Belief Distortions and Macroeconomic Fluctuations / Francesco Bianchi, Sydney C. Ludvigson, Sai Ma.

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Bianchi, Francesco

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Material type: Text

TextSeries: Working Paper Series (National Bureau of Economic Research) ; no. w27406.Publication details: Cambridge, Mass. National Bureau of Economic Research 2020.Description: 1 online resource: illustrations (black and white)Subject(s):

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Abstract: This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.

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June 2020.

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to publicly available information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with biases in expectations evolving dynamically in response to cyclical shocks. The results suggest that artificial intelligence algorithms can be productively deployed to correct errors in human judgement and improve predictive accuracy.