A new method to assess the degree of information rigidity using fixed-event forecasts

Luciano Vereda, João Savignon and Tarciso Gouveia da Silva
Authors registered in the RePEc Author Service: Luciano Vereda Oliveira

International Journal of Forecasting, 2021, vol. 37, issue 4, 1576-1589

Abstract: We propose a new method to explore the information content of fixed-event forecasts and estimate structural parameters that are keys to sticky and noisy information models. Estimation follows a regression-based framework in which estimated coefficients map one-to-one with parameters that measure the degree of information rigidity. The statistical characterization of regression errors explores the laws that govern expectation formation under sticky and noisy information, that is, they are coherent with the theory. This strategy is still unexplored in the literature and potentially enhances the reliability of inference results. The method also allows linking estimation results to the signal-to-noise ratio, an important parameter of noisy information models. This task cannot be accomplished if one adopts an “agnostic” characterization of regression errors. With regard to empirical results, they show a substantial degree of information rigidity in the countries studied. They also suggest that the theoretical characterization of regression errors yields a more conservative picture of the uncertainty surrounding parameter estimates.

Keywords: Forecast behavior; Sticky information; Noisy information; Inflation forecasts; Output growth forecasts (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:37:y:2021:i:4:p:1576-1589

DOI: 10.1016/j.ijforecast.2021.03.001

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