Agencies | AGENCIES | October 08, 2003 | 20:46
American Robert F Engle and Briton Clive W J
Granger on Wednesday won the 2003 Nobel Economics Prize for their work in
analysing economic time series.
The Royal Swedish Academy of Sciences decided that the Bank of Sweden
Prize in Economic Sciences in Memory of Alfred Nobel, 2003, would be
shared between the two 'for methods of analyzing economic time series with
time-varying volatility' and 'for methods of analysing economic time
series with common trends (cointegration).'Robert Engle was born in
1942 in Syracuse, NY, USA (American citizen) and got his Ph.D. from
Cornell University in 1969.
He is Michael Armellino Professor of Management of Financial Services
at New York University, NY, USA.
Clive W J Granger was born in 1934 in Swansea,
Wales (British citizen) and obtained his Ph.D. from University of
Nottingham in 1959.
He is emeritus Professor of Economics at University of California at
San Diego, USA.
Their research gave economists new tools to evaluate risk by improving
analysis of indicators such as economic growth, prices or interest rates
over a period of time.
The prize money of 10 million kronor ($1.3 million) will be divided
equally between them.
Statistical methods for economic time series
Researchers use data in the form of time series, i.e., chronological
sequences of observations, when estimating relationships and testing
hypotheses from economic theory. Such time series show the development of
GDP, prices, interest rates, stock prices, etc.
During the 1980s, this year's Laureates devised new statistical methods
for dealing with two key properties of many economic time series:
time-varying volatility and nonstationarity.
On financial markets, random fluctuations over time - volatility - are
particularly significant because the value of shares, options and other
financial instruments depends on their risk.
Fluctuations can vary considerably over time; turbulent periods with
large fluctuations are followed by calmer periods with small
Despite such time-varying volatility, in want of a better alternative,
researchers used to work with statistical methods that presuppose constant
Robert Engle's discovery was therefore a major breakthrough. He found
that the concept of autoregressive conditional heteroskedasticity (ARCH)
accurately captures the properties of many time series and developed
methods for statistical modeling of time-varying volatility.
His ARCH models have become indispensable tools not only for
researchers, but also for analysts on financial markets, who use them in
asset pricing and in evaluating portfolio risk.
Most macroeconomic time series follow a stochastic trend, so that a
temporary disturbance in, say, GDP has a long-lasting effect.
These time series are called nonstationary; they differ from stationary
series which do not grow over time, but fluctuate around a given
Clive Granger demonstrated that the statistical methods used for
stationary time series could yield wholly misleading results when applied
to the analysis of nonstationary data.
His significant discovery was that specific combinations of
nonstationary time series may exhibit stationarity, thereby allowing for
correct statistical inference.
Granger called this phenomenon cointegration. He developed methods that
have become invaluable in systems where short-run dynamics are affected by
large random disturbances and long-run dynamics are restricted by economic
Examples include the relations between wealth and consumption, exchange
rates and price levels, and short and long-term interest