159 stochastic simulation to be 1000 so that the sampling variation in the statistics that we
obtain is relatively small.
In generating the innovations of the stochastic equations, we use the values of the standard deviations calculated from the behavioural equations in our model. The
exception is exchange rate innovations in the UIP equation, which are assumed to be zero. To simulate the distributions of the random error components, we use a Monte
Carlo approach as follows. At each replication of a stochastic simulation, a set of independent random numbers is drawn from the standard normal distribution, and then
these numbers are scaled to match the actual variance-covariance matrix of the system.
14
This ensures that the correlations of the random draws match the correlations of the observed equation residuals. The model is solved many times with pseudo-random
numbers substituted for the unknown errors at each repetition. Finally, we employ the same Gauss-Seidel algorithm to solve the model as in the deterministic case.
5.6.2 A Quest for Best Monetary Policy Response
In this sub-section, we will experiment with two types of monetary policy rules, namely, the Taylor interest rate rule and the McCallum money supply rule. Since the primary goal
of monetary policy in the Indonesian economy is to achieve sustainable non-inflationary economic growth in the medium to long-run, our aim is to understand which policy rule
is likely to be more effective in bringing inflation down to a targeted level with less variability in output. A comparison of the volatility of the economy under simulation with
14
In the general case, this involves multiplying the vector of random numbers by the Cholesky factor of the covariance matrix. If the matrix is diagonal, this reduces to multiplying each random number by its desired
standard deviation.
160 the volatility in the data will therefore provide some indication of the extent to which the
conduct of monetary policy can be improved using these alternative policy rules.
In the presence of forward-looking variables in the Indonesian SSMM, the public and private sector’s beliefs about the future course of monetary policy will affect the
stability of the economy. Thus, an appropriate monetary policy will depend, to some extent, on whether BI chooses to make binding commitments with regard to future
monetary policy or not. In the former case, BI should keep the policy rule fixed across periods whereas in the discretionary case, BI updates the optimal policy rule every period
by taking into account new information. Although in practice discretionary monetary policy rule approximates reality better, this is feasible only if the central bank possess the
necessary credibility to pursue monetary policy without adversely affecting inflationary expectations Clarida, Gali, and Gertler 1999. As argued in the previous sub-section, we
hesitate to assume that such credibility in the Indonesian monetary authority is present. Hence, we feel that employing a committed monetary policy rule seems to be more
appropriate for our study.
Because of the issues mentioned above, we will focus on simple rules that feed back from a subset of the state variables, such as the inflation gap, the output gap, and the
nominal exchange rate. We will specifically consider the Taylor rule augmented with the nominal exchange rate change estimated earlier. The dependence on imported goods for
domestic production as well as our earlier empirical findings suggest that responding to movements in the exchange rate is likely to be important for output and inflation
161 variability see also Leitemo and Soderstrom, 2000. For comparison, we will employ
another simple rule, i.e. the money supply rule due to McCallum 1988 that is described in Chapter 4 as
t t
t t
x x
k v
m
7 1
1
ε λ
+ −
+ =
+ Δ
− ∗
∗ −
. Again, we use OLS to estimate the rule and follow the original McCallum specification in using money growth as the policy
instrument.
15
The estimation yields a parameter estimate of 0.038 or 3.8 for the long- run nominal output growth that is quite close to the assumption made by McCallum of
4.5. The elasticity of the output gap is found to be 0.45, which is again close to McCallum’s assumed value of 0.5.
We follow the literature closely by evaluating the performance of the alternative rules according to the extent to which they are able to stabilize the target variables.
Hence, we follow Taylor 1993 in tracing out and plotting the trade-off between output and inflation volatility, the so-called “policy points”
16
, under our estimated Taylor and McCallum rules, and then comparing them to the historical pattern in the data. The policy
points are traced out under the assumption of no cost to instrument volatility. We compare the trade-off points in the rules and the historical data for our full sample period
and also three sub-periods: 1983:Q1–1986:Q4, 1987:Q1–1995:Q4, and 1996:Q1– 2004:Q1.
15
We use M1 money growth instead of base money growth in McCallum 1988 and follow McCallum and Nelson 1999 in formulating the velocity.
16
This is a set of combinations of inflation and output gap volatility that can be attained given a particular form of policy rule. When we vary the relative weights on inflation, output, or the exchange rate, we trace
out a “policy frontier” instead.
162 The variability of the total output gap is expressed as the standard deviation of the
percentage deviation of output from its long-run trend.
17
The variability of inflation is measured directly by its simulated standard deviation. Our first result, presented in Figure
5.18, shows that during the period 1987–1995, the volatility of inflation and the output gap is the smallest. This coincides with the period of rapid growth and economic
recovery in the Indonesian economy during which a lower inflation rate is combined with significant improvements in real income growth, as discussed in Chapter 2. Not
surprisingly, after the Asian financial crisis hit Indonesia in 1997, the variability of both output and inflation increased substantially.
Figure 5.18 Comparison of Monetary Policy Rules
1 2
3 4
5 6
0.0 0.5
1.0 1.5
2.0 2.5
3.0 3.5
4.0 4.5
5.0 Taylor Rule
Money Supply Rule Historical Data 1983-1986
Historical Data 1987-1995 Historical Data 1996-2004
Historical Data Full Sample
Inflation Variability O
u tp
ut G a
p V a
ri ab
il it
y
17
To be consistent with the estimation procedure, we employ the Hodrick-Prescott filter to measure the long-term trend of output.
163
Table 5.7 Output Gap and Inflation Volatilities
Classification of Periods Output Gap
Variability Inflation Variability
Taylor Rule Full Sample 3.08
3.26
Money Supply Rule Full Sample 3.16
3.16
Historical Data Full Sample 3.52
3.19
Historical Data 1983–1986 2.69
1.28
Historical Data 1987–1995 1.49
0.88
Historical Data 1996–2004 5.1
5
When we compare the performance of the Taylor rule with the full sample’s actual output and inflation volatilities, we find that the two trade-off points are quite close
to one another. Nevertheless, by employing the rule, there will be smaller fluctuations in the output gap Table 5.7. Similarly, the money supply rule does not improve much on
actual historical outcomes in terms of the trade-off between output and inflation variability. This result is to be expected because during the bulk of the sample period
under study, Indonesia’s monetary policy was mainly conducted using base money as the operational target, with the nominal exchange rate being depreciated at a fairly steady
rate in a crawling peg setting Alamsyah et al 2000.
164 The empirical results suggest that the alternative monetary policy rules are
substitutes for one another, given the similar trade-off points that they generated. It is perhaps surprising that the use of the Taylor rule augmented with the exchange rate
change did not improve output and inflation outcomes considerably. To some extent, this could be due to the fact that the parameters in the rule used for the stochastic simulations
are estimated and as such, are not optimal. In the next section, we will experiment with different calibrations of the parameters in order to derive a more optimal policy rule for
inflation targeting.
5.6.3 The Policy
