Macroeconomic forecasts are used extensively in industry and government even though the historical accuracy and reliability is questionable. Moreover, professional forecasters lack a test environment in which they can test their forecasting ability. We design a play-money market game for economic variables that aggregates macro-economic information. We analyse participation and learning in such an online game. In our platform learning occurs on three levels. First, participants learn how to trade in a continuous double auction just as in stock markets. Second, they learn about their own macro-economic forecasting ability in comparison to their peers. Third, by following market forecasts they learn about the current state of the economy. We show that the game successfully aggregates macro-economic information as forecast errors fall over the prediction horizon. The game-generated forecasts compare well to the Bloomberg- survey forecasts, the industry standard.
Teschner, Florian and Weinhardt, Christof, "LEARNING BY TRADING IN A MACRO-ECONOMIC FORECASTING GAME" (2012). ECIS 2012 Proceedings. 133.