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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
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