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Economic Forecasts in the Age of Big Data

WASHINGTON, DC – In late July, it came to light in the press that the US Federal Reserve had accidentally published economic forecasts for the next five years on its website. The forecasts, which make clear that the Fed does not expect a recession before 2020, revealed worrying problems not just in terms of data security, but also in the methods used by its economists.

Given that periods of economic expansions historically average about 4.8 years, the Fed’s predictions seem like wishful – and perhaps dangerous – thinking. The economic recovery following the 2009 global financial crisis may have been extremely weak; but we would be wise to prepare for another downturn in the next few years.

The disconnect between the Fed’s forecast data – upon which, in theory, it bases its decisions – and the historical trends is not surprising. Attempts by economists to predict the future have had mixed results, at best; very few foresaw the depth of the Great Recession, even after it had already started. The trouble lies in the fact that many of the leading indicators used to measure the economy rely on out-of-date, incomplete, or flawed data.

For example, forecasters calculate real GDP on the basis of initial monthly estimates of quarterly GDP – a statistic that is often substantially revised as more data become available. As a result, forecasts lag behind reality. During the third quarter of 2008, fewer than 30% of the forecasters who contribute to the Survey of Professional Forecasters predicted a decline in GDP in the remaining months of the year; in fact, GDP plunged more than 8% in the fourth quarter of 2008, one of the largest drops on record.