American Journal of Industrial and Business Management, 2013, 3, 645-654
Published Online December 2013 (
Open Access AJIBM
Interactions between House Prices, Stock Prices and
Monetary Policy—Using Recursive VAR
Chun-Chang Lee1, Chih-Min Liang2, Wen-Hui Wu1, Shu-Man You3
1Department of Real Estate Management, National Pingtung Institute of Commerce, Pingtung, Taiwan; 2Department of Public Fi-
nance and Tax Administration, National Taipei Institute of College of Business, Taipei, Taiwan; 3Department of Real Estate and
Built Environment, Natioanl Taipei University, New Taipei City, Taiwan.
Received July 21st, 2013; revised August 21st, 2013; accepted August 29th, 2013
Copyright © 2013 Chun-Chang Lee et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-
dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual
property Chun-Chang Lee et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
This paper uses a recursive VAR model to analyze the effects of monetary policy shock and the role of house price and
stock price shocks. The data come from the first quarter of 1993 to the second quarter of 2010 in Taiwan. There is em-
pirical evidence suggesting that house prices and stock prices do not play the role of a transmission mechanism for
monetary policy shocks. When house prices increase, stock prices undergo a simultaneous and significant increase, but
this effect gradually disappears. As house prices increase, the interest rate is simultaneously and positively affected; this
effect is insignificant until after the second quarter. As stock prices increase, house prices are positively affected, which
is consistent with the sign of the expected value, but the effect is insignificant. Under increasing stock prices, the inter-
est rate simultaneously increases, and this effect is significant during the third and tenth quarters.
Keywords: Asset; House Prices; Stock Prices; Monetary Policy; Recursive VAR
1. Introduction
The subprime crisis and a series of financial crises in the
US highlight the importance of asset prices as a trans-
mission mechanism for monetary policy changes. This
is primarily due to the central collateral role played by
asset prices such as house prices. Asset prices not only
affect the capital of financial institutions but also re-
sult in changes in overall corporate investment and
private consumption patterns, making these a source of
macroeconomic fluctuations. Bjørnland and Jacobsen
[1] suggested that central banks have managed to keep
inflation in check through inflation targeting, yet they
have not managed to prevent asset prices from burst-
ing and having real negative effects. A report by the
International Monetary Fund [2] noted that monetary
policy-makers should pay more attention to the overall
financial risk caused by the bursting of real property
bubbles. Due to the role played by the wealth storage of
assets, the impact of asset prices should be carefully
assessed in times of disturbance. Asset prices can
simultaneously respond to monetary policy and thus
become an important shock transmission mechanism
[3-5]. Hence, because of their timely response to
economic shocks, asset prices may be important
indicators of the status of monetary policy. An analysis
of the role played by asset prices as a transmission
mechanism of monetary policy can help to understand
whether monetary policy can effectively achieve its
Unlike other assets, houses have the dual role of being
a wealth storage mechanism as well as an important
durable consumption good. Changes in house prices can
affect the wealth of households. According to the Tobin
Q theory, when the value of a pledge rises, the rising
house price will stimulate housing construction activities .
House price shock may affect real output and commodity
price, making it an important forward-looking variable.
Hence, monetary policymakers will monitor house prices.
House prices can affect housing market development,
capital flows and prices in other markets. Past research
on the role house prices play in the overall economy has
often neglected the effect of other asset prices, leading to
Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR
errors in the empirical results. Case et al. [6] noted that
the stock market and housing market have an important
effect on macroeconomic activity in a mature economy.
Hence, this study will also explore the role of stock price
When estimating the relationships between macro-
economic variables, VAR (vector autoregression) analy-
sis, including three types of reduced form VAR, re-
cursive VAR, and SVAR (structural VAR), is often
applied. The reduced form VAR is a VAR model with-
out a priori limits that can perform an empirical study
without relying on any economic theory. Although it is
very convenient for analyzing the short-term dynamic re-
lationships between variables, the over-parameterization
of results in the VAR model reduce the estimation ef-
ficiency of the model, and the long-term relationships
between economic variables cannot be explained. Bernanke
and Blinder [7] noted that it is highly risky to use the
VAR model without limits in structural inference. Each
variable of the reduced form VAR model is a linear
function composed of the past value of the variable, the
past values of other variables, and errors, without con-
sidering the current impact in between variables. The
regression errors of the reduced form VAR are correlated
with the contemporaneous term, making it difficult to
identify the structural impact. Comparatively, there is no
correlation between the recursive VAR structural errors.
As IRF (impulse reaction function) requires no cor-
relation between shocks, the recursive VAR model is
also frequently used to illustrate the dynamic impact of
specific variables on the endogenous variables.
When using a recursive VAR model to measure the
structural effects caused by policy changes, the ordering
of monetary policy variables and other economic va-
riables should be conducted. The contemporaneous
impact between variables is of recursive form [8-9]. The
order of model endogenous variables is subject to the
contemporaneous relationship between monetary policy
and other economic variables. Compared with SVAR, the
identification of recursive VAR is simpler and more
This paper uses recursive VAR as the model setting
approach. Because monetary policy may affect asset
prices (stock and house prices) via the interest rate chan-
nel, this model illustrates the interactive relationship be-
tween asset price and monetary policy. When setting the
empirical model, this study also considers the potential
impact of asset prices on monetary policy to understand
the short-term interaction between monetary policy and
asset prices. The proposed model is a traditional closed
economy system model, which consists of macroeco-
nomic variables for monetary policy and asset prices to
explain the effect of changes in monetary policy and the
role of house and stock price shocks.
2. Empirical Model Setting
First, t is assumed to represent the stationary variable
vector in the macroeconomic respect :
ln, ln, ln, ln,
; the
endogenous variables in brackets represent output, con-
sumer price index, house prices, stock prices and interest
rate, respectively. If the VAR presentation equation of
y is
 
, and
L is an invertible matrix, then it can be written into
the equation of the moving average as follows:
where t
is a
vector of reduced form residuals
assumed to be identically and independently distributed,
, with a positive definite covariance matrix
BL is the
convergent matrix polyno-
mial in the lag operator L,
. Following
the literature, the innovations
are assumed to be
written as linear combinations of the underlying or-
thogonal structure disturbances
e, i.e., tt
. The
VAR can then be written in terms of structural shocks in
the following manner:
CL BLS. If S is identified, we can derive
the MA representation in (2) as is calculated
from a reduced form estimation. To identify S, the et’s
are normalized so that they all have unit variance. The
normalization of
ecov t implies that SS
. Be-
cause the model contains five variables, the simultaneous
matrix S can identify as many as 15 parameters. There-
fore, the matrix S should be added with some restrictions
to identify structural disturbances.
With a five-variable VAR, we can identify five struc-
tural shocks, namely, the output shocks , the
consumer price index shocks , house price
ln GDP
ln CPI
e, stock price shocks
monetary policy shocks
e. We then order the vector
of structural shocks as follows:
lnlnln ln
,,, ,
tttt tt
eee eee
This paper attempts to establish a recursive VAR
model and set constraints for the simultaneous variables,
ten of which are set to zero. No variable is assumed to be
affected by the variable that follows it in the contempo-
raneous term. Hence, in the recursive VAR model, dif-
ferences in the ordering of variables will affect the em-
Open Access AJIBM
Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR 647
pirical results as well as the degree of response to shocks.
The variables in the model are ordered as follows: gross
domestic product and consumer price index are the proxy
macroeconomic variables; asset price is represented by
the Sinyi house price index and the weighted average of
the stock prices; finally, the weighted average lending
interest rate is the proxy variable of monetary policy. The
reason for such ordering is illustrated below.
This paper bases the limits set on simultaneous vari-
ables on past literature. Using the rules on implementing
monetary policies set by the Central Bank, Rotemberg
and Woodford [10] established a model with the interest
rate as a function of the output and inflation. Bjørnland
and Jacobsen [1] argued that macroeconomic variables
do not simultaneously react to policy variables, while the
simultaneous reaction from the macroeconomic envi-
ronment to policy variables is allowed for. Hence, the
output in the first column and inflation in the second
column are ranked higher than interest rate (as shown in
Equation (3)). The output represents the aggregate de-
mand relationship and inflation stands in for the aggre-
gate supply relationship. The two columns represent the
equation of commodity market equilibrium [9-10].
The third column is the equation of the house prices.
As noted by Bjørnland and Jacobsen [1], house prices
contribute greatly to the macroeconomic variables and
the interest rate, indicating the importance of including
house prices in the model. In other words, the unexpected
rise of house prices will promote short-term consumption
and output growth, which results in inflation, thereby
affecting monetary policy.
The fourth column is the equation of stock prices. As
noted by Case et al. [6], stock market, macroeconomic
factors and the housing market are often correlated in a
mature economic system. Regarding stock and housing
markets, assets of different types have apparent price
spillovers [2], suggesting that booming transactions will
result in rising house prices, which trigger stock price
fluctuations in times of a booming housing market.
In the monetary policy equation represented in the
fifth column, the real GDP, CPI, house prices and stock
prices are ranked ahead of monetary policy, indicating
that the macroeconomic variables have a contemporane-
ous effect on the interest rate given the constraints on
simultaneous variables and assuming that “monetary
policy has no simultaneous impact on economic vari-
ables.” Svensson [11] stressed that monetary shocks have
no effect on output or inflation in the monetary policy
transmission mechanism. However, changes in mac-
roeconomy occur simultaneously with monetary policy
shifts. Therefore, Bjørnland and Jacobsen [1] established
a VAR model in 2010 that ranks output, inflation, and
house prices ahead of interest rate. Therefore, we define
the model as follows:
21 22
31 3233
41 424344
51 52535455
SS e
3. Data Source and Description
First, we describe the empirical data source and the data
processing of the variables at their original value levels.
We then describe the decomposed sequence diagram by
Hodrick-Prescott as well as the unit root test results.
3.1. Data Source
This paper sources 70 batches of quarterly empirical data
from the period of the first quarter in 1993 to the second
quarter in 2010. The five variables of the empirical
model include: the natural logarithm of the real GDP
after quarterly adjustment, the natural logarithm of CPI
after quarterly adjustment, the natural logarithm of the
Sinyi house price index, the natural logarithm of the
weighted average stock prices and the weighted average
lending interest rate. The real GDP and CPI data are
taken from the Taiwan Economic Journal (TEJ) Database;
the weighted average stock price index data are taken
from the Taiwan Stock Exchange; and the weighted
average lending interest data are based on the published
data of the Central Bank.
The data on house prices in this paper is represented
by the Sinyi house price index. The index is compiled
using hedonic pricing theory to remove problems of
heterogeneity (excluding biased samples such as aged
houses). The Sinyi house price index covers mainly the
market of old and middle-aged houses (including apart-
ment buildings, buildings with elevators and excluding
pre-sale houses). The index is a relatively credible house
price index based on publicly available information. The
variables and the data source used in this study are
shown in Table 1.
3.2. Data Processing
Before conducting the empirical study, we use the unit
root test approach to test whether the variables included
in the analysis are stationary to avoid the problem of
“spurious regression.” This study uses the ADF and
KPSS approaches to test whether the time series is
stationary. If the time series is not stationary, the
stationary test will be conducted after Hodrick-Prescott
decomposition. The Hodrick-Prescott decomposition can
decompose the non-stationary time series data into
stationary and non-stationary parts. The empirical study
of this paper uses the stationary part after the Hodrick-
Open Access AJIBM
Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR
Prescott decomposition as the data for the empirical
According to the original level sequence for the vari-
ables shown in Figure 1 and the unit root test results of
the original levels shown in Table 2, the original se-
quences of lnGDP, lnCPI, lnHP all have unit roots indi-
cating a non-stationary time series. Hence, we conduct
the unit root test after the Hodrick-Prescott decomposi-
According to the results from the unit root test con-
ducted after Hodrick-Prescott decomposition and shown
in Tab le 3, both the ADF test and the KPSS test confirm
that real GDP, CPI and Sinyi house price index data be-
come stationary time series data after the Hodrick-Pres-
cott decomposition.
4. Data Source and Description
In time series data, the choice of a lagging period is
closely related to stability. More lagging periods in the
model will result in more parameters to be estimated and
fewer degrees of freedom. Hence, there should be a bal-
ance between lagging periods and degrees of freedom.
The lag order of the model is determined using LR (like-
lihood ratio), AIC (Akaike Information Criterion), SC
(Schwarz Criterion), Hannan-Quinn Information Criteria,
and FPE (Final Prediction Error Criterion) for model
reductions. The tests suggested that one lag is acceptable.
We therefore set the VAR model lag as 1 for estimation.
Table 1. Variable and data description.
Variable Description Data Source
lnGDP Natural logarithm of real GDP TEJ Database
lnCPI Natural logarithm of CPI TEJ Database
lnHP Natural logarithm of Sinyi
house price index
Sinyi Real Estate
lnSTOCK Natural logarithm of weighted
average stock price index
Taiwan Stock
Exchange corporation
RATE Weighted average
lending interest rate Central Bank of Taiwan
Table 2. Unit root test of the original levels of the variables.
Variables ADF KPSS
lnGDP 1.12 1.10***
lnCPI 2.80* 1.01***
lnHP 0.56 0.38*
lnSTOCK 3.35** 0.17
***”, “**”, “*”represent significance at the 1%, 5% and 10% levels, re-
spectively; the ADF testing rejects the null assumption of unit root; and
KPSS testing rejects the null assumption of no unit root.
Table 3. Unit root test results after Hodrick-Prescott de-
Variables ADF KPSS
ln GDP 4.53*** 0.04
ln CPI 2.97** 0.08
P 4.44*** 0.08
***”, “**”, “*” represent significance at the 1%, 5% and 10% levels, re-
spectively; the ADF testing rejects the null assumption of no unit root; and
KPSS testing rejects the null assumption of unit root.
4.1. Effects of Monetary Policy Shocks
By the analysis of IRF as set by the theoretical model, we
can explore whether a change of one standard deviation
of a certain variable has a positive, negative, continuous
or sporadic effect on other variables. The IRF of a stable
model should be close to 0. Figures 2-4 illustrate the
IRFs of different structural shocks on the variables.
As past studies have indicated, contractionary mone-
tary policy shocks often affect output and inflation. An
unexpected rise in interest rates, though temporary, re-
sults in lowered output and inflation [1]. Figure 2(a)
illustrates that, given an increase of one standard devia-
tion (approximately 0.13%) in the interest rate, the GDP
responds positively, at first, then negatively, after the
sixth quarter. The effect, however, is not significant. The
results suggest that the expected contractionary monetary
policy shocks will have a negative effect on employment
and salary. According to a study by Elbourne and de
Haan [10], among EU countries, interest rate shocks have
a negative impact on industrial production in all coun-
tries except Italy, and the effect has not been significant
in most cases. These findings are consistent with the em-
pirical outcomes of this study. Figure 2(b) illustrates that
after one standard deviation increase in interest rate, CPI
unexpectedly responds positively, but the effect is not
significant. Goux and Cordahi [12] used the recursive
VAR to discuss the transmission mechanism of inter-
national monetary policy and found that CPI and short-
term interest rate are positively, though not significantly,
correlated. The contractionary monetary policy does not
result in a lowering of the CPI but in an increase in
commodity prices, and this phenomenon is generally
known as the price puzzle. This puzzle may be explained
by a cost channel of interest rate, where an increase in
firms borrowing costs is offset by an increase in prices
As shown in Figure 2(c), after an increase of one
standard deviation in the interest rate, house prices re-
spond positively in the first four quarters, but the effect is
not significant. One possible reason for this might be the
fact that construction companies transfer part of the cost
o consumers to trigger an increase in house prices in the t
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Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR
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94 96 98 00 02 04 06 08 10
Figure 1. Sequence of variable original levels.
case of rising lending costs. The response turns negative
after the fifth quarter, however, although not significantly.
Past research on housing market channels and the mone-
tary transmission mechanism has generated controversial
results. The findings of the Hong Kong Monetary Au-
thority (HKMA) [14], based on research in Hong Kong,
suggest that interest rates affect asset prices and inflation
rates through the housing market channel. Iacoviello and
Minetti [15] analyzed five variables, including the total
loan, house prices, GDP, inflation and interest rate, in the
case of Finland, Germany, Norway, and the UK. The
empirical results suggested that the monetary policies of
Finland and Germany have a significant and simultane-
ous negative effect on house prices; the monetary policy
of the UK has a significant and simultaneous positive
effect on house prices; Norway’s monetary policy has a
positive effect on house prices, but this effect is not sig-
nificant. The research findings suggest that the time,
strength and response of monetary shocks vary in differ-
ent countries, indicating that the implementation of
monetary policies may have different effects on house
As shown in Figure 2(d), an increase of one standard
deviation in the interest rate has a predominantly nega-
tive effect on stock prices, which is consistent with the
expected sign; however, this effect is not significant.
When interest rates rise, investors may consider selling
stocks and depositing capital in the bank, resulting in the
negative correlation between changing interest rates and
stock prices. As shown in Figure 2(e), due to the positive
shock of the monetary policy in place, the interest rate
significantly and continuously rises.
Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR
Figure 2. Response to a monetary policy shock.
The empirical results from the monetary policy shocks
analysis do not support a transmission role for house and
stock prices. A possible reason for this might be low in-
terest rates. During the period from the first quarter of
1993 to the second quarter of 2010, the average interest
rate was 3.5%, and it decreased to less than 3% after the
fourth quarter of 2001. Such low interest rates have no
significant impact on investment and consumption lend-
ing costs, making monetary policy ineffective. Secondly,
the sources of capital in investment and consumption
may be channels other than the banking system. Lai [16]
also uses the recursive VAR model to discuss possible
transmission channels for Taiwan’s monetary policy. The
empirical results suggest that, except for its effect on
housing investment, contractionary monetary policies
have no effect on consumption costs for durable goods,
such as machinery and equipment for enterprises, imply-
ing that enterprises may have other capital source chan-
nels. Hence, a possible reason for the inability of house
prices and stock prices to transmit monetary policy
shocks is low interest rates and alternative capital source
hannels. c
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Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR 651
Figure 3. Response to a house price shock.
As the variance decomposition shown in Table 4 sug-
gests, the monetary policy shock has very low explana-
tory power on changes in house and stock prices as the
interest rates in Taiwan have been relatively low for a
long time. Extremely low lending costs make interest
rates an insignificant factor in housing and stock invest-
ment. Thus the role of stock and housing markets as a
transmission mechanism for monetary policy shocks can-
not be determined.
4.2. The Role of House Price Shocks
Drawing from the analysis of the effects of monetary
policy shock, the discussion of the effect of house price
shock on the relevant variables is organized as follows.
As shown in Figures 3(a) and (b), an increase of one
standard deviation (an approximately 0.029 unit) in
house price, generates a positive but not significant
change in GDP and CPI [17,18].
As shown in Figure 3(c), an increase of one standard
deviation in house price generates a simultaneous, sig-
nificant, and positive increase in stock prices, reaching a
peak value of an approximately 0.05 unit1 in the third
quarter. After the fifth quarter, this effect gradually dis-
appears. As expected, changes in house prices positively
Table 4. Variance decomposition. Contributions from
monetary policy shocks to ln GDP, ln CPI, ln HP and ln
1 0.000 0.000 0.000 0.000
2 0.001 0.002 0.000 5.92E-07
3 0.001 0.006 0.000 6.73E-05
4 0.001 0.014 0.000 0.000
5 0.001 0.022 0.000 0.001
12 0.011 0.059 0.006 0.027
24 0.029 0.098 0.014 0.076
affect the contemporaneous stock prices, confirming that
different types of assets exhibit price spillovers [2].
As shown in Figure 3(d), an increase of one standard
deviation in house price results in a positive but not sig-
nificant change in the interest rate. This positive effect
appears after the second quarter and is significant until
the eleventh quarter. The relevant literature on this phe-
nomenon has noted that the effect of house prices on
interest rate is positive (see [5,9]). Our research findings
are consistent with Bjørnland and Jacobsen [1], confirm-
ng that house price shocks can respond simultaneously i
1Elasticity is 0.05/0.029 = 1.724.
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Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR
Figure 4. Response to a stock price shock.
to monetary policy changes in Sweden and Britain. In
Norway, the initial effect on house prices is insignificant,
but after two quarters, house prices increase 10 basis
points. The strength and timing of the response thereafter
varies from one country to another, indicating that hous-
ing may play a different role in monetary policy setting.
Bjørnland and Jacobsen [1] noted that the house price
shock can result in changes in the interest rate within one
year. The results in this paper confirm these results.
The variance decomposition as illustrated in Table 5
suggests that, the house price shock explains approxi-
mately 11% - 18% of the variance in stock price and ap-
proximately 6% - 21% of the variance in interest rate
after the second period. The explained variance in stock
price and interest rate gradually increases. House price
shock explains approximately 5% of the variance in GDP
and CPI after the 12th period. These results demonstrate
the importance of house price shock to stock prices and
the interest rate.
4.3. The Role of Stock Price Shocks
As shown in Figure 4(a), an increase of one standard
deviation (an approximately 0.1 unit) in stock price, re-
sults in a simultaneous and significant increase in GDP,
Table 5. Variance decomposition. Contri butions from house
prices shocks to ln GDP, ln CPI, ln STOCK, and RATE.
1 0.000 0.000 5.379 3.235
2 0.795 0.111 11.693 6.326
3 2.148 0.516 15.469 9.280
4 3.411 1.307 17.418 11.795
5 4.245 2.378 18.286 13.825
12 4.721 5.843 18.233 19.705
24 4.799 5.844 18.214 21.357
reaching a peak value of 0.004 unit2 after the fourth
quarter of stock price shock and gradually disappearing.
As shown in Figure 4(b), after an increase of one stan-
dard deviation in stock price, CPI increases significantly
in the fifth quarter, and the period of significant increase
lasts for four quarters, from the fifth to the ninth quarter.
As shown in Figure 4(c), an increase of one standard
deviation in stock price results in an increase in house
price, but this effect is not significant. As shown in Fig-
2Elasticity = 0.004/0.1 = 0.04.
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Interactions between House Prices, Stock Prices and Monetary Policy—Using Recursive VAR 653
ure 4(d), an increase of one standard deviation in stock
price results in a simultaneous, significant increase in the
interest rate from the third to the tenth quarter, reaching a
peak value of approximately 0.15 units in the ninth quar-
ter after the shock. From the perspective of point estima-
tion, the effect seems to be permanent, but it becomes
insignificant after the tenth quarter.
The variance decomposition illustrated in Table 6
suggests that the variance in GDP, CPI, house price and
interest rate attributable to the stock price shock is low
at first but gradually increases with time. The ranking
by explanatory variance is interest rate, GDP, CPI and
house price, indicating that stock price shocks have a
significant long-term impact on interest rate, GDP and
5. Conclusions and Suggestions
This study uses a recursive VAR model to analyze the
effect of monetary policy shocks and the role of house
price and stock price shocks. The empirical results
suggest that contractionary monetary policy shock has no
significant impact on GDP, CPI, house prices and stock
prices. The role of house prices and stock prices as a
transmission mechanism for monetary policy shock
cannot be confirmed.
The findings on the effect of house price changes
suggest that stock prices will be simultaneously and
positively affected when house prices increase but that
this effect gradually disappears after the fifth quarter.
Increases in house prices result in simultaneous increases
in the interest rate, and this effect becomes significant
after the second quarter and lasts until the eleventh
quarter. These results are consistent with Bjørnland and
Jacobsen [1]. The results from the stock price shock
analysis suggest that GDP significantly increases with
increases in stock price, but this effect lasts only until
the fifth quarter. Increases in stock prices positively and
significantly affect CPI. An effect lasts for four quarters
from the fifth to the ninth quarter. House prices are
Table 6. Variance decomposition. Contributions from stock
price shocks to lnGDP, lnCPI, lnHP, and RATE.
Term lnGDP lnCPI lnHP RATE
1 0.000 0.000 0.000 0.078
2 2.651 0.056 1.074 1.364
3 6.491 0.656 2.590 4.694
4 9.676 2.193 3.742 8.673
5 11.564 4.451 4.327 12.358
12 12.314 11.454 4.678 22.449
24 12.438 11.436 4.738 23.961
positively affected by increases in stock prices, but this
effect is not significant. As stock prices increase, the
interest rate simultaneously increases, and this effect is
significant from the third to the tenth quarter, becoming
insignificant after the tenth quarter.
This analysis in this paper assumed a traditional closed
economic system. However, because Taiwan is a small
open economy, the interaction between exchange rate
and monetary policy might be significant, as previous
studies suggest [19-21].
Exchange rates and international crude oil prices may
be added as the foreign economic variables. Given that
mainland China, the US and Taiwan are closely related
in terms of economy, future studies may wish to include
data on China and the US in the analysis. Additionally,
this paper uses the weighted average lending rate as a
proxy variable for monetary policy. However, the
weighted average lending rate published by the Central
Bank is not entirely subject to the control of monetary
policy designed to regulate supply and demand in capital
markets. Hence, future studies may wish to use monetary
supply or non-borrowed money supply as a proxy vari-
able for monetary policy.
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