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Sign Restrictions and Supply-demand Decompositions of Inflation

Matthew Read

arXiv 7 Sep 2026 · Econometrics

arXiv:2609.06907 · PDF · Extracted main text

Abstract

Sign restrictions on the slopes of supply and demand curves are often used to identify historical decompositions in structural vector autoregressions. I show that the identifying power of these restrictions depends on both reduced-form parameters and realised forecast errors. Consequently, unlike many other structural objects, the strength of identification cannot be assessed from reduced-form parameters alone. Empirically, identified sets for historical decompositions of US inflation are typically largely uninformative, both in aggregate and in most expenditure categories. Existing inflation decompositions are therefore sensitive to auxiliary assumptions used to select among observationally equivalent models.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Shapiro, A. H (2026) Decomposing Supply and Demand Driven Inflation1.00073100%
2Giannone, D. and G. E. Primiceri (2024) The Drivers of Post-pandemic Inflation, Working Paper 32859, National Bureau of Economic Research1.00065100%
3Bergholt, D., F. Canova, F. Furlanetto, N. Maffei-Faccioli, and P. U…0.84333100%
Montiel-Olea_Nesbit_2021unmatched citation key Montiel-Olea_Nesbit_20210.84333100%
5Baumeister, C. and J. D. Hamilton (2015) Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information0.81142100%
6Uhlig, H (2017) Shocks, Sign Restrictions, and Identification, in0.81142100%
7Giacomini, R., T. Kitagawa, and M. Read (2022) Narrative Restrictions and Proxies: Rejoinder0.73732100%
8Inoue, A. and L. Kilian (2026) When Is the Use of Gaussian-Inverse Wishart-Haar Priors Appropriate?0.73732100%
9Antolín-Díaz, J. and J. F. Rubio-Ramírez (2018) Narrative Sign Restrictions for SVARs0.64422100%
10Giacomini, R. and T. Kitagawa (2021) Robust Bayesian Inference for Set-identified Models0.64422100%

Showing the top 10 of 39 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.