Learning, disagreement and inflation forecasting

Ji Chen, Xinglin Yang and Xiliang Liu

The North American Journal of Economics and Finance, 2022, vol. 63, issue C

Abstract: This paper studies inflation forecasting based on the Bayesian learning algorithm which simultaneously learns about parameters and state variables. The Bayesian learning method updates posterior beliefs with accumulating information from inflation and disagreement about expected inflation from the Survey of Professional Forecasters (SPF). The empirical results show that Bayesian learning helps refine inflation forecasts at all horizons over time. Incorporating a Student’s t innovation improves the accuracy of long-term inflation forecasts. Including disagreement has an effect on refining short-term inflation density forecasts. Furthermore, there is strong evidence supporting a positive correlation between disagreement and trend inflation uncertainty. Our findings are helpful for policymakers when they forecast the future and make forward-looking decisions.

Keywords: Inflation; Bayesian learning; Unobserved components; Disagreement; Heavy tails (search for similar items in EconPapers)
JEL-codes: C11 C32 E31 E37 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:63:y:2022:i:c:s1062940822001693

DOI: 10.1016/j.najef.2022.101834

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