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Anastasia Allayioti

3 December 2024
WORKING PAPER SERIES - No. 3003
Details
Abstract
We document that about 33% of the core inflation basket in the euro area is sensitive to monetary policy shocks. We assess potential theoretical mechanisms driving the sensitivity. Our results suggest that items of a discretionary nature, as reflected in a higher share in the consumption baskets of richer households, and those with larger role of credit in financing their purchase, tend to be more sensitive.Non-sensitive items are more frequently subject to administered prices and include non-discretionary items such as rents and medical services. Energy intensity does not seem to drive our results and the sensitive items are not dominated by durable goods, but are relatively evenly split between goods and services. Estimations over different samples show that the impact of monetary policy shocks on sensitive core inflation has become larger recently.
JEL Code
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
1 August 2024
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 5, 2024
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Abstract
Euro area inflation differentials rose sharply during the pandemic and the energy crisis but have since largely returned to previous levels. Monitoring the evolution and nature of inflation differentials is informative when assessing the transmission of the single monetary policy. This box puts the recent developments in inflation dispersion into perspective. Headline inflation and its subcomponents have all experienced considerable divergences across countries, with energy and food inflation playing a significant role. However, with a few exceptions, these temporarily sizeable differentials did not result in substantial changes in relative price levels across countries.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
E65 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Studies of Particular Policy Episodes
1 February 2024
WORKING PAPER SERIES - No. 2901
Details
Abstract
Commodity prices co-move, but the strength of this co-movement changes over time due to structural factors, like changing energy intensity in production and consumption as well as changing composition of underlying shocks. This paper explores whether econometric models that exploit this co-movement and account for parameter instability provide more accurate point and density forecasts of ten major commodity indices viz-a-viz constant coefficient models. Improvements in point forecast accuracy are small, with predictability varying substantially across forecast horizons and commodity indices, but they are large and significant in terms of density forecasting. An economic evaluation reveals that allowing for parameter time variation and commonalities leads to higher portfolios returns, and to higher utility values for investors.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C55 : Mathematical and Quantitative Methods→Econometric Modeling→Modeling with Large Data Sets?
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications