Firms pricing in the UK: survey expectations and industry-level determinants (with Cristina Griffa, University of Chile)
Abstract: We exploit the CBI survey that collects UK firms’ quarterly expectations and perceptions about their own-price inflation and about inflation in markets the firms compete in (own-industry) since 2009. Own-price inflation expectations are found to be robustly positively associated with firms’ price changes. There is evidence for inattention to industry-wide inflation, since own-price and own-industry expected inflation is often reported to be identical. However, when firms expect higher inflation in their industry compared to their own-price expectations, this is associated with additional price increases. The effects are asymmetric, i.e., non-existent when own-industry inflation is expected to be lower. This can have aggregate implications since the effects of inflationary shocks could be amplified when they affect entire industries and firms are catching up to each other in their price setting.
Coverage: Catherine L. Mann: "Mind the gap(s): Inflation data and prospects". Speech given at the Official Monetary and Financial Institutions Forum.
Boosted Inflation: supply- and demand determinants of inflation using machine learning (with Marcus Buckmann and Philip Schnattinger)
Abstract: We propose the Blockwise Boosted Inflation Model: an interpretable, non-linear, boosted tree method that decomposes inflation dynamics into components akin to an open-economy hybrid Phillips curve. Demand and supply contributions are identified by imposing sign restrictions on the association between inflation and indicators. We model monthly CPI inflation in the United Kingdom. The recent rise in UK inflation is explained by a combination of supply determinants, demand, and changes in the role of lagged inflation and expectations. Non-linearities that the model learns can be traced over time and help explain the recent rise in inflation---a counterfactual model that does not update its functional forms fares worse in explaining inflation in the recent episode. Strong non-linear effects from global supply pressures and cost-related variables contributed to inflation during 2021-22, but these effects quickly reverted to the flat region. On the demand side, we detect a convex Phillips curve relationship for labour market tightness and unemployment. Our model also performs competitively in out-of-sample forecasting against an AR benchmark and other machine-learning models.
Lloyd, S., H. Pill, and G. Potjagailo (2025). Inflation Targeting and monetary policy in practice: the experience of the Bank of England. Chapter 26 in: Research Handbook on Inflation. Edited by G. Ascari and R. Trezzi. May 2025.
Kohns, D. and G. Potjagailo (2025). Flexible Bayesian MIDAS: time-variation, group-shrinkage, and sparsity Journal of Business and Economic Statistics, April 2025. Replication codes coming soon.
Joseph, A., G. Potjagailo, C. Chakraborty, and G. Kapetanios (2024). Forecasting UK inflation bottom up. International Journal of Forecasting, Volume 40, Issue 4, October–December 2024, Pages 1521-1538
Potjagailo, G. and M.H Wolters (2023). Global financial cycles since 1880. Journal of International Money and Finance, Vol. 131, March 2023.
Jannsen, N., Potjagailo, G. and M.H. Wolters (2019). Monetary policy during financial crises: Is the transmission mechanism impaired? International Journal of Central Banking, Vol. 15, No. 4, October 2019.
Potjagailo, G. (2017). Spillover effects from Euro Area Monetary Policy across Europe: A Factor-Augmented VAR approach. Journal of International Money and Finance (72), 127-147.
"Dissecting UK service inflation with a neural network Phillips curve" (with Marcus Buckmann and Philip Schnattinger), Bank Underground blog post, July 2023
"How broad-based is UK inflation?" (with Boromeus Wanengkyrtio and Jenny Lam), Bank Underground blog post, October 2022
"Global Financial Cycles since 1880" (with Maik Wolters), Bank Underground blog post, August 2020