Optimal Instrument jurnal

8/13/2019 Optimal Instrument jurnal

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The Optimal Instrument Rule of Indonesian Monetary Policy

Dr. Muliadi Widjaja

Dr. Eugenia Mardanugraha

Abstract

Since 1999, according to Law No. 23/1999, Bank Indonesia (BI- the Indonesian Central Bank)

set inflation targeting as the goal of its monetary policy. At the time, BI set monetary

aggregates M1 as its operational instrument. However, as BI considers that M1 is more and

more difficult to control, they change its operational instrument into nominal interest rate. The

changes are stipulated in Law No.3/2004. This paper discusses the optimal value of interest

rate as the operational instrument of Indonesian monetary policy, or known as the optimal

instrument rule. Instrument rule is defined as single mathematical expression indicating how

much value of interest rate (as a policy instrument) is the Central Bank supposes to set.

Previous examples of instrument rule are the well-known Taylor rule and McCallum rule.

Instrument rule in this paper is constructed by applying mathematical model which is then

tested empirically by using econometric method. The period of estimation for the policy

instrument in the model are quarterly monetary data from 1993-2006. The econometric

findings explain that, first, even though the setting of the nominal interest rate policy has

different direction from inflation, the changes of central bank concern, either concern oninflation stability or output stability, would not have large effect the nominal interest rate

policy. Therefore, it is time for Bank Indonesia to concern more on the output growth, since it

has only small impact to the decrease of nominal interest rate.

Second, the gap between actual inflation with the target inflation contributes as high as 32% of

nominal interest rate increase. It means that if the gap becomes 1 percent wider, Bank

Indonesia is supposed to set the increase of nominal interest rate by 32 basis points. In fact, the

increase of Bank Indonesia nominal interest rate as a short term instrument policy is frequently

lower than the optimal nominal interest rate as the result findings of instrument rule in this

research. In addition, Bank Indonesia should also be able to choose the correct timing to

increase the nominal interest rate, in order not to induce inflationary volatility but to control

inflation.


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Finally, Bank Indonesia inflation target is set too low so that it is difficult for BI to achieve it.

This too low inflation targeting diminishes the credibility of BI monetary policy, since BI

should revise it as the actual inflation rises. Therefore, our suggestion is that the target is need

to be more evaluated and announced more frequently, based on the real economy condition.


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represented by the variances of structural disturbance, affecting the magnitude of

macroeconomic parameters.

In this paper, we develop a model constructed by Guender (2003). The Guender model

discusses the structure of an economy and choosing an optimal interest rate that becomes the

monetary policy instrument. The model we develop in this paper differs from the Guender

model in a way that we explicitly explain the exchange rate behavior. After that, we estimate

the model empirically by using Indonesian quarterly macroeconomic data from 1993:1 to

2006:1 period.

The main results obtained in the paper are as follows: (i) Although the interest rate

determination policy has different direction with inflation rate, the changes of focus of the

central bank, whether the central bank concern more either on inflation stability or output

stability, would not have significant impact on the policy interest rate. Therefore, it is time forthe central bank to pay more attention on output growth, since it has only little impact on

interest rate decrease. (ii) The gap between actual inflation and the target of inflation

contributes 32% of nominal interest rate increase. It means that if the gap becomes wider by

1%, the central bank supposes to increase the optimal interest rate by 32 basis points. We find

that the increase of Bank Indonesia nominal interest rate, as a short-term instrument policy, is

usually of lower value than the optimal interest rate generated by the instrument rule of our

research. In addition, Bank Indonesia must be able to choose the right timing to increase

nominal interest rate, in order not to stimulate inflation rate but control inflation rate. (iii) The

target of inflation set by Bank Indonesia is too low, causing difficulties for the bank to achieve

it. Therefore, the set target of inflation needs more evaluation, and the announcement of the

target of inflation needs to be done more often, by looking at the actual economy condition,

forward or backward.

2. Literature reviewThere are some rules of monetary policy applied in many countries, among them are the

well-known Taylor rule and McCallum rule. The Taylor Rule can be expressed as follows:

( ) tatatt ypprR ~5.05.0 * +++= (1)


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HereRtis short term nominal interest rate that central bank uses as its instrument or operating

target. ris the long-run average real rate of interest. atp is an average of recent inflation

rates (or a forecast value), and * is the central banks target inflation rate. Finally ty~ is the

measure of the output gap, the percentage difference between actual and capacity output values.The rule suggests that monetary policy should be tightened (by increase) when inflation

exceeds its target value and/or output exceeds capacity.

The rule proposed by McCallum can be expressed as follows:

( )1** 5.0 += tatt xxvxb (2)

Here tb is the change in the log of the adjusted monetary base, i.e. the growth rate of the base

between period t-1 and t. The terms *x is target growth rate for nominal GDP , tx being the

change in the log of nominal GDP. *x is specified as ** y+ , where *y is the long-run

average of growth of real GDP. atv is the average growth of base velocity over the previous

16 quarters, ttt bxv = being the log of base velocity.

Guender (2003) constructed a monetary policy instrument using a structural model

explaining the economic behavior of a country. The Guender model generated an optimal

instrument rule of monetary policy for countries adopting the inflation targeting policy. The

rule is derived from a simple macroeconomic model in order to find a parameter gauging the

optimal policy. Having reached the parameter, we are able to estimate an optimal interest rate

policy.

Next are equations applied in the Guender model. Equation (3) and (4) indicate the main

behavior of the economy together with the central bank monetary policy. Equation (5)

indicates the objective function that the central bank would like to achieve, and equation (6)

indicates the optimal solution of the central banks goal. Those equations are written as follows:

ttttt vyEry ++= +1 (3)

0> , ),0( 2vt Nv

Equation (3), representing the IS relation, explains that output gap will increase at the

same moment with the increase of the expected output gap for the next period, and will go

down to respond the increase of real interest rate.


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ttttt uayE ++= +1 (4)

0>a , ),0( 2ut Nu

Equation (4), representing a Forward-looking Phillips Curve, explains that the current

inflation rate is the positive function of the next period expected inflation and the output gap.

( )Ttt rr += (5)

Equation (5) represents the instrument rule, where variable rtis the real interest rate that the

central bank should achieve in running the monetary policy. Variable ris the nominal interest

rate target, is the policy parameter, achieved by deriving the central bank objective function.

Variable indicates how quick the central bank adjusts its monetary policy until the inflationtarget is reached.

Min )()( tt VyVL += (6)

After that, equation (6), the central bank objective function, is the sum of 2 variables, the

variance of the output gap and the variance of the inflation. The central bank chooses a

parameter indicating its concern, whether the central bank concern more on the variability of

the output gap or inflation. Parameter is an exogenous variable determined by judgment.

( )

++= 2

2

2* 1u

vaa

(7)

Finally, equation (7) is the solution of equation (6). The parameter * depends on the

source of economic uncertainty (the variability of output gap and inflation) and also on the

preference of policy maker in running monetary policy. By substituting equation (7) to

equation (5), we will obtain the optimal nominal interest rate.

In order to implement the model on Indonesian economy and the way Bank Indonesia set

its optimal interest rate, we need to estimate equation (3) and (4) in the model. The Guender

model lays freedom to do empirical testing to gain the following optimal parameter: (a

coefficient relating the interest rate and output gap), a (a coefficient relating inflation and

output gap), the stochastic disturbance variance of each equation, and the central bank

preferenceparameter .


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3. The ModelThe goal of this research is to develop the Guender model until we achieve an optimal

nominal interest rate for Bank Indonesias target of operation. The characteristics of the model

that we applied are several. First, the instrument used as an intermediate target is the nominal

interest rate, leaving no space for a debate whether money supply or interest rate to be used as

the chosen instrument. Second, the model is constructed only for countries applying the

inflation targeting policy. Finally, the model assumes that economic agents are forward

looking in forming their expectation, applying the rational expectation. However, the Guender

model assumes a closed economy, so the model has not included the exchange rate fluctuation

explicitly. The exchange rate fluctuation is our improvement over the Guender model.

The model characteristics follow the current world economy condition, where many

countries central bank apply the inflation targeting policy and set the nominal interest rate asthe intermediate target. Meanwhile, the rational expectation theory is also a modern theory in

explaining how people form their expectation, leaving the adaptive expectation theory behind.

In explaining the central bank characteristics, besides the output and inflation stabilization, the

exchange rate stabilization could also be another main focus of central bank policy.

The model applied in this research is to compile equation (3) to (5) in a system of equation

and after that we add the following equation (8):

( ) tttttt wrreEe += + *1 (8)

Variable et is the real exchange rate as a function of the difference between real domestic

interest rate (r) and foreign interest rate (r*).

We take the first order condition of central bank objective function in equation (6) to get a

parameter determining the optimal interest rate. By following the flow of the model, we can

determine whether the actual Bank Indonesia interest rate policy (called the BI Rate) is

higher or lower or close to the estimated optimal value.

In short, we write down the system of equation as follows:

1. ( ) tttttttt veeEbyEry ++= ++ 11 IS Relation

2. ttttt uayE ++= +1 Forward Looking Phillips Curve

3. ( )Ttt rr += Instrument Rule


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4. ( ) tttttt wrreEe += + *1 Interest Rate Parity Condition

The purpose of the system of equation is to find the optimal solution value for parameter .

Having gained the estimated value of parameter and the estimated value of other coefficients,

we will gain the estimated value of the optimal central bank interest rate.An estimation problem with a forward-looking model is the difference of time period

between a dependent variable with the equations error term. The existence of time period

differences cause estimation bias on variance of errors. In fact, variance of errors is the main

component to gain the estimated instrument rule. To overcome the time period difference

problem, Rudd & Whelan (2005) express the following forward looking equation:

tt

b

tt

f

t xE ++= + 11 (9)

in term of:

111 ++ +++= tttb

t

f

t x (10)

Which are: 1+t ( the actual inflation at time t+1), 1+t : (the error of expectation), tx : ( the

output gap or unemployment rate).

According to the theory of rational expectation, the error term at equation (10) cannot be

determined at time t, so that the coefficient fcan be estimated consistently by using the same

variable at time period t or before, as a coefficient for 1+t . We estimate the coefficient f

using the following method:First, we do a backward-looking estimation to gain estimated values of 1 +t towards the

following equation:

tttt zx 32111 ++= + (11)

In equation (11), variable tz is other unknown variable stimulating inflation.

Second, we use the estimated values of 1 +t on the second stage regression between the

current inflation t as the independent variable towards the expected future inflation

(approximated by 1 +t ), the lag of inflation rate 1t and the driving variable tx , reaching the

next regression equation:

ttt

b

t

f

t x +++= + 11 (12)


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We estimate the Forward Looking Phillip Curve by using the two-stage regression method. We

also use the same method to estimate equation (3), (4) dan (8).

4. Solving the modelThe model is solved by using a post-putative solution for the three endogenous variables:

tttt wuvy 13121110 +++= (13)

tttt wuv 23222120 +++= (14)

tttt wuve 33323130 +++= (15)

Readers may find the mathematical derivation for the optimal solution in Appendix 1. the

solution of equation (6) is the parameter , derived mathematically with the following step: (i)

Substitute one equation to another to get the reduced form for output, inflation and exchangerate. (ii) Determine the coefficient of error term and the intercept of each reduced form

equation of output, inflation and exchange rate. (iii) Create the equation of output variance and

inflation variance by using the reduced form equation. (iv) Take the first order equation for

equation (6) from the equation of output variance and inflation variance created in the previous

step (v) Generate the optimal value of parameter , whose value depends on other parameters

in the structural equation. The other parameters are: the source of uncertainty in the economy

(the output gap variability and inflation variability) and the preference of the policy maker.

(vi) Substitute the optimal parameter into equation (5) to get the value of optimal nominal

interest rate.

Having included the interest rate parity equation, the solution for the objective function is:

( )( ) ( ) ( )( ) ( )[ ]222222222

21

1

1wuv babba

baL

Minimize

++++++

++= (16)

The solution for the first order condition of equation (16) is the optimal value of the following

instrument rule parameter:

( )

+++

+=

2

22

2

22* 1

u

w

u

v bab

a

(17)


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Readers may see how equation (17) differs from equation (7). Equation (17) explicitly

include the variance of errors of the exchange rate ( 2w ), derived from the interest rate parity

equation. Equation (17) stated that the optimal monetary policy not only affected by the

fluctuation of output and inflation, by also by the exchange rate fluctuation.We apply the constructed model to explain the behavior of Indonesian economy and the

way Bank Indonesia is supposed to set the optimal interest rate. In that case, we need to

estimate equation (3), (4) and (8) empirically. The parameters to be estimated from the three

equations are: (a coefficient relating the interest rate and the output gap), a (a coefficient

relating the inflation and the output gap), (a coefficient relating the exchange rate with

domestic and international interest rate parity), the variance of stochastic disturbance for each

equation, and the central bank preferenceparameter. A necessary condition for the estimated

model is that it has a forward-looking explanatory variable.

5. The Empirical EvidenceTo determine the optimal interest rate, we start from doing estimation towards three

structural equations, equation (3), equation (4) and equation (8). The best model we estimate is

the one making parameters of the three equations statistically significant. The estimation

results by using statistical software EVIEWS are shown in Appendix 2.

We do a two-stage regression for each equation. At the first stage, we estimate the expected

value of each dependent variable, then use the forecasted expected value at the first stage as the

proxy for the expected value at the second stage. Rudd and Whelan (2005) apply a two-stage

regression to avoid the model misspecification problem.

We apply the logarithm transformation for each variable in the structural equation to make

the value of estimated variance homogenous and no more dependent on the unit of data . The

Forward Looking Phillips Curveequation is being estimated by using logarithmic CPI variable

instead of inflation data, in order to generate the variance of error homogenous amongequations. The estimated parameter value of equation (3), (4), and (8) above is as follows:

a : 0.0185b : 0.2725: 0.1002: -0.0353


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The three estimated structural equation can be formulated as:

( ) tttttttt veeEyEry ++= ++ 11 *2725.0*1002.0 (18)

ttttt uyE ++= + *0185.01 (19)

( ) tttttt wrreEe ++= + *1 *0353.0 (20)

We use thesum squared residual (SSE) from each equation to estimate variance of error term

of each structural equation, following the definition: variance of error term = SSE / (T-M).

Variable T = the amount of observation and M = the amount of estimated parameter. The

estimated values of the three variance of error are:2v : 0.99372u : 0.34032w : 0.8312

Next, we substitute the values of estimated parameters and the value of variance of errors to get

the value of * in equation (17), equal to 0.318. Therefore, we conclude that the instrument

rule as the guidance for Indonesian monetary policy based on 1993-2006 data:

( )Ttt rr += *0.318 (18)

Equation (18) shows that there is a roughly 32% deviation of the difference between

inflation target and actual inflation, for the optimal interest rate from the long run interest rate.

The higher the difference between inflation target and actual inflation is, the wider the gap

between the long run interest rate and the optimal interest rate that Bank Indonesia is supposed

to achieve.

As example, in May 2008, Bank Indonesia set BI rate at 8.25%, increase by 25 basis point

from the previous period, 8%. If we follow the instrument rule we generate in equation (18),

and assume that the inflation target and the output growth target have the same value, with the

inflation target as much as 6.5%, BI is supposed to set the interest rate at 8.78% instead of

8.25%.


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Table 1

Several Values of Interest Rate and Inflation

Based on the Instrument Rule

Variable Optimal Value Actual Value

Optimal interest rate 8.78 8.25

Inflation target 6.50 8.10-- -- --actual inflation (yoy) -- 8.96previous period interest rate -- 8.00

Source : Bank Indonesia and estimation result

The instrument rule performed in equation (18) emphasizes the importance of achieving the

inflation target. The instrument rule provide space for the actual inflation to be higher 0.8%

than the inflation target, assuming that the current Bank Indonesia interest rate policy were

optimal. By that assumption, the increase of 25 basis point interest rate taken by yang BankIndonesia may only decrease inflation rate to the level 8.16%, not to its target level; i.e. 5+1%.

Our next discussion will be based on two simulations. First, a simulation that explains the

importance of the central bank to focus to its goal, whether the goal is output stability or

inflation stability. Second, a historical review comparing the set BI rate so far with the optimal

interest rate generated by the instrument rule in equation (18).

The focus of the central bank is shown by the parameter in the objective function

)()( tt VyVL += . The value = 1 shows that the central bank set the same weight betweenoutput stability and inflation stability. The higher the value of parameter is, the more

attentive the bank central towards inflation stability. The simulation on many values of

parameter and resulted on a linear function between the two parameters, as shown in

Figure 1:


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Figure 1

The Linear Function of Parameter toward Parameter

Notes: = a parameter indicates the focus of a central bank on controlling inflation. The higher the value of the

parameter indicates that the central bank prefer controlling inflation to promoting economic growth. = a parameter indicates the function of the gap between actual inflation with target inflation towards thedifference between optimal and actual interest rate.

Figure 1shows that the higher the focus of the central bank towards inflation stability is, the

larger the role of differential proportion of inflation target and actual inflation on optimal

interest rate. It means that to achieve the inflation target, Bank Indonesia must set a higher BI

rate. The implication is that, should Bank Indonesia set a too low inflation target, it will have

heavier burden to pay the interest on Certificate of Bank Indonesia (Sertifikat Bank Indonesia-

SBI), the central bank securities instrument to do open market operation.

Figure 2

Inflation, BI Rate and Optimal Interest Rate

Based on 6.5% Inflation Target y-o-y

Period August 9 April 3, 2008

= 0.0704 + 0.2468

-

0.1

0.1

0.2

0.2

0.3

0.3

0.4

0.4

0.5

0 1 2


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0

2

4

6

8

10

12

14

16

18

9-Aug-05

6-Sep-05

4-Oct-05

1-Oct-05

6-Dec-05

9-Jan-06

7-Feb-06

7-Mar-06

5-Apr-06

9-May-

6-Jun-06

6-Jul-06

8-Aug-06

5-Sep-06

5-Oct-06

7-Nov-06

7-Dec-06

4-Jan-07

6-Feb-07

6-Mar-07

5-Apr-07

8-May-

7-Jun-07

5-Jul-07

7-Aug-07

6-Sep-07

8-Oct-07

6-Nov-07

6-Dec-07

8-Jan-08

6-Feb-08

6-Mar-08

3-Apr-08

0

2

4

6

8

10

12

14

16

18

20

Actual BI Rate Optimal Interest Rate Inflation

Source: www.bi.go.id

Figure 2shows a simulation between the BI rate, optimal interest rate and actual inflation

since Bank Indonesia announced BI rate for the first time (August 9, 2005) up to date (April 3,

2008). There are two assumptions regarding how the figure is made. First, we assume that

Bank Indonesia set the same weight between output stability and inflation stability ( = 1).

Second, they set inflation target at 6.5%. We may see from Figure 2that the higher inflation

rate is, the wider the gap between BI rate and the optimal interest rate.

6. ConclusionThe step taken by Bank Indonesia to set the inflation targeting policy framework

following the worldwide monetary policy - is accurate. However, for the case of Indonesia,

Bank Indonesia does not have an instrument rule as a guidance for policy implementation yet.

The purpose of this paper is to construct a monetary policy rule for Indonesia, developed from

the Guender optimal rule (2003) and from the empirical estimation of the model. Several

conclusions from the empirical estimation result are as follows.

First, the policy based interest rate has different direction with the inflation rate. However,

the changes of the focus of the central banks policy, whether they concern more either on

stability inflation or on output stability, neither have large nor significant impact on the

changes of the interest rate policy (BI rate in this case). Therefore, it is time for Bank Indonesia


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to pay more concern on the growth of output, since it has only small impact on the decrease of

interest rate.

Second, the gap between actual inflation with inflation target contributes as high as 32% to

the increase of interest rate. As the gap increases by 1%, the optimal interest rate Bank

Indonesia is supposed to set increases by 32 basis points (0.32 %). The increase of BI rate as a

short-term policy instrument is mostly lower than the optimal interest rate generated by the

instrument rule of our research. In addition, Bank Indonesia need to choose well-timing to

increase the interest rate, as well as concern on increasing a higher long-run interest rate that is

valid in longer term.

Finally, the inflation target set by Bank Indonesia is relatively too low, make it difficult to

achieve. Therefore, Bank Indonesia needs to evaluate the target and needs more often and

regularly announce the inflation target, according to the past and present economic condition,and the future expectation.


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References

BEECHEY, MEREDITH, NARGIS BHARUCHA, ADAM CAGLIARINI, DAVID GRUENand CHRISTOPHER THOMPSON (2000), A Small Model of the AustralianMacroeconomy, Economic Research Department.Reserves Bank of Australia. Research

Discussion Paper.

DEBELLE, GUY and JENNY WILKINSON (2002), Inflation Targeting and The InflationProcess: Some Lessons from an Open Economy, Economic Research Department.Reserves Bank of Australia. Research Discussion Paper.

GUENDER, ALFRED V. (2003), Optimal Monetary Policy under Inflation Targeting Basedon an Instrument Rule,Economic Letters.78 (2003) 55-58.

LEITEMO, KAI & ULF SDERSTRM. (2005), Robust Monetary Policy in A Small OpenEconomy,Bank of Finland Discussion Papers .20.

MAJARDI, FAJAR (2004), Pembentukan Model Makroekonomi Skala Kecil (SSMX),Bagian Studi Sektor Rill. Direktorat Riset Ekonomi dan Kebijakan Moneter. BankIndonesia.

RUDD, JEREMY & KARL WHELAN (2005), New Test of The New-Keynesian PhillipsCurve, Journal of Monetary Economics.52(2005) 1167-1181.

Website of Bank Indonesia at www.bi.go.id


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Appendix 1

Substitute Phillips Curve eq(4) to instrument rule eq (5)

(A1) ( )Tttttt uayErr +++= +1

Substitute eq (A1) to IS Relation eq (3)

(A2) ( )( ) ( ) ttttttTttttt veeEbyEuayEry +++++= +++ 111

Substitute eq (8) to eq (A2)

(A3) ( )( ) ( )( ) ttttttTttttt vwrrbyEuayEry +++++= ++ *11

Substitute eq (A1) to eq (A3)

(A4) ( )( ) 11 ++ ++++= ttTttttt yEuayEry

( )( )( ) tttTtttt vwruayErb ++++ + *1 Reduced Form IS Relation

(A5) ( )( ) ( )( ) 111 ++ +++=++ ttTtttt yEuErbay

( )( )( )ttt

T

ttt vwruErb +++ +*

1

Putative Solutions for endogenous variables:

(A6) tttt wuvy 13121110 +++=

(A7)tttt

wuv23222120

+++=

(A8) tttt wuve 33323130 +++=

The Expected Values

(A9) 101 =+ttyE

(A10) 201 =+ttE

(A11) 301 =+tteE

Solution for parameters in eq (26)

(A12) 010=

(A13)( )

ba ++

=1

111


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(A14)( )

( )

ba

b

+++

=112

(A15)( )

ba

b

++=

113

Substitute eq (21) to eq (4)

(A16) ( )( ) ttTttttttt wruayEreEe ++++= ++ *11

Substitute eq (26) and the expected values to eq (29)

(A17) ( )( )( ) ttTttttttt wruwuvaEre +++++++= + *13121110130

Solution for parameters in eq (28)

(A18)( )

ba

a

++

=131

(A19)( )

ba ++

=

132

(A20)( )

ba

ab

++=

1133

Substitute eq (26) and the expected values to eq (2)

(A21) ( ) ttttt uwuva +++++= 1312111020

Solution for parameters in equation eq (27)

(A22)( )

ba

a

++=

121

(A23) ( ) ba ++= 11

22

(A24)( )

ba

ab

++=

121


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The objective function is:

(A25)( )( )

( ) ( )( ) ( )[ ]2222222222

11

1wuv babba

baL

Minimize

++++++

++=

The policy maker is assumed to set the parameter value that minimizes the loss function. The

parameter optimal value of the instrument rule is:

(A26) ( )

+++

+=

2

22

2

22* 1

u

w

u

v bab

a


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Appendix 2

EVIEWS output of estimation of theIS Relationequation

Dependent Variable: GDPGAPMethod: Least SquaresDate: 05/26/08 Time: 11:23

Sample(adjusted): 1993:2 2006:4Included observations: 55 after adjusting endpointsConvergence achieved after 3 iterations

Variable Coefficient Std. Error t-Statistic Prob.

C 11921.81 2312.085 5.156304 0.0000AR(1) 0.415229 0.124628 3.331742 0.0016

R-squared 0.173173 Mean dependent var 11963.38Adjusted R-squared 0.157573 S.D. dependent var 10924.12S.E. of regression 10026.58 Akaike info criterion 21.29955Sum squared resid 5.33E+09 Schwarz criterion 21.37255Log likelihood -583.7377 F-statistic 11.10050Durbin-Watson stat 2.033944 Prob(F-statistic) 0.001578

Dependent Variable: LOG(ABS(GDPGAP-GDPGAPF(+1)))Method: Least SquaresDate: 06/05/08 Time: 13:38Sample(adjusted): 1993:1 2006:3Included observations: 55 after adjusting endpoints


C 8.475644 0.906749 9.347283 0.0000LOG(ABS(USDF(+1)-

USD))-0.272458 0.156727 -1.738426 0.0882

0.4*SBI -0.250437 0.074731 -3.351183 0.00150.6*SBC 0.256445 0.073092 3.508503 0.0010

R-squared 0.209304 Mean dependent var 8.515750Adjusted R-squared 0.162792 S.D. dependent var 1.089477S.E. of regression 0.996861 Akaike info criterion 2.901536Sum squared resid 50.68032 Schwarz criterion 3.047524Log likelihood -75.79224 F-statistic 4.500040Durbin-Watson stat 1.693048 Prob(F-statistic) 0.007078


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EVIEWS output of estimation of thePhillips Curve equation

Dependent Variable: CPIMethod: Least SquaresDate: 05/26/08 Time: 13:07Sample(adjusted): 1993:3 2002:4Included observations: 38 after adjusting endpoints


CPI(-1) 1.499453 0.147086 10.19441 0.0000CPI(-2) -0.483853 0.151699 -3.189548 0.0029

R-squared 0.989905 Mean dependent var 60.88457Adjusted R-squared 0.989625 S.D. dependent var 25.59550S.E. of regression 2.607161 Akaike info criterion 4.805597Sum squared resid 244.7024 Schwarz criterion 4.891785Log likelihood -89.30634 Durbin-Watson stat 2.190726

Dependent Variable: LOG(ABS(CPI-CPIF(+1)))Method: Least Squares

Date: 05/26/08 Time: 13:02Sample(adjusted): 1993:3 2006:3Included observations: 53 after adjusting endpointsConvergence achieved after 9 iterations


LOG(GDPGAP) 0.050048 0.022769 2.198087 0.0325AR(1) 0.630649 0.107693 5.855962 0.0000

R-squared 0.385247 Mean dependent var 0.564072Adjusted R-squared 0.373193 S.D. dependent var 0.736845S.E. of regression 0.583368 Akaike info criterion 1.797009Sum squared resid 17.35625 Schwarz criterion 1.871360Log likelihood -45.62075 Durbin-Watson stat 2.247950

Inverted AR Roots .63


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EVIEWS output of estimation of theInterest Rate Parity equation

Dependent Variable: USDMethod: Least SquaresDate: 05/23/08 Time: 16:56Sample: 1993:1 2006:4Included observations: 56


SING 0.903575 0.238794 3.783906 0.0004YEN 0.530061 0.164382 3.224573 0.0021

R-squared 0.961032 Mean dependent var 6738.769Adjusted R-squared 0.960310 S.D. dependent var 3419.728S.E. of regression 681.2910 Akaike info criterion 15.92092Sum squared resid 25064500 Schwarz criterion 15.99325Log likelihood -443.7857 Durbin-Watson stat 2.427764

Dependent Variable: LOG(ABS(USD-USDF(+1)))Method: Least SquaresDate: 06/03/08 Time: 11:30Sample(adjusted): 1993:1 2006:3Included observations: 55 after adjusting endpoints


C 5.756505 0.177871 32.36329 0.0000ABS(SBI-FEDRATE) 0.035255 0.011029 3.196471 0.0023

R-squared 0.161624 Mean dependent var 6.167423Adjusted R-squared 0.145805 S.D. dependent var 0.986432S.E. of regression 0.911686 Akaike info criterion 2.688644Sum squared resid 44.05211 Schwarz criterion 2.761638

Log likelihood -71.93772 F-statistic 10.21743Durbin-Watson stat 1.581512 Prob(F-statistic) 0.002346

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