Standard sticky information pricing models successfully capture the sluggish movement of aggregate prices in response to monetary policy shocks but fail at matching the magnitude and frequency of price changes at the micro level. This paper shows that in a setting where firms choose when to...

This paper formulates and estimates a three-shock US business cycle model. The estimated model accounts for a substantial fraction of the cyclical variation in output and is consistent with the observed inertia in inflation. This is true even though firms in the model reoptimize prices on...

This paper analyses the implications of the evidence on micro price setting gathered by Price-setting Microdata Analysis Network (PRISMA) for inflation dynamics and monetary policy, relying on calibrated models and direct empirical evidence. According to models calibrated to the euro area micro...

This paper documents five stylised facts relating to price adjustment in the euro area, using various micro price datasets collected in a period with relatively low and stable inflation. First, price changes are infrequent in the core sectors. On average, 12% of consumer prices change each...

We study the impact of the COVID-19 pandemic on euro area inflation and how it compares to the experiences of other countries, such as the United States, over the two-year period 2020-21. Our model-based calibration exercises deliver four key results: (1) compositional effects, or the switch...

We document the real-time forecasting performance for output and inflation of the New York Fed dynamic stochastic general equilibrium (DSGE) model since 2011. We find the DSGE's accuracy to be comparable to that of private forecasters before Covid, but somewhat worse thereafter.

We estimate a multi-country, multi-sector New Keynesian model to quantify the drivers of domestic inflation during 2020-23 in several countries, including the United States. The model matches observed inflation together with sector-level prices and wages. We further measure the relative...