Extracting the Italian output gap: a Bayesian approach
Mauro Bernardi, Antonio Di Ruggiero

TL;DR
This paper introduces a Bayesian method for extracting Italy's output gap from quarterly GDP data, incorporating inflation dynamics and validating results against official cycle datings.
Contribution
It develops a novel Bayesian approach with an adaptive sampler for trend-cycle decomposition, including inflation effects, tailored to Italian economic data.
Findings
Inflation significantly influences the estimated output gap.
The method aligns well with OECD official cycle datings.
The approach effectively captures the Italian business cycle phases.
Abstract
During the last decades particular effort has been directed towards understanding and predicting the relevant state of the business cycle with the objective of decomposing permanent shocks from those having only a transitory impact on real output. This trend--cycle decomposition has a relevant impact on several economic and fiscal variables and constitutes by itself an important indicator for policy purposes. This paper deals with trend--cycle decomposition for the Italian economy having some interesting peculiarities which makes it attractive to analyse from both a statistic and an historical perspective. We propose an univariate model for the quarterly real GDP, subsequently extended to include the price dynamics through a Phillips curve. This study considers a series of the Italian quarterly real GDP recently released by OECD which includes both the 1960s and the recent global…
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Taxonomy
TopicsMonetary Policy and Economic Impact · Italy: Economic History and Contemporary Issues · Complex Systems and Time Series Analysis
