Understanding the role of sticky price and sticky information for inflation dynamics is a key issue in economics. The literature has treated the two forms of stickiness as independent. This paper proposes a new dual stickiness Phillips curve based on dependence among the events of setting prices and updating information. Using US data over the period 1947Q1–2020Q1, the new model is scrutinized against a dual stickiness model without dependence, a pure sticky price model, and a pure sticky information model, through in- and out-of-sample analyses. The results show: (i) the new model outperforms the model without dependence in-sample; (ii) the dual stickiness models perform similarly out-of-sample; and (iii) the pure sticky models yield the worst forecasts. The results have some implications for policy makers and practitioners. A policy maker may consider the new model given its performance in- and out-of-sample, while a practitioner may prefer the model without dependence, given its lesser complexity and its competitive forecasting performance.

On the role of dependence in sticky price and sticky information Phillips curve: Modelling and forecasting

Costantini M.
;
2021

Abstract

Understanding the role of sticky price and sticky information for inflation dynamics is a key issue in economics. The literature has treated the two forms of stickiness as independent. This paper proposes a new dual stickiness Phillips curve based on dependence among the events of setting prices and updating information. Using US data over the period 1947Q1–2020Q1, the new model is scrutinized against a dual stickiness model without dependence, a pure sticky price model, and a pure sticky information model, through in- and out-of-sample analyses. The results show: (i) the new model outperforms the model without dependence in-sample; (ii) the dual stickiness models perform similarly out-of-sample; and (iii) the pure sticky models yield the worst forecasts. The results have some implications for policy makers and practitioners. A policy maker may consider the new model given its performance in- and out-of-sample, while a practitioner may prefer the model without dependence, given its lesser complexity and its competitive forecasting performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/172073
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