Professor Sir Clive Granger, who died on May 27 aged 74, was a Nobel prize-winning economist whose work on analysing economic data was credited with improving the forecasting performance of the Treasury and the Bank of England.

In 2003 he 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 *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.

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".