In 2003

**Clive Granger**shared the Nobel prize for economics with the American academic**Robert Engle**for their work on the concept of co-integration – ways of analysing sequences of economic data recorded at regular intervals, known as time series.Their use of sophisticated statistical techniques now help the understanding of market movements and economic trends.
The economics prize, introduced in 1968 by the Swedish central bank,
is the only Nobel award not established by the Swedish industrialist
Alfred Nobel.

Granger's principal achievements – so-called Granger
causality and co-integration – were acclaimed as having helped
build the statistical plumbing of modern economics and finance. "It
may not be glamorous," declared the magazine

**Clive Granger**

*BusinessWeek*, "but as the Nobel committee recognised, it's indispensable."
Trained in statistics, Granger specialised in research that helped
to demystify the often baffling behaviour of financial markets,
pioneering a
range of different ways of analysing statistical data which have
since become used routinely by government departments, world banks,
economists
and academics.

Clive always had a particular passion for identifying what was predictable in economic relationships. He emphasized the importance of sorting out which variables are helpful for purposes of forecasting others as one of the first steps in understanding underlying causal relationships. That philosophy came to be a regular feature in thousands of economic studies and seminars, as scholars would routinely report investigations of "Granger-causality."Clive also discovered, in a paper with Paul Newbold published in 1974, the phenomenon of spurious regression. The pair demonstrated by Monte Carlo simulation that if a researcher fails to take account of the underlying dynamics, a regression of one trending variable on another can produce what look like marvelously significant t-statistics, even though the reality could be that the two series are completely independent of each other.The complex methods Granger devised are used to analyse links between such factors as wealth and consumer spending, price levels and exchange rates. They allow the construction of economic models that help the understanding of how trends develop over time, and how relationships evolve between different variables.

Sir Clive Granger |

Clive always had a particular passion for identifying what was predictable in economic relationships. He emphasized the importance of sorting out which variables are helpful for purposes of forecasting others as one of the first steps in understanding underlying causal relationships. That philosophy came to be a regular feature in thousands of economic studies and seminars, as scholars would routinely report investigations of "Granger-causality."Clive also discovered, in a paper with Paul Newbold published in 1974, the phenomenon of spurious regression. The pair demonstrated by Monte Carlo simulation that if a researcher fails to take account of the underlying dynamics, a regression of one trending variable on another can produce what look like marvelously significant t-statistics, even though the reality could be that the two series are completely independent of each other.The complex methods Granger devised are used to analyse links between such factors as wealth and consumer spending, price levels and exchange rates. They allow the construction of economic models that help the understanding of how trends develop over time, and how relationships evolve between different variables.

He first came to notice in the 1960s with his work on time series
and the development of the concept of Granger causality, an idea rooted
in
the work of the mathematician Norbert Wiener. Granger was primarily
concerned with time series that were non-stationary (ie statistics such
as
a country's gross domestic product that, despite periodic
fluctuations, follow a long-term trend of growth or shrinkage; by
contrast,
unemployment figures or interest rates tend to remain at or around a
particular level and accordingly are described as stationary).The current value of a time series can often be predicted from its
own past values. For example, gross domestic product this quarter is
imperfectly predicted by GDP data from the past few years. But the
introduction of a second time series can improve predictive accuracy, a
concept that became known as "Granger Causality".In the 1970s Granger moved on to redefine the field of econometrics
(using mathematical or statistical techniques to study economic
problems)
by overturning much of the received wisdom in the study of time
series data. As

*BusinessWeek*later observed, Granger showed that the statistical techniques used by forecasters to come up with patterns in historical data "were simply wrong".Best-developed statistics formerly assumed that time series were stationary, tending to vary randomly around a common long-term mean (or average) value or around a non-random trend. Many economic time series, however, seem to be non-stationary, following processes related to the so-called "random walk", a term suggested by the idea of a drunken man stumbling along a street, who is just as likely to go one way as another.For want of better techniques, economists often applied statistics designed for stationary data to non-stationary data. But in 1974, Granger and his post-doctoral student Paul Newbold, building on the earlier work of the British statistician G Udny Yule, showed that pairs of non-stationary time series could frequently display highly significant correlations when there was no causal connection between them. For example, the US federal debt and the number of deaths due to Aids between 1981 and 2000 are highly correlated but are clearly not causally connected. Such "nonsense correlations" called into question the meaningfulness of many econometric studies.

Robert Engle , Mark Machina and Clive Granger |

Working with Engle, Granger realised that not all long-term associations between non-stationary time series are nonsense. Suppose, as the American academic Kevin D Hoover explained, that the randomly-walking drunk has a faithful (and sober) friend who follows him down the street from a safe distance to make sure he does not injure himself.

"Because he is following the drunk, the friend, viewed in isolation, also appears to follow a random walk, yet his path is not aimless; it is largely predictable, conditional on knowing where the drunk is," Hoover noted. Granger and Engle coined the term "co-integration" to describe the genuine relationship between two non-stationary time series. Time series are "co-integrated" when the difference between them is itself stationary – the friend never gets too far away from the drunk, but, on average, stays a constant distance behind.

Granger's discovery had an enormous impact, leading, as one of his students put it, to "chaos for a few years". In order to forecast non-stationary variables, new techniques had to be developed to replace the ones Granger had debunked.

Clive William John Granger was born on September 4 1934 in Swansea, where his father worked for the Chivers jam and jelly firm.When he was still a baby his parents moved to Lincoln and during the Second World War his father enlisted in the RAF. His mother took him first to Cambridge, and thence to Nottingham where, at West Bridgford grammar school, he showed promise as a mathematician and foresaw a career in insurance or meteorology. At the University of Nottingham he was among the original intake for the first joint degree course in Economics and Mathematics.

When he graduated with a First in 1955, Granger stayed on to become a lecturer in Statistics, gaining his PhD in 1959. After spending an academic year at Princeton, New Jersey, he married and spent his honeymoon travelling across the United States. Returning to Nottingham he remained on the faculty until 1973, with occasional visiting positions at other universities. In 1974 he returned to America to become a professor at the University of California at San Diego. It was there, with Engle, that Granger introduced methods for testing for co-integration among variables in a paper in the journal

*Econometrica*in 1987. The concept would have remained useful only in theory had not Granger and Engle introduced powerful statistical methods for estimating and testing hypotheses, groundbreaking work that earned them the Nobel Prize.

With Paul Newbold, in 1977 Granger published

*Forecasting Economic Time Series*(one of 12 books he produced in the course of his career) that became a standard reference work on time series forecasting.On his retirement from UCSD in June 2003, Granger became a visiting scholar at Canterbury University in New Zealand. Weeks later he heard he had shared what was then the £779,000 Nobel Prize with Engle, having been told the news in a 3am telephone call which he suspected at first might have been a hoax. He was reassured only after speaking to members of the prize committee. "I know one of them rather well," he explained, "so when I heard his voice I knew it wasn't a hoax, so I was then cheerfully relaxed."

Granger was knighted in 2005. In the same year, to mark his Nobel achievement, Nottingham University's Economics and Geography department premises were renamed the Sir Clive Granger Building.

Granger once wrote: "A teacher told my mother that 'I would never become successful', which illustrates the difficulty of long-run forecasting on inadequate data."Clive Granger married, in 1960, Patricia Loveland, who survives him with their son and daughter (who, although born four years apart, share the same birthday).

**Robert Engle**

**Engle was born in Syracuse, New York in November, 1942. He graduated from Williams College in 1964 obtaining bachelor's degree of physics in 1964, and received master's degree of physics and doctor's degree of economics from Cornell University in 1969. Engle acted as a professor of MIT between 1968 and 1974. In 1975, he transferred to University of California at San Diego to act as a professor and served as dean of Department of Economics there between 1990 and 1994. He acts as a professor majoring in financial management of Leonard N. Stern School of Business since 1999 and now is a member of American Economic Association and American Academy of Arts & Sciences. Engle had worked with Clive W. J. Granger in University of California.**

His works mainly include Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W. J. Granger, Robert F. Engle and Halbert White, eds., Oxford: Oxford University Press, 1999, ARCH Selected Readings, Oxford University Press1995, Handbook of Econometrics, Robert F. Engle and McFadden, North Holland Press, 1994, Long-run Economic Relationships: Readings in Cointegration, Robert F. Engle and C. W. J. Granger, Oxford University Press, 1991.Engle has acted as a member of American Academy of Arts & Sciences, consultant of Econometrics Association, research fellow of National Bureau of Economic Research (NBER), a member of Econometrics Association, and had won Roger F Murray Award, Institute for Quantitative Research in Finance and Outstanding Teacher, MIT Graduate Economics Association.

He established the concept of economic time series with time-varying volatility: ARCH and developed series of volatility models and methods of statistic analysis. The Royal Swedish Academy of Sciences (RSAS) declared that he is not only an excellent example of research fellows but also a model of financial analyser and that he provided not only a indispensable tool for the research fellows but also find out a short cut in asset pricing, asset allocation, risk assessment for the analysers.As an analyzer of time series, his research involves wave band spectrum regression, hypothesis testing, exogeneity and cointegration analysis, ARCH analysis and high frequency analysis of financial asset return data in 1980s.

As an important pioneer of financial econometrics during the recent 20 years, Engle had great interest in financial market analysis and financial econometrics including financial market microstructure, equity asset, interest rate and option. In Engle's view, with the development of electronic trade, financial econometrics is helpful for the market maker, broker and traders of financial market to establish the optimum strategy in accordance with specific market environment and object in virtue of statistic analysis.

Robert Engle |

The thesis over 100 and three published works made Engle a productive economist. In addition, he sometimes made speeches in academic circles and business circles. Just like what he had said, it was dull to research without application, but it was also bald to bear too many consultant responsibilities without research significance. The splendid achievements of Engle could be attributed to not only academic contribution with Granger, David F. Hendry and other economists and econometrician of University of California at San Diego, but also the actual environment in New York and New York University. New York, as the World Financial Center, provided him with data required for analysis of financial problems and models for his academic research. Additionally, viewpoints about practical problems put forward by his companions majoring in financial practice in New York University: Stern enlightened him for model studies.

It is interesting that Engle, a winner of Nobel Prize in Economics, had been willing to become a physicist during his university time.He had applied for postgraduate degree in physics of Cornell University and University of California at Berkeley. Because his contact via telephone with postgraduate college of University of California at Berkeley had been delayed, he chose Cornell University finally. At the beginning, he had being eager to become a member of superconductor research group. However, he decided to transfer to economic department like many friends one year later. Engle was expert in study of economics although he was majoring in physics, and he obtained his doctor's degree in economics soon after he got his master's degree in physics. Actually, many economists, such as Daniel L. McFadden who won the Nobel Prize in Economics 2000, had learned physics.

According to the Royal Swedish Academy of Sciences, Engle's ARCH theory mode is now an indispensable tool for study of economics and evaluating price and risks by financial market analysers.

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