Monetary Policy in Singapore and the Global Financial Crisis

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b1110

Challenges for the Singapore Economy

Nonetheless, whether monetary policy should be used as a tool

for limiting upswings in asset prices in Singapore still depends

empirically on the effectiveness of such policy in offsetting asset

price movements and, if the answer is yes, what the costs would be if

the bubble is deflated at the expense of slower economic growth

and higher unemployment. Moreover, since it is the mission of

MAS to promote sustained non-inflationary economic growth, it is

also important to understand the effects of asset price inflation on

consumer price inflation over the medium to long term.

In order to answer these questions empirically Chow and Choy

(2009) examined Singapore’s monetary system with particular refer-

ence to local stock price and house price cycles.

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GDP and the

consumer price index are used to represent domestic economic activ-

ity and price movements, while the Singapore residential property

price index and the Stock Exchange of Singapore (SES) price index

are used to proxy asset prices in the economy. For the reasons given

earlier, the TWS$ exchange rate is also included because Singapore is

a very small and open economy but, more importantly, because

changes in the TWS$ are a key indicator of the monetary policy

stance in Singapore. Interest rates and monetary aggregates, on the

other hand, are not included since the MAS does not explicitly target

these variables when it carries out monetary policy.

The time paths of the variables in the model following a one time

shock to monetary policy are then traced out and provide an indicator

of the extent to which monetary policy might influence asset prices. At

the same time, the responses of inflation and output growth serve as

an indication of both the benefits and costs of monetary policy.

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C. H. Kwan and P. Wilson

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They utilize a factor augmented VAR (FAVAR) model. The VAR model is a

dynamic system of equations that allows for interactions between economic variables

while imposing minimal assumptions about the underlying structure of the economy

while FAVAR models permit the incorporation of information from large datasets in

a parsimonious manner in order to adequately capture the information monitored by

the central bank for a better identification of monetary policy innovations. The mon-

etary VAR is augmented in this case with common factors extracted from a large

panel dataset spanning 127 local and foreign economic time series from the first

quarter of 1980 to the second quarter of 2008.

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