Inflation Uncertainty, Output Growth Uncertainty and Macroeconomic Performance
| Published date | 01 June 2006 |
| DOI | http://doi.org/10.1111/j.1468-0084.2006.00164.x |
| Author | Menelaos Karanasos,Jinki Kim,Stilianos Fountas |
| Date | 01 June 2006 |
Inflation Uncertainty, Output Growth
Uncertainty and Macroeconomic Performance
Stilianos Fountas,*Menelaos Karanasosand Jinki Kimà
*Department of Economics, University of Macedonia, Thessaloniki, Greece
Business School, Brunel University, Uxbridge, UK
(e-mail: menelaos.karanasos@brunel.ac.uk)
àGangwon Development Research Institute, Chuncheon-si, South Korea
Abstract
We use a bivariate generalized autoregressive conditionally heteroskedastic
(GARCH) model of inflation and output growth to examine the causality
relationship among nominal uncertainty, real uncertainty and macroeconomic
performance measured by the inflation and output growth rates. The
application of the constant conditional correlation GARCH(1,1) model leads
to a number of interesting conclusions. First, inflation does cause negative
welfare effects, both directly and indirectly, i.e. via the inflation uncertainty
channel. Secondly, in some countries, more inflation uncertainty provides
an incentive to Central Banks to surprise the public by raising inflation
unexpectedly. Thirdly, in contrast to the assumptions of some macroeconomic
models, business cycle variability and the rate of economic growth are related.
More variability in the business cycle leads to more output growth.
I. Introduction
Since the early 1980s, there has been a tremendous improvement in
macroeconomic performance in industrialized and developing countries.
Krause (2003) reports that in a cross section of 63 countries, mean inflation
has fallen from approximately 83% in the pre-1995 period to approximately
9% in the latter half of the 1990s. Furthermore, both inflation and output
growth have become more stable. Cecchetti and Krause (2001) report that,
JEL Classification numbers: C22, C51, C52, E0.
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 68, 3 (2006) 0305-9049
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in a sample of 23 industrial and developing countries, the average country
experienced a decline in both inflation and output variability in the 1990s
compared with the 1980s. A second fact reported in the Cecchetti and Krause
(2001) study is that there seems to exist a trade-off between inflation and
output variability. A number of interesting issues arise from the above
findings. First, is the reduction in average inflation related to the reduction
in inflation uncertainty, and if so, is the causality between the two variables
bidirectional? Secondly, is it true that a reduction in inflation and, therefore,
inflation uncertainty can have a favourable impact on the rate of economic
growth, as predicted for example by Friedman (1977)? Thirdly, can a more
stable and less volatile output growth rate lead to more output growth?
This study analyses the empirical relationship among four important
macroeconomic variables: average inflation, output growth, nominal (in-
flation) uncertainty and real (output growth) uncertainty. Our objective is to
examine all possible effects among these four variables using time-series data
for the G7. In this respect, we attempt to provide answers to the above three
questions and, therefore, test for the empirical relevance of several theories
that have been advanced on the relationship between inflation, output growth,
real and nominal uncertainty. These theories include: first, the Cukierman and
Meltzer (1986) hypothesis that Central Banks tend to create inflation surprises
in the presence of more inflation uncertainty; secondly, the Black (1987)
hypothesis that increasing output uncertainty leads to more output growth;
thirdly, the Taylor (1979) effect that predicts a trade-off between inflation and
output variability and hence uncertainty.
Following the pathbreaking work on the autoregressive conditional
heteroskedasticity (ARCH) approach, the prevalent approach to measuring
uncertainty in the macroeconomics literature has been the conditional variance
of a macroeconomic series. To test for the relationship between macro-
economic uncertainty and indicators of macroeconomic performance, such
as inflation and output growth, one can use a simultaneous or a two-step
approach. Under the simultaneous approach, an ARCH-in-mean (ARCH-M)
model is estimated with the conditional variance equation incorporating lags
of the series, thus allowing simultaneous estimation and testing of the
bidirectional causality between the series and the associated uncertainty.
Under the two-step approach, estimates of the conditional variance are
obtained from the estimation of an ARCH model and then these estimates are
used in running Granger-causality tests to examine the causality between
macroeconomic performance and uncertainty.
In this paper, we examine the theoretical issues raised above with the use
of a bivariate generalized ARCH (GARCH) model that allows for
the measurement of uncertainty about inflation and output growth by the
respective conditional variances. GARCH methods to examine these issues
320 Bulletin
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