As we wait for Gross Domestic Product data to be released on Thursday, Massey University has launched a high tech real time GDP tracker which could revolutionise the way we keep track of the nation's economic progress.

The GDPLive project aims to create a real time model of GDP using up-to-date data like PayMark's electronic card spending figures.

GDP data due this week will be for the third quarter - to September 30.

That means that some of the data it is based on will be almost six months out of date.

This project was an attempt to use the latest computer technology and the growing volume of digital data that organisations collect to better model GDP in real time, says Christoph Schumacher, professor of economics and innovation at the Massey Business School.


It uses complex self-learning (artificial intelligence) to constantly improve its modelling.

"If we know how much money exchanged hands yesterday using cards, then that is a pretty good indication of economic activity because GDP measures market based transactions of how many products are sold," says Schumacher.

It has a partnership with Port Connect to provide data from Ports of Auckland and Tauranga to monitor movement of goods in and out of the country.

KiwiRail offered freight data and - via the Interislander Ferry - real time tourism data.

The project also pulled in publicly available data from government sources like Stetson, The Reserve Bank, Immigration NZ, NZ Transport Agency and the Ministry of Business Innovation and Employment.

They also used traffic data from the Ministry of Transport, all the macroeconomic indicators from Stats NZ and the Reserve Bank.

It has been developed by the Knowledge Exchange Hub, a multi-disciplinary research hub at Massey University.

The project is headed by Schumacher and Dr Teo Susnjak, a computer scientist with Massey University's Institute of Natural and Mathematical Sciences.


Schumacher says the key was to build a programme that could process all the up-to-date data and also continually benchmark it against historic data.

"So it keeps learning in little steps and when we expose it to the new data, with what it has learned from the past it then makes a prediction."

The project had been underway for three years and the algorithm had been learning using data with data going back 10 years.

So far it had proved highly accurate and inside expected margin of error, Schumacher says.

For example it successfully anticipated the strong second quarter GDP data in June that surprised many economists.

"But this will only get better," he said. "If something completely unexpected were to happen our algorithm will learn from this."

- To follow the live GDP tracker visit