Analysing inflation with semi-structural models

Thomas Hasenzagl, Filippo Pellegrino, Lucrezia Reichlin and Giovanni Ricco

Chapter 7 in Research Handbook on Inflation, 2025, pp 141-166 from Edward Elgar Publishing

Abstract: The chapter explores semi-structural time series models as a tool for analysing, forecasting, and nowcasting inflation. We define as semi-structural models a class of multivariate time series models, in the tradition of Harvey (1985, 1990), where minimal economic restrictions are used to identify common and idiosyncratic trends and cyclical components in the data. This chapter shows that these models are suitable to analyse price pressure at business cycle frequency and to provide real-time indicators of unobserved economic measures such as trend inflation and output gap. Moreover, it compares semi-structural models with other common models in macroeconomics, like vector autoregressions (VARs), showing that semi-structural models often outperform them in forecasting accuracy.

Keywords: Real-time forecasting; Output gap; Phillips curve; Structural time series; Semi-structural models; Bayesian estimation; JEL classification; C11; C32; C53; E31; E32; E52 (search for similar items in EconPapers)
Date: 2025
ISBN: 9781035327751
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