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WHAT COULD BE THE INDICATOR OF THE MONETARY POLICY STANCE? THE CASE OF ALGERIA MEDACI Narimèn, PhD. Higher School of Management & International Trade. University Pole of KOLEA- Algeria E-mail [email protected] Tel: +213 771 601 742 Abstract: we try through this paper, to look for the right indicator for monetary policy stance, conducted by the Bank of Algeria in the period of post structural adjustment, and an overliquidity context. Using an empirical approache, we have measured the impact of policy indicator on real economy. Indeed, searching for the right indicator is based on the test of three hypotheses. First, the variable that would be a good indicator to measure the monetary policy stance should be a good predictor for changes in the real economy. This good indicator should be heavily influenced by the macroeconomic targets such as inflation -technically Bank of Algeria set the ultimate target of monetary policy expressed in terms of medium-term stability of prices that is fixed at 3%-. Then, the good indicator should reflect the policy induced shocks on the supply of money and not shocks to money demand, caused by changes in the real economy. Findings supported that M2 and the interbank interest rate could be the indicators for monetary policy stance, however they are not operating because of the conjuncture of monetary policy stance mechanism in such as a structural overliquidity situation, and the most important variable of the Algerian economy, which is the oil price. Key words: Monetary policy, policy instruments, policy targets , money market, monetary transmission mechanism JEL Classifications: E510, E520, E590 1. Introduction We try through this paper, to look for the right indicator for monetary policy stance, conducted by the Bank of Algeria (BoA) , within a post structural adjustment, specially characterized by a situation of overliquidity, that hampers the regular use of open market operations. In an economy in transition to a market economy this issue seems be even more complex. Maria Piotrowska (1999) revealed this difficulty, following the approach of Bernanke & Blinder (1992) . In this regard, we are interested in the conduct of Monetary Policy by the BoA. In fact, the transition to market economy in Algeria, by adopting a liberal approach to the market economy in 1990, forced the government to establish stabilization programs and structural adjustment programs under the IMF direction between 1990 and 1998 which constitutes the transitional period. Moreover, by the end of 2000 and 1 early 2001, begins the post transition period, which is characterized by economic recovery programs. In this period BoA has undergone reforms to adapt to the new features. Money market is wearing a structural overliquidity aspect since the end of 2001, this has meant that the banking system is out of central bank control. 2 . Methodology we apply the method outlined by Bernanke and Blinder (1992) for the case of the BoA. We retain the assumption that the stance of monetary policy is an unobserved variable to a dimension, which reacts to changes in the final goal of the BoA namely inflation. The stance of monetary policy, though unobserved, is revealed in the behavior of a set of observed monetary variables, which we call variables or monetary policy indicators. These variables are directly influenced by monetary policy over the period. When we want to measure the orientation of monetary policy, the crucial question is to determine the variables to consider, and then we determine the transmission mechanisms of monetary policy through these indicators. Indeed, searching for the right indicator is based on the test three hypotheses which are formulated by Bernanke &Blinder (1992), and resumed by Piotrowska (1999). The first hypothesis is inspired by the implication that invokes if the Monetary Policy actually affects different sectors of the real economy, the correct measurement of the orientation of the Monetary Policy should therefore be useful in predicting real variables. In the case of Algeria we assume beforehand that the aggregate M2, would be sought this indicator. The predictive power of this indicator is checked by the Granger causality test and variance decomposition. While the second hypothesis reflects the principle that the monetary policy conducted by the BoA, seeks to achieve its ultimate target which prices stability. If this is the case, the correct measurement of the monetary policy stance should be systematically linked to the most important macroeconomic variable such as inflation. We test this hypothesis by estimating the slope of the reaction function of the BoA. Finally, the third hypothesis assumes that if the interest rate is a good measure for the actions of monetary policy, it means that would be indifferent to the changes in the demand for money in a short period . This would be true if at that period, money supply curve is extremely elastic at that interest rate. Thus, if money supply curve is vertical, changes caused by the economy to stimulate demand for money would be completely reflected by the movements of interest rates. A similar test could be applied to the monetary aggregates. If the monetary aggregate is a good indicator for PM, it should only respond to shocks in the money supply induced by the BoA, not to on the demand for money. So in the case of monetary aggregates, they will remain intact as long as the Central Bank does not take decisions to change the money stock. 2 The observations are available monthly covering the period from June 2000 to December 2014.We choose from money market instruments which contribute to absorb excess of liquidity and regulate the supply of money, namely: The Monthely average interbank rate MM-r, which is the more active interbank rate used by BoA ; Required reserves RR which represent a part of commercial deposit that are compelling to keep at the BoA (since 2005 this part is fixed at 12%); The reserve required rate RR-r: is the remuneration rate of required reserves ; The deposit facility rate DF-r: is a market instrument of a short term refinancing ; Liquidity recovery rate of 7 days LR7-r: is also is a market instrument of a short term refinancing at 7 days deadline used by BoA ; M1 and M2 as monetary aggregates; Excess reserves which are not remunerated . The amount of industrial investments Inv; Monthly salaries of permanent and non- permanent employees. Yp; the volume of household consumption C; the unemployment rate Unempl; Consumption Price Index CPI; the rate of inflation Inf; monthly positions created by companies Empl In a context of structural overliquidity, caused primarily by hydrocarbon export revenues since 2001, commercial banks are outside central bank actions. That explains the low activity of BoA, and the rigidity of interest rates which are , almost, unchanged for more than 130 months. Once we have run all the necessary tests to build a Structural VAR model, we can begin to test identified hypothesis for our research. All variables are in Log- except rates - and all are first differentiated. CPI is used as a monetary instrument policy at first. In order to test the robustness of the results, tests will be repeated for different details. 3.1 Results 3.1.1 Looking for information contained in the monetary variables First, variables which are a good indicators for measuring monetary policy stance, should be a good predictors for change in the real economy. findings show that the aggregate M2 and MM-r seem to be the indicator for monetary policy conducted by the BoA. The predictive power of variables is checked by the Granger causality test and variance decomposition. Definitely findings revealed that M2 and MM-r are considered statistically, as major indicators of monetary policy stance. See tables (1-6). First we introduce the number of lags gradually to determine the short and medium term effects of monetary variables on real variables. And we focused primarily on the relationship between monetary variables and real variables. Tables (10-13). Our tests reveal a certain significance of monetary variables on real activity. This is the case for all the monetary aggregates. However interest rates put forth significant real effects. The rates that appear in estimations are Average Monthly Interbank market rate (MM-r) and Recovery liquidity rate at 7 days (RL-7d) rates, while very significantly explain the four real variables. Both seem to be significant indicators. However they are less interesting for forecasting real variables. (Table 9) . Also, monetary aggregates M1 3 and M2 and the Required Reserves are significant. Among the rate, only LR-7d rate is significant in particular for the industrial investment variable. We see that other interest rates appear less significant. Therefore because of the specific mechanism of monetary regulation of BoA , the interest rate MM-r is more present than any other administered rates. However MM-r and M2 are the most significant. We present the results of the variance decomposition in( Table .14). .Results clearly show that for most of the variables M2 contributes most in their variances, over 24 months. While alternating the same details of previous VAR models defined namely in case of: (deletion of aggregates;Additions of interest rates ,Change in the order of variables ). We can see, that aggregate M2 has the largest share in the influence of shocks on different variables in real economic activity. Thus, we can consider M2 as the most informative variable for all real variables. In other words, the results of the variance decomposition show that M2 is more predictive than other monetary variables. Indeed, through analysis of the impact of IRF (Figure 1) , we distinguish a cyclical and staircase shape response. The observation of the impulse function, shows that the response of real variables impact of the M2 aggregate is different. according to the preceding tests, the arbitration between interbank rates and M2 aggregate is mixed. This requires test and analyze the actions of the BoA through these two indicators, we will put forward in the next section. 3.2 BoA reaction function The second hypothesis reflects that the Monetary Policy conducted by the BoA, seeks to achieve its ultimate goal which is inflation rate. If this it is so, the correct measurement of the monetary policy stance should be systematically linked to the most important macroeconomic variable such as inflation. This good indicator should be heavily influenced by the macroeconomic targets such as inflation. We test this hypothesis by estimating the slope of the reaction function of the BoA. Technically we estimate the slope using Instrumental variables. Results of estimation are presented in Tables 15 & 16. The estimation of the response function shows that the lagged variables CPI affect M2 and MM-r , according to the probability of P- Value for the regression of inflation on the M2 growth rate , in fact, the coefficients of the lagged inflation are borderline as significant. However, about MM-r, we find that the coefficients are not significant at all. The results show that the use of MM-r as an intermediate target has no impact on inflation. It is characterized by the non-significance of the parameters. See Tables 15 &16. 3.3 The elasticity of money supply slope If we consider that Average Monthly Interbank rate would be a good indicator of monetary policy. It should not be sensitive to changes in the demand for money in the given month. It is true that if for a month, money supply curve were extremely elastic at the Average 4 Monthly Interbank rate determined by the BoA. However, if the supply curve is not horizontal, regardless of the development would affect the monetary base demand, it should also change the Average Monthly Interbank rate. The verification of the hypothesis which states that Average Monthly Interbank rate should response only to shock the money supply, requires testing the slope of the function of the supply of central bank money.. Specifically, we regress the innovations of monetary variables on the innovations of the money supply, while using the innovations of real variables as instruments. If innovations macroeconomic variables contain information that BoA did not do at the time of implementation of its monetary policy for the expected month, so, regression with instrumental variables should provide an estimate of the slope the function of the money supply. With two alternatives, namely, the growth of M2 and the Average Monthly Interbank rate, and two sets of instrumental variables. If the slopes are negative and insignificant, this is consistent with the idea of the elasticity of the curve. We use the Tow Stage Least Square estimation method, we estimate two equations, each having, excess reserves explained by the interest rate MM-r by instrumental variables as a set: • Set 1: private consumption, monthly wages and industrial investments; • Set 2: industrial investments, employment and unemployment. Tables 17 & 18 show that the coefficients are positive and significant when the set I is used as instrumental variables which does mean that the MM-r is sensitive to the demand for money, and cannot constitute a right indicator for the monetary policy stance. The results show that the supply curve of excess reserves is not horizontal. This implies that the MM-r is affected by the relevance of excess reserves. And therefore it cannot be considered as an indicator of the of monetary policy stance of the BoA. A similar test will be applied for M2. If M2 is a good indicator of monetary policy, it should therefore respond only to money supply shocks caused by the BoA , and ignores the impact of demand of money. Therefore, if the supply curve is vertical base money, no change in the real economy stimulating the demand for base money, would be completely reflected in the movements of interest rates. In this case M2 will be intact as the BoA is not ready to change the money stock., as tables 19 & 20. The estimation results show that only a coefficient that is statistically significant at the 90 % level that approaches 1 ( 0.838 ) , when innovations in the instruments of the set I are used. 4. Conclusions: Overall, M2 seems to be the indicator for the conduct of monetary policy, and also to a lesser extent, the Average Monthly Interbank Interest Rate. Even in the presence of other aggregates such as M1 and Required Reserves, M2 remains statistically significant, except for the unemployment rate. We note that the Average Monthly Interbank rate is also statistically significant for most. Indeed, the superiority of M2 persists even when we change the model details (policy variable order, lags, deletion of variables ...). Moreover, we know that M2 in the monetary policy of the BoA is a determining variable, once it has a monetary consideration of the most influential in the Algerian economy, namely the foreign assets . Indeed, the foreign assets are very important because of the impact of oil export revenues since 2001. 5 When we consider the growth of excess reserves and M2 we will obtain a significant impact in some cases , which does mean that the use of monetary aggregates as an intermediate target will affect in some way the ultimate target of the BoA . The analysis of the consolidated monetary situation shows that the evolution of the monetary situation in Algeria is dominated by the foreign assets as influential factor. Actually, since 2005 foreign assets exceeded monetary and quasi-monetary liquidity in the domestic economy. However, the official foreign exchange reserves held by the BoA largely guarantees the money supply in the national economy. Also, it remains important to take in account the nature of the economy of Algeria. In fact the major part of the GFP provided from the oil exportations. In other words, the export earnings which feed the foreign assets, stoke the overliquidity in the economy. That is why the monetary policy stance indicators are not operational. Obviously, the Bank of Algeria follows the expectations; it is required to carefully consider the information provided by above indicators considered. For example, price fluctuations, and changes in inflation are the traces left by other monetary policy shocks; they are likely to be corrected by potential policy. Thus observations leads directly to use relevant information revealed by the "reaction function" of the central banks and sometimes even, prop up the level of "intermediate goals" as the change in the growth rate of the money supply or the change in money market rates. Aknowledgement: My deep gratitude and my sincere thanks go to Professor Radoslow Kurach from The University of Economics of Wroclow. 6 7 TABLES & LEGENDS Table 1: significant coefficants wthin the Original model Housholder Consumption C= 0.004+ 0.0003 C(-1) + 0,003 C-2 +1.32 Yp(-1)* + 0.02 M1(-1) +0.03 M1* (-4) + 0.099 M2(-1)* + 0.036 M(-2)+ 0.11 M2(-3) +0.029 M2(-4) + vt Industrial Inv= 0.01 + 0.01 I(-1) + 0.32 M1(-2) + 0.93 M2(-3) -0.006 RR (-2) Investment 0.013 RR( -4) 0.0014 MM-r (-1) + Vt Monthely Yp = 0.001+ 9.2 PIB-CAP (-1) + 0.11 M2(-1) + 0.014 M2 (-4) - 0.02 salaries TMM (-2) + vt Emploiment Empl = 0.006 + -0.21 C(-1) + 0.09 RL-7J (-2) - 0.003 RL-7J (-3) + vt Unemploiment 0.006 + vt inflation 0.018 + vt Source: running VAR model (output from Eviews 8.1) Table 2: significant modle with Deleting M1 : Housholder C= 0.0099 + 0.017 C-1 + 1.41 Yp(-1) + 0.74 Yp(-3) + + 0.013 C-2+ Consumption 0.53 M2(-1) -0.011 M 2 (-2)+ vt Industrial Inv= 0.6 + 3.64 Yp (-1) + 2.98 I(-3) - 0.017 RR(-1) - 0.27 RR (-3) Investment 0.19 TMM(-3) + Vt Personal Yp = 0.6 + 0.60 YD (-1) + 0.16 M2(-1)+ 0.02 M2(-3) + 0.017 TMM Income (-1) -0.028 MM-r (-4) Emploiment Empl = 0.005 + 0.097M2(-3) - 0.70 LR-7 (-3) +vt Unemploiment none inflation None Table 3: significant coeffciants after deleting M2 from original model Housholder C= 0.012 + 0.14 Yp(-1) + 0.29 Yp(-2) + 0.33 M1 (-1) + 0. 58 M1(-2)+ Consumption vt Industrial Inv= 0. 20 + 0. 39 Y(-1) + 0.77 M1 (-1) + -0.028 MM-r (-1) --0.18 Investment MM-r (-2) + 0.063 RR-rate(-1) +-0.028 RR-rate (-2) + Vt Personal Yp = 0.21. I(-4) + 0.12 M1(-1) + 0.34 M1(-2) - -0.21 MM-r (-2)+vt Income Emploiment Empl = 0.92+ -0.21 MM-r(-2) + -0.03 M1(-1) + 0.07 Inv(-4)+vt Unemploiment none inflation None Table 4: significant coefficient with changing moentary varibles order Housholder C= 119.75+ 0.017 C-1 + 0.013 C-2+ 0.33 M2(-1) + 0.72 M2 (-2) 8 Consumption Industrial Investment Personal Income Emploiment Unemploiment inflation +0.029 M1(-3) + vt Inv= 0.01 + 0.01 I(-1) + 2.98 I(-3) -0.013 RR(-1) + Vt PIB-CAP = 1.9 + 0.0013 MM-r (-2) + 0.9 M2(-3) + 9.2 Yp (-1) +) Empl = 0.017 + 0.09 RL-7 +vt none none Table 5: significant coeffcients with adding liqidity recovery at 3 months rate and TBills rate C 0.38 M2 (-1)+ 0.19 M2(-3) + M1 + 0.97 Y(-1) Inv 0.031 M2(-2) +-0.45 TMM (-1) -0.24 TMM (-2) + LR-7J + Vt Inc 0.13 M2(-1) +vt Empl none Uneml None inflation None Table 6: resuming monetry varibales presenting significant different models P=4 Original Housholder Consumption M2 M1 Industrial Investment Personal Income Emploiment Unemploimen t Source: Deleting M1 M2 Deleting M2 Addings intrests rates M1 M2 M1 levels at estimating Changing policy variable order M2 M1 Reserve obligatoire TMM RR TMM M1 TMM RR-rate M2 TMM RL-7J RR TMM M2 TMM M2 TMM M1 TMM M2 TMM M2 TMM Reprise de M2 liquidité à 7J RL-7J M1 TMM TMM RL-7J none none none none none 9 Tableau 1 lsignificant coeffcient in estimating OLS Variables M1 Housholder Consumption 0.2165 Industrial Investment 0.21674 none Personal Income 0.0119 Emploiment M2 none RR RR-r 0.2048 -0.001 MM-r LR-7 none 0.2463 none -0.006 0.0128 none -0.001 0.2226 none 0.0068 none 0.158055 none Source TABLE 10: POLICY VARIABLES GRANGER CAUSE REAL VARIABLES P=2 Original Deleting M1 Deleting M2 Changing Addings policy intrests rates variables order TMM M2 RL none none Housholder M2 M2 M1 Consumption RO Industrial TMM none Investment RO Personal M2 none none none Income Emploiment TMM none none none Unemploiment none none none none Source : élaboré par nous-mêmes à partir des estimations fournies par Eviews8. none M2 TMM none TABLE 11 POLICY VARIABLES GRANGER CAUSE REAL VARIABLES P=4 Changing Deleting Deleting Addings intrests Original policy M1 M2 rates variables order Housholder M2 M2 M1 TMM Consumption RO RL Industrial RR-r TMM none none Investment TMM RR Personal M2 non non none none Income e e Emploiment TM non non none none M e e Unemploiment non non non none none 10 e e e Source : élaboré par nous-mêmes à partir des estimations fournies par Eviews8.1 TABLE 12 POLICY VARIABLES GRANGER CAUSE REAL VARIABLES P=6 Deleting Deleting Addings intrests Changing policy Original M1 M2 rates variables order Housholder M2 M2 M1 TMM M2 Consumption RO RL-7 Industrial TM non none none Investment M e RO Personal M2 non non none M2 Income e e Emploiment TM non non none MM-r M e e Unemploiment non non none e e Source : élaboré par nous-mêmes à partir des estimations TABLE 13 POLICY VARIABLES GRANGER CAUSE REAL VARIABLES P=8 Deleting Deleting Addings intrests Changing policy Original M1 M2 rates variables order Housholder M2 M2 M1 TMM M2 Consumption RO RL-7J Industrial M2 TMM none TMM M2 Investment RO Personal M2 none none none M2 Income Emploiment TMM none none none MM-r Unemploiment none none none none None Source : élaboré par nous-mêmes à partir des estimations fournies par Eviews8.1 Housholder Consumption Industrial Investment Personal Income Emploiment Unemploiment Housholder Consumption Période M1 mois 6 24 M2 6 24 6 24 2.964856 2.964856 7.622888 RR RR-r MMr LR-7J 2.272101 2.274467 7.618336 11 Industrial Investment 6 24 8.084855 8.324318 Table 15: results of estimation the reaction function of bank of algeria * Coefficient standard Erreur Z-staistc Probabilité C(1) -0.026 0.139680 -1.205204 0.0740 C(2) 19.01636 0.138507 0.655377 0.6453 C(3) -1.474949 0.092515 1.789826 0.2286 C(4) 9.5899 0.093321 0.114993 0.0722 C(5) -4.011110 0.001967 -4.548568 0.4632 C(6) 9.345610 0.035943 1.119198 0.6577 C(7) 7.867817 0.116109 0.587648 0.1354 C(8) -1.149217 0.088319 -0.689407 0.2198 * output of estimation of latente variable by using Sspace and testing maximum of likilohood (significance at 5%) ** log liklihood 1839.460 Table 16 results of estimation the reaction function of bank of algeria * Coefficient Z-staistique** Probabilité C(1) -0.026 -0.225204 0.005204 C(2) 0.01636 0.785377 0.055377 C(3) -0.474949 1.239826 0.089826 C(4) 0.5899 0.114993 0.014993 C(5) -0.011110 -0.048568 0.048568 C(6) 0.345610 1.119111 0.019198 C(7) 0.867817 0.767648 0.087648 C(8) -0.149217 -0.339407 0.0689407 *coefficient output estamted by using Sspace frome Eviws8.1 ** M2 growth rate as intermediate target regression on inflation as final target *** log liklihood 765.89 Table19: estimation output of money supply slope Variable Coefficient erreurs t-Statistic Probability Offre de 0.838078 0.141239 0.269596 0.0778 monnaie constante -0.000929 0.003882 -0.239396 0.8111 Source : using TSLS to estimate the impact of interbank market rate on excess reserves Set 1: private consumption, GDP per capita and industrial investments; Set 2: investments, employment and unemployment. Table12: estimation output of money supply slope Variable Coefficient erreurs t-Statistic Probability 12 Offre de 0.009372 0.009004 1.040891 0.2994 monnaie constante -0.000232 0.001286 -0.180151 0.8572 Source : using TSLS to estimate the impact M2 growth rate rate on excess reserves Set 1: private consumption, GDP per capita and industrial investments; Set 2: investments, employment and unemployment. Figure 1: la fonction de réponse de l’agrégat M2 aux chocs sur l’inflation Response of DM2 to Cholesky One S.D. DCPI Innovation .004 .002 .000 -.002 -.004 -.006 2 4 6 8 10 12 14 16 18 20 22 24 Source: response function Références Bernanke B. S. & A. S. Blinder (1992): The Federal Funds Rate And The Channels Of Monetary, Transmission. The American Economic Review, Vol. 82 No.4. pp 901-921. Bernanke B. S. & I Mihov (1995): Measuring Monetary Policy. 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