% Significance However, error correction term provides a significant evidence that the money demand will converge to its equilibrium level reacting to a short-run shock by (24%) in each quarter. Finally, since the findings of CUSUM and CUSUMQ tests infer a stable money demand function, money is still matter for Saudi Arabia in conducting an efficient monetary policy and its also valid for the out of sample forecasting. 4. Conclusion This paper examines the long and the short run effects of oil price shocks on the stability of money demand function in Saudi Arabia over 1990:Q1-2014:Q4. We augmented the money demand function by adding the oil price together with the scale and opportunity cost variable. This variable is extremely important to shed lights on the potential impact of oil price on the money demand behavior in an oil rich country based on Islamic monetary system. To this end this paper employs Autoregressive distributed lags ARDL bounds tests for cointegration proposed by Pesaran and Smith (2001). The empirical results show that there is a robust evidence for a stable long run relationship between real money demand, real GDP, consumer price index, and oil price. Moreover, oil price has a positive long and short run effect on the demand for money which supports the income effect strand. In contrast, consumer price index has a negative impact which maintains the opportunity cost of holding money. However, error correction term provides a significant evidence that the money demand will converge to its equilibrium level reacting to a short-run shock. Finally, since the findings of CUSUM and CUSUMQ tests infer a stable money demand function, money is still matter for Saudi Arabia in conducting an efficient monetary policy. 12

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