Should the central banks of emerging markets
respond to movements in stock prices?
Mauricio Nunesa, Sergio Da Silvab*
Department of Economics, Federal University of Santa Catarina, 88049970 Florianopolis SC, Brazil
E-mail address: mauriciosimianonunes@gmail.com
E-mail address: professorsergiodasilva@gmail.com
Tel.: +55 48 8804 7489
Fax: +55 48 3721 9901
*Corresponding author
Abstract
Should the central banks of emerging markets respond to movements in stock prices?
Using a dynamic stochastic optimization model we find the answer to be “no” for the
central banks that already adopted inflation target regimes.
JEL classification: E44; E52; E58; E65
Keywords: Stock prices; Monetary policy; Emerging markets; Inflation targeting
1. Introduction
There is consensus among many that asset bubbles have damaging effects on real and
financial variables through the monetary policy analytical channels, i.e. the wealth and
credit constraints effects on consumption and investment. However, whether central
banks should respond to asset prices movements is still a controversial issue (see
Roubini, 2006). The prevailing viewpoint of the US Federal Reserve, for instance, is
that nothing can be done about rising bubbles and everything needs to be done to avoid
the damage of bursting bubbles. Monetary policy should not preemptively react to an
asset bubble that is uncertain and thus difficult to track and “prick”, but should strongly
react after the bubble bust. Yet a number of theoretical models challenge this view by
suggesting that an optimal monetary policy should react to asset prices beyond the
deviation of output and inflation from target (see Roubini, 2006 and references therein
for an overview of the debate; extra works on the subject are also shown in Table 1).
Nevertheless, in the model of Bernanke and Gertler (2001) if the central bank is
committed to inflation targeting it is not generally desirable that monetary policy reacts
to asset prices movements, unless the latter also affect expected inflation. This contrasts
with the models in Cecchetti et al. (2000, 2003), who argue that though asset prices
should not be the main focus in an inflation target regime, if agents anticipate that the
monetary policy is leaning against the wind there is still room for successfully
counteracting asset price bubbles. The results of Bernanke and Gertler (2001) were
found in the context of nonoptimizing monetary policy rules. Thus it has been
suggested that they are not robust with respect to optimal policy rules (Filardo, 2004).
Here we will contribute to the debate by considering a dynamic stochastic model (where
the policy rule is chosen optimally) along with stock prices data from 22 emerging
markets. We will show that the view of Bernanke and Gertler receives partial support
from the data for the countries that adopted inflation target regimes.
In our model the central bank minimizes an intertemporal loss function subject
to a linear stochastic equation system representing the economy. We will find that
disregarding the stock prices minimizes the central bank loss function for the countries
that adopted inflation targets. By contrast, considering the stock prices in the central
bank reaction function minimizes inflation variance for the countries that adopted an
accommodative policy regime.
The rest of the paper is organized as follows. Section 2 will present our model
in two versions, namely one that includes the stock prices and one that disregards them;
the model will then be solved for both variants. Section 3 will present our data set from
the 22 emerging markets. The data will be used to calibrate the alternative models in
Section 4. Section 5 will describe our results, and Section 6 will conclude. The details
of the model optimization will be relegated to an appendix.
2. Modeling
To assess whether the authorities should consider the stock prices in their monetary
policy decisions we take an optimal policy rule model whose solution is an interest rate
reaction function. The model can be used to simulate the central bank optimal policy
decision through adjustments of the control variable (the monetary policy tool) in a
series of time periods.
In the unrestricted version of the model the stock prices enter the central bank
reaction function; in the restricted version the stock prices are left out. The model
equations assume a small open economy.
2.1. Model with stock prices
In the unrestricted version the central bank minimizes an intertemporal loss function
subjet to a system of linear stochastic equations representing the economy, i.e.

  ( t   *)2 (1   ) yt2 
min Et    t 


2
2
 1


(1)
s.t.
yt  1   2 yt 1  3it   4 (et  et 1 )  5 ( pt  pt 1 )   t
(2)
 t  1  2 t 1  3 yt  t
(3)
et  et 1   1   2 (et 1  et 2 )   3it  t
(4)
pt  pt 1  1  2 ( pt 1  pt 2 )  3it  t .
(5)
Equation (1) is a deterministic loss function for a number of finite time periods,   1 is
the central bank discount rate, and  and 1   are the penalties associated with the
deviations of inflation from target  t   * and with the output gap yt respectively.
Minimizing inflation and output gap variability posits a tradeoff. If the central bank
chooses a strict inflation target policy then   1 . For 0    1 the central bank is
committed to the joint objectives of dampening inflation and output gap variability.
Equations (2) and (3) are demand and supply functions respectively. Those
extend the ones in the inflation target model of Rudebusch and Svensson (1999). The
demand function relates output gap with past output gap yt 1 , nominal interest rate it ,
real exchange rate changes et  et 1 , and real stock returns pt  pt 1 . The supply
(Phillips) curve relates inflation rate with past inflation  t 1 and output gap yt . The  t
and  t terms are white noise demand and supply shocks respectively, and the s and
 s are parameters.
Equations (4) and (5) show that real exchange rate changes and stock returns
relate to the nominal interest rate as well as present first order autoregressive
components, i.e. et 1  et 2 and pt 1  pt 2 . Interest rate rises make domestic stocks
more attractive, appreciate domestic currency, and then reduce stock prices. The
financial shocks  t and t are considered as white noise as well, and the  s and  s are
parameters.
Equations (2)(5) track the monetary policy transmission mechanism through
the stock prices because the optimal interest rate is adjusted not only to minimize both
inflation deviation from target and output gap but also to influence the exchange rate
and stock prices. The exchange rate and stock prices affect output gap, which in turn
influence inflation; in other words, the financial variables affect inflation indirectly
through their impact on output gap.
2.2. Model without stock prices
The alternative version of the model is given by

min Et  
 1
 t
  ( t   *)2 (1   ) yt2 



2
2


(1)
s.t.
yt  1   2 yt 1  3it   4 (et  et 1 )   t
(6)
 t  1   2 t 1  3 yt  t
(3)
et  et 1   1   2 (et 1  et 2 )   3it  t .
(4)
Equation (6) is obtained from equation (2) after dropping the stock prices; this means
that the latter no longer influence output gap. While equation (5) is dismissed in this
version of the model, exchange rate changes still affect output gap (as in Ball, 1999,
Batini and Nelson, 1999, and Svensson, 2000).
2.3. Solving the models
The model versions in Section 2.1 and 2.2 can be rewritten as an optimal control
problem as follows.

min E0 Wt  E0  (y t  at )T K t (y t  at )
(7)
t 1
s.t.
y t  At y t -1  Ct xt  bt  ut
(8)
Loss function (7) is minimized subject to the first order difference equation system (8).
The Wt is a matrix of the components of central bank loss, y t is a vector of current
endogenous variables, a t is a vector of the targets for each variable in y t , and K t is a
symmetric diagonal matrix representing the penalties related to the deviations of a
variable from target (the elements of the diagonal are zero for the lagged variables and
positive for the endogenous variables chosen to enter the loss function), y t -1 is a vector
of lagged endogenous variables, xt is a vector of the current control variable, At and
Ct are matrices of parameters, bt is a vector of parameters, and the stochastic
disturbance vector ut is serially uncorrelated with mean zero and covariance matrix Ω .
Symbol T refers to the transpose. The 0 subscript of the expectations operator in (7)
means that the loss is minimized assuming vector y 0 as an initial condition. Despite the
time subscripts we assume At , Ct , and bt to be constant over time (and also K t as we
will see below). Since we have chosen a deterministic loss function for analytical
convenience, we have also to accept the choice related opportunity cost, i.e. we will get
only an approximate solution to the problem.
We apply the Langrange multiplier method for the dynamic optimization
alghorithm to find results that are similar to those from the Bellman dynamic
programming method. The resulting reaction function is a linear function of current and
one-period lagged endogenous variables, i.e.
xt  Gt y t -1  gt
(9)
where G t is the matrix of coefficients representing the sensitivity of the control variable
to the arguments in the reaction function, and gt is the reaction function intercept
vector. To calculate the reaction function each period one just needs to find G t and gt .
The two alternative models above can be rewritten in matrix notation:
y

 
t

 
 t
 

 
e

e
 t t 1   

 
 pt  pt 1  

 
i
t

 


  




  

















yt
y
 
     
    



  t 
t 1
 



  
           
   t 
            t 
       






  
e

e


 it   
  t 1 t  2   
  t 
 



  



  pt 1  pt  2  

 t 






  
i



t 
 

  0 
 
At
y t 1
Ct
xt
bt
(10)
ut
and
 yt   2
  t   1
 et et 1    0
 i  0
 t  
yt

 t 
0
 4 2 0   yt 1    3  4 3 
1  4 1

  
 2 3 4 2 0    t 1   3 3  3 4 3 








1
3
1
3
4
1
   t 
it   




  


0
2
0  et 1 et  2
3
1
t
0
0
At

0 
it 1
y t 1
 
 
1
Ct




xt
0
bt


(11)
 
 0 
ut
Table 2 shows the reduced forms. Since the values of matrices At and Ct and of
vector bt are known, the elements of matrix K t can be defined so as loss function (7)
includes only inflation deviation from target and output gap. Matrix K t and target
vector a t for the two versions of the model are given by

0

Kt   0

0
 0
0 0 0
(1   ) 0 0 0 
0
0 0 0

0
0 0 0
0
0 0 0 



at   (et

( pt




 t*

 et 1 )* 

 pt 1 )* 

it*

(12)

0
Kt  
0

0
0 0
(1   ) 0 0 
0
0 0

0
0 0


at  
 (et






*
 et 1 )

it*

(13)
0
yt*
and
0
yt*
*
t
for t  1 4 . As loss function (7) is calibrated with the definitions of K t and a t it
reduces to loss function (1). The fact that K t is constant means that the central bank
does not change its preferences over four periods regarding the relative importance of
inflation and output. However, the central bank targets are allowed to change. Using
At , Ct , bt , K t along with a1 , a 2 , a3 , and a 4 one can find the matrices G1 , G 2 , G 3 ,
G 4 and g1 , g 2 , g3 , g 4 , and then the interest rate reaction function (9) through
Lagrange method.
After optimization, the interest rate reaction function of the model with stock
prices is given by
it   1,t
 2,t 3,t  4,t
 yt 1 
 

t 1


5,t   et 1  et 2    gt 


 pt 1  pt  2 
 it 1

(14)
T
where xt  it  , G t  1,t  2,t 3,t  4,t 5,t  , y t -1   yt 1  t 1 et 1  et  2 pt 1  pt 1 it 1  ,
and gt   gt  . For the model without stock prices the optimal reaction function is
it   1,t
 2,t 3,t
 yt 1 
 

t 1
 g 
 4,t  
t
 et 1  et  2 


 it 1 
(15)
T
where xt  it  , G t  1,t  2,t 3,t  4,t  , y t -1   yt 1  t 1 et 1  et  2 it 1  , and gt   gt  .
Using G1 , G 2 , G 3 , G 4 and g1 , g 2 , g3 , g 4 one can get the optimal reaction functions
each period. The details of the optizimation procedure are presented in the Appendix.
3. Data
We collected quarterly data from Datastream for 22 emerging countries in the Standard
& Poors’ Emerging Markets Data Base. The countries were as follows. Argentina,
Brazil, Chile, China, Colombia, Czech Republic, Indonesia, India, Israel, Korea,
Malaysia, Mexico, Peru, the Philippines, Poland, South Africa, Russia, Sri Lanka,
Thailand, Taiwan, Turkey, and Venezuela. Table 3 presents every sample’s time
periods. Consumer price indices were taken from the IMF’s International Financial
Statistics. The other variables were stock prices, as measured by a country’s
stockmarket index, gross domestic products and industrial outputs, nominal interest
rates, and real exchange rates. Interest rates were money market rates and either
interbank or discount rates for the countries where money market rates were not
available. The real exchange rates were calculated using the dollar price in terms of a
country’s currency discounted by the difference between a country’s inflation and that
of the United States.
Analysis was performed with the natural logs of the variables. Output data were
deseasonalized using the X-11 ARIMA method, and the output gap was found using the
potential output estimated by Hodrick-Prescott filter with smoothing constant of 1600.
4. Calibration
Rather than adopting a purely analytical approach, we decided to employ the data to
calibrate the model parameters in Section 3, and then resume analysis. We used a panel
model with fixed effects considering standard errors robust to heteroskedasticity and
autocorrelation. The t-statistic was reckoned for every coefficient with the significance
levels of up 10 percent. Once a model was estimated, dummies considered important to
capture outliers and currency crisis shocks as well as to remove heteroskedasticity and
lack of normality were included in some cases. Akaike and Schwarz diagnostic tests
were performed to help us decide where to include either a dummy or constant of an
estimated equation. Table 4 shows the results of the estimated regressions.
Overall most of the coefficients were statistically significant and theoretically
consistent. The inflation and output gap coefficients were positive and real exchange
rate changes did not enter the reaction function of the model without stock prices. The
financial variables entered the reaction function of the model with stock prices, which
also presented greater interest rate sensitivity to inflation and output gap.
The estimated IS equations of the two model versions showed that the financial
variables contemporaneously impact output gap. The lagged values of the dependent
variable also presented some persistence with average coefficients of nearly 0.6. The
estimated Phillips curves showed that the output gap coefficient was statistically
significant in explaining inflation in the emerging countries. The estimated lagged
coefficient of the dependent variable also showed that on average there is some
persistence of past inflation on current inflation. The interest rate was also statistically
significant in the equations for stock prices and real exchange rates. This means that
foreign exchange and stock markets act as transmission channels for monetary policy.
5. Empirical results
After considering the data from emerging countries the optimization results for the loss
and reaction functions described in Section 2 are given in Table 5. Table 5A shows the
results for the model with stock prices, and Table 5B presents the results for the model
without stock prices. Because we considered quarterly data and the optimization was
performed annually, the reaction functions were estimated for four periods. Possible
discrepancies in the definition of the optimal interest rate could be assessed through
comparisons between the reaction functions across periods. The annual loss function
was then the sum of the estimated losses each period, i.e. Wannual   t41 Wt .
Reaction function (9) depends on both the coefficient matrix G t and the
intercept vector gt . As shown in the Appendix, the values of G t do not depend on the
initial conditions and the inflation and output targets. To get the values of g1 , g 2 , g3 ,
and g 4 we need to know the values of the targets a t . The output gap target is set by
definition at zero. However the inflation target, if any, varies across countries.
Following Fischer (1996), here we set the inflation target at 0.5 percent per quarter
(nearly 2 percent a year). The initial conditions are set at the endogenous variables’
values at the fourth quarter of 2006.
We consider two types of central bank preferences, namely (1) explicit inflation
target, i.e. aggresive reaction to the deviations of inflation from target (   0.9 ) without
too much concern with output gap ( 1    0.1 ), and (2) no inflation target, i.e.
accomodative reaction to the deviations of inflation from target by equally penalizing
the deviations of inflation and output gap (   0.5 ). Table 5 shows the results in the
two alternative models for the loss functions in both monetary policy regimes. As
expected, the interest rate sensitivity to past inflation was always positive. This means
that the central banks reacted by raising the nominal interest rate whenever there was
inflation in the previous period. In the model with stock prices the inflation rate
coefficients were found to be greater than one, which means large interest rate
sensitivity to inflation. The central banks’ reaction to output gap was found to be
positive and less than one, which means that interest rate increases impact output
negatively. Yet current output also depended on past output and thus a rise in interest
rates did not necessarily make current output inferior to past output. Also, the real
exchange rate did not take part of the model without stock prices. This means that only
in the model with stock prices the interest rate increases made domestic stocks relatively
more attractive and thus appreciated domestic currency. In the model without stock
prices there was low temporal sensitivity of the coefficients in the arguments of the
reaction function. Yet in the model with stock prices the coefficients changed between
the first and the fourth optimization period. As for the minimization of the loss
function, the best monetary policy regime for the emerging countries adopting an
explicit inflation target was not to include the stock prices in their central banks’
reaction function. However, considering the stock prices in the reaction function seems
to minimize the loss function under a regime of no inflation target.
6. Conclusion
Using a optimal control model framework this paper considered two alternative models,
one with stock prices and one without stock prices to evaluate whether monetary policy
decisions should respond to movements in stock prices. This question was assessed
after calibrating the model parameters with data from 22 emerging countries.
In the models, the central bank adjusts the interest rate each period so as to
minimize an intertemporal loss function subject to a stochastic linear equation system
that tracks the major characteristics of the emerging economies.
Overall our results showed that it is optimal for the central banks adopting
explicit inflation targets not to consider stock price movements, that is, disregarding the
stock prices minimizes the central bank loss function. By contrast, it is optimal for the
central banks with no inflation target to consider the stock prices in their reaction
function because this minimizes inflation variance.
Acknowledgements. SDS acknowledges financial support from the Brazilian agencies
CNPq and CAPES-Procad and MN acknowledges support from CNPq.
Appendix
Here we present the details of the optimal control problem through the Lagrange
method. The optimal rule problem can be represented by

min Wt   ( y t  at )T K t ( y t  at )
(A1)
t 1
s.t.
yt  At yt -1  Ct xt  bt
(A2)
where y 0  y0 , yt   yt  t et  et 1 it  , and xt   it  . Overbars indicate the matrices of
the deterministic model. Because we have assumed that the shocks in equations (1)
through (5) are white noise we further assume that vector ut in equation (8) equals its
mean zero.
Forming the Lagrangean by introducing vector λ t and then differentiating yields
T
L1 

1
2
 (y
t 1
10
t
 at )T K t ( y t  at )   λ Tt (y t  At y t -1  Ct xt  bt )
(A3)
t 1
Then we set all of its first-order partials equal to zero:
L1
 K t ( y t  at )  λ t  ATt 1 λ t 1  0
y t
(A4)
L1
 CTt λ t  0
xt
(A5)
L1
 ( y t  At y t -1  Ct xt  bt )  0
λ t
(A6)
where t  1  and λ11  0 . These are three equations in three unknowns, xt , y t , and
λ t . Matrices At , Ct , K t , and vector bt have already been defined and assumed to be
constant, and vector at has also been defined and assumed to vary from t  1 to t  4 .
To solve the system (A4) through (A6) we first consider t   and then repeat
the four steps to be presented below for t    1,   2, ,   1 . The first step is to
write λ t as a function of y t using equation (A4). Then we employ the initial conditions
Ht  K t and ht  K t at for λ t 1  0 to get
λ t  K t y t  K t at  ATt 1λ t 1  H t y t  ht
(A7)
The second step is to write xt as a function of y t . Substituting (A6) and (A7) into (A5)
yields
CTt λ t  0  CTt (H t y t  ht )  CTt (H t At y t -1  H t Ct xt  H t bt  ht )
(A8)
xt  G t y t 1  gt
(A9)
or
where
G t  (CTt H t Ct ) 1 CTt H t At
(A10)
g t  (CTt H t Ct ) 1 CTt (H t bt  ht )
(A11)
and
The third step is to write y t and λ t as a function of y t . Substituting (A9) into (A6), we
have
yt  (At  Ct G t )y t 1  bt  Ct gt
(A12)
Then substituting (A12) into (A7) yields
λ t  Ht (At  Ct Gt )yt 1  Ht (bt  Ct gt )  ht
(A13)
The final step is to find an equation similar to (A13) for λ t 1 . This can be done by
substituting (A13) into (A4) lagged one time period:
λ t 1  K t 1y t 1  K t 1at 1  ATt λ t  H t 1y t 1  ht 1
(A14)
H t 1  K t 1  ATt H t ( A t  Ct G t )
(A15)
ht 1  K t 1at 1  ATt H t (bt  Ct g t )  ATt ht
(A16)
where
and
The routine above from (A9) through (A16) goes on by replacing t   with t   1 ,
and so forth.
Having found the reaction function, we can find inflation deviation from target
and output gap. The deterministic loss function

Wt   ( y t  at )T K t ( y t  at )
t 1
(A17)
can be found using the result
yt  At yt 1  Ct xt  bt  (At  Ct G t )y t 1  Ct gt  bt
(A18)
Equation (A18) is first calculated for t  1 using initial condition y 0  y0 , and then for
the subsequent time periods until t   .
Observe that the calculation of the interest rate depends on the initial conditions
and inflation and output targets arbitrarily chosen. However, it is not critical finding the
optimal interest rate each period. By contrast, the calculation of matrices G1 , G 2 , G 3 ,
and G 4 does not depend on the initial conditions and targets. Thus, interest rate
sensitivity to lagged inflation, output, and exchange rate independs of the initial
condition and targets. The targets influence only the reaction function intercept, that is,
the matrices g1 , g 2 , g3 , and g 4 .
The elements  5,t (in the model with stock prices) and  4,t (in the model without
stock prices) of matrix G t are always zero in equations (12) and (13). This means that
current interest rate is insensitive to previous interest rate. Thus the central bank is free
to choose the best interest rate in t taking into account its effects both in t and the
subsequent time periods.
References
Akram, Q.F., Bardsen, G., Eitrheim, O., 2006. Monetary policy and asset prices: to
respond or not? International Journal of Finance & Economics 11 (3), 279–292.
Alexandre, F., Bacao, P., 2002. Equity prices and monetary policy: an overview with an
exploratory model. Birkbeck College Working Paper.
Ball, L., 1999. Rules for monetary policy. International Finance 2 (1), 63–83.
Batini, N., Nelson, E., 1999. Optimal horizons for inflation targeting. Journal of
Economic Dynamics and Control 25 (6), 891–910.
Bernanke, B., Gertler, M., 2001. Should central banks respond to movements in asset
prices? American Economic Review 91 (2), 253–257.
Cecchetti, S.G., Genberg, H., Wadhawani, S., 2000. Asset prices and central bank
policy. Geneva Reports on the World Economy Series.
Cecchetti, S.G., Genberg, H., Wadhwani, S., 2003. Asset prices in a flexible inflation
targeting framework. In: Hunter, W.C., Kaufman, G.G., Pomerleano, M. (Eds.), Asset
Prices Bubbles: the Implications for Monetary, Regulatory, and International Policies.
MIT Press: Cambridge, MA, pp. 427–444.
Chadha, S.J., Sarno, L., Valente, G., 2004. Monetary policy rules, asset prices and
exchange rates. IMF Staff Papers 51 (3), 529–552.
Filardo, A.J. (2004). Monetary policy and asset price bubbles: calibrating the monetary
policy trade-offs. BIS Working Paper.
Fischer, S., 1996. Why are central banks pursuing long-run price stability? In:
Achieving Price Stability, Federal Reserve Bank of Kansas City Symposium Series, pp.
7–34.
Gilchrist, S., Leahy, J.V., 2002. Monetary policy and asset prices. Journal of Monetary
Economics 49 (1), 75–97.
Kontonikas, A., Ioannidis, C., 2005. Should monetary policy respond to asset price
misalignments? Economic Modelling 22 (6), 1105–1121.
Kontonikas, A., Montagnoli, A., 2006. Optimal monetary policy and asset price
misalignments. Scottish Journal of Political Economy 53 (5), 636–654.
Mishkin, F.S., White, E.N., 2002. U.S. stock market crashes and their aftermath:
implications for monetary policy. NBER Working Paper.
Rigobon, R., Sack, B., 2003. Measuring the reaction of monetary policy to the stock
market. Quarterly Journal of Economics 118 (2), 639–669.
Roubini, N., 2006. Why central banks should burst bubbles. International Finance 9 (1),
87–107.
Rudebusch, G.D., Svensson, L.E.O., 1999. Policy rules for inflation targeting. In:
Taylor, J.B. (Ed.), Monetary Policy Rules. University of Chicago Press, Chicago, pp.
203–259.
Schwartz, A.J., 2003. Asset price inflation and monetary policy. Atlantic Economic
Journal 31 (1), 1–14.
Svensson, L.E.O, 2000. Open-economy inflation targeting. Journal of International
Economics 50 (1), 155–183.
Table 1. Asset prices and monetary policy: selected recent work
Authors
Gilchrist and Leahy (2002)
Alexandre and Bacao
(2002)
Mishkin and White (2002)
Schwartz (2003)
Rigobon and Sack (2003)
Chadha et al. (2004)
Kontonikas and Ioannidis
(2005)
Akran et al. (2006)
Kontonikas and
Montagnoli (2006)
Results
Central banks should not respond to asset price movements.
Central banks should respond to stock price movements that are caused by financial shocks rather
than demand-supply shocks.
Central banks should monitor the effects of asset price movements on financial stability, but that
does not mean prompt response to them.
Central banks should respond to asset price movements that affect aggregate demand.
Central banks should pay attention to the deviations of asset prices and exchange rates from
equilibrium because such variables help to forecast expected inflation and output gap; however, asset
prices and exchange rates should not be considered as arguments in the monetary policy rule.
Central banks should respond mildly to asset price movements under the Taylor rule and inflation
targeting.
Central banks should include asset prices (but not exchange rates) in their monetary policy rule to
ensure macroeconomic stability.
Central banks should be aware that asset prices play an independent role in the presence of wealth
effect and inefficient markets; thus asset prices are not mere predictors of inflation and output.
Table 2. Reduced form equations
Lagged endogenous variable
Equation
Endogenous variable
Intercept
Control variable
yt 1
 t 1
et 1  et 2
pt 1  pt 2
it
(2)
yt
1   4 1   51
2
0
 4 2
 52
3   4 3  53
(3)
t
1  31  34 1  351
3 2
2
3 4 2
 3 52
33  3 4 3  3 53
(4)
et 1  et 2
1
0
0
2
0
3
(5)
pt 1  pt 2
1
0
0
0
2
3
(6)
yt
1   4 1
2
0
 4 2

 3   4 3
(3)
t
1  31  3 4 1
3 2
2
3 4 2

33  3 4 3
(4)
et 1  et 2
1
0
0
2

3
Table 3. Sample
Country
Argentina
Brazil
Chile
China
Colombia
Czech Republic
Indonesia
India
Israel
Korea
Malaysia
Mexico
Peru
The Philippines
Poland
South Africa
Russia
Sri Lanka
Thailand
Taiwan
Turkey
Venezuela
Time Period
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1993Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1993Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1994Q1–2006Q4
1990Q1–2006Q4
1994Q1–2006Q4
1990Q1–2006Q4
1994Q2–2006Q4
1996Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
1990Q1–2006Q4
Table 4. Parameters’ fixed-effect panel estimation robust to heteroskedasticity and
autocorrelation
Equations
Output gap in the model with
stock prices
Output gap in the model
without stock prices
Inflation rate
Exchange rate
Stock prices
Constant
Output
gap
Inflation
rate
16646.4*
0.614**

(1.65)
(21.69)
155.09
6.671**

(0.02)
4.381**
(7.27)
5.88E-18
(1.03)
0.042**
(6.88)
(27.90)
0.318**
(12.18)

0.391**
(7.63)



Coefficients
Interest
Exchange
rate
rate
1874.2**
24566.7
Stock
prices
R2
DW

0.40
1.75
(5.80)
(1.17)
1662.7**
0.534*
0.09**
0.48
1.79
(5.51)

(1.83)

(13.57)

0.38
2.09
2.6E-19*
(2.71)
0.0043*
(7.14)
4.82E-5*
(5.45)


0.98
1.44
0.070**
(2.19)
0.05
1.84
Notes:
* significant at 10 percent, ** significant at 5 percent
All variables are stationary at the 5 percent significance level
The values in brackets are t-statistics
Table 5. Results for the alternative models
A. Model with stock prices
Monetary policy
regime
Control variable
it
Coefficients of G t
Coefficients
of gt
yt 1
 t 1
et 1  et 2
pt 1  pt 2
i4
i3
i2
i1
0.4019710
0.1312534
0.2374247
0.2359348
1.535506
0.947033
1.470225
1.469769
0.7679570
0.9693621
0.4545266
0.4429218
0.6471631
0.5234262
0.8320050
0.8341642
36.43871
32.14383
48.27960
37.11349
i4
i3
i2
i1
0.8147229
0.6856297
0.2464668
0.2402701
1.691317
1.371836
1.490928
1.477755
0.4731379
0.5690580
0.4480977
0.4425085
0.2266134
0.1736210
0.8345638
0.8339504
8.875297
7.163906
48.334980
38.538679
No inflation target
Loss function
Wt
210.9287
Explicit inflation target
865.7390
B. Model without stock prices
Monetary policy
regime
Coefficients of G t
Control variable
it
Coefficients
of gt
yt 1
 t 1
et 1  et 2
pt 1  pt 2
i4
i3
i2
i1
0.1749510
0.1875287
0.1906866
0.1914745
0.9528441
0.9478989
0.9265714
0.9212498
0
0
0
0




6.101685
6.768457
7.154845
6.579256
i4
i3
i2
i1
0.4299527
0.4784011
0.4947805
0.5003039
0.2116417
0.2080425
0.2068256
0.2064153
0
0
0
0




3.184874
3.444218
3.626268
3.424819
No inflation target
Loss function
Wt
271.5090
Explicit inflation target
100.0713