Clive Granger Quotes

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I was born in Swansea in the Principality of Wales in September 1934 and named Clive William John Granger. The 'William John' names were traditional Granger boy's names, and my mother liked the name Clive because some popular musician at the time had it.

As far as I could tell, I was the first person anywhere in my family tree to go to university.

On completing my degree, I started a Ph.D. in statistics, although I knew very little about the topic. My supervisor was Professor Harry Pitt, who was an excellent pure mathematician and probabilist.

In 1973, I was offered a professorship at the University of California, San Diego. Although I was certainly not unhappy at Nottingham, I had been there over twenty years from starting undergraduate studies to Professor of Applied Statistics and Econometrics, and I thought that a change of scene was worth considering.

There are many types of economic data, but the type considered by Rob Engle and myself is know as time series.

A potentially useful property of forecasts based on cointegration is that when extended some way ahead, the forecasts of the two series will form a constant ratio, as is expected by some asymptotic economic theory.

I think it is true to say that I am not the first Nobel Prize winner in economics to have little formal training in economics.

I wonder if economics has less basic core material than is necessary for fields such as mathematics, physics, or chemistry, say.

I work with the macro economy, which involves the major variables that measure the health of the whole economy, such as total consumption, investment, income, employment, and inflation.

Forecasts vary in horizon, from a few seconds up to a few days in financial markets, compared to from one to several months for macro variables. We have to provide uncertainty intervals around the central forecasts to indicate the extent to which we are unclear about the future.

Rob Engle and I are concerned with extracting useful implications from economic data, and so the properties of the data are of particular importance.

The stock market is like a small row boat on a rough sea, bouncing around as it drifts, whereas the macro economy is like a large ocean liner, very ponderous and difficult to maneuver but without such a rough journey.

A teacher told my mother that I would never become successful, which illustrates the difficulty of long-run forecasting on inadequate data.

I preferred to use mathematics in some practical fashion and thought that meteorology sounded promising.

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