Information quality-driven business cycles

Research output: Working paper

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Abstract

This paper introduces information quality in a real business cycle model. Information quality relates to the idea that information obtained can inaccurately reflect the actual state of the economy. Using the Survey of Professional Forecasters, I document that forecast errors are larger during downturns, even if agents acquire more information. I then augment a rational inattention model with information search frictions that generate variable information quality. Information depends on both data abundance and information search intensity. Unlike rational inattention models, which are demand driven, I allow for time-varying data abundance, or information supply, generating fluctuations in information quality. The model delivers pro-cyclical information quality, which rationalizes puzzling evidence that information acquisition and uncertainty increase in downturns. A Bayesian estimation of the model for the US economy shows that information quality accounts for sizable fluctuations in uncertainty and output. The model also generates: (i) systematic mistakes when agents do not internalize fluctuations in information quality, (ii) variation in information processing costs, which produce higher frequency and dispersion in price changes during downturns, and (iii) production externalities, as firms do not internalize that more activity generates data abundance, which reduces uncertainty
Original languageEnglish
Number of pages72
Publication statusPublished - 2023

Keywords

  • Business Cycles
  • Information Acquisition
  • Uncertainty

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