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We propose a proxy for the inflation expectations of firms based on aggregating own-firm probabilistic unit cost expectations. Unlike other surveys of firms or households that elicit 'aggregate' expectations, we focus on idiosyncratic costs that firms are well aware of, plan for, and matter for price setting. We document five key findings. first, in aggregate, firms' unit cost expectations significantly outperform households' inflation expectations and are at least as accurate as the expectations of professional forecasters in out-of-sample forecasting exercises. Second, once aggregated, firms' unit cost realizations closely comove with US inflation statistics. Third, using a novel, flexible technique to parametrically estimate firms' unit cost uncertainty, we find that up until early 2020, the evolution of firms' views was similar to other survey and market-based measures of inflation uncertainty. Fourth, using special questions, we find evidence that information treatments about aggregate inflation and policymakers' forecasts do little to alter firms' unit cost expectations. And, last, we show that unit costs at the firm level are an important determinant of their own price setting behavior. JEL classification: E6, E31, E52, L2 Key words: bimodality, inflation expectations, probability distributions, randomized controlled trials, uncertainty, unit cost https://doi.org/10.29338/wp2021-12a, 'Price stability is that state in which expected changes in the general price level do not effectively alter business or household decisions.' --Federal Reserve Chairman Alan Greenspan, July 1996, FOMC [...]

By carefully matching the data sets from the Michigan Survey of Consumers with the Survey of Professional Forecasters, we show that there exists substantial heterogeneity in the propensity of U.S. households to learn from experts in forming inflation expectations. Additional results for a group of European economies broadly confirm this observation. We advance an extended version of the sticky‐information model to analyze disagreement in consumer inflation expectations. Besides differences in consumers' propensities to learn, disagreement in our model arises from heterogeneity in consumers' fundamental inflation and past expectations and experts' different views about future inflation. [ABSTRACT FROM AUTHOR]

We document and evaluate how businesses are reacting to the COVID-19 crisis through August 2020. First, on net, firms see the shock (thus far) largely as a demand rather than supply shock. A greater share of firms reports significant or severe disruption to sales activity than to supply chains. We compare these measures of disruption to their expected changes in selling prices and find that, even for firms that report supply chain disruption, they expect to lower near-term selling prices on average. We also show that firms are engaging in wage cuts and expect to trim wages further before the end of 2020. These cuts stem from firms that have been disproportionally negatively affected by the pandemic. Second, firms (like professional forecasters) have responded to the COVID-19 pandemic by lowering their one-year-ahead inflation expectations. These responses stand in stark contrast to that of household inflation expectations (as measured by the University of Michigan or the New York Fed). Indeed, firms' one-year-ahead inflation expectations fell precipitously (to a series low) following the onset of the pandemic, while household measures of inflation expectations jumped markedly. Third, despite the dramatic decline in firms' near-term inflation expectations, their longer-run inflation expectations remain reasonably stable. JEL classification: E31, E32 Key words: business expectations, COVID-19, demand shock, inflation, pandemic, supply shock, Working Paper 2020-17a August 2020 (Revised December 2020) 'Now we see a big shock to demand, and we see core inflation dropping to 1 percent. And I do think for [...]

The COVID-induced recession began in March 2020 for the United States. We identify this turning point by applying a Bayesian sequential quickest detection method to a real-time index of economic activity. Supporting evidence is also found from macroeconomic data releases and stock markets. [ABSTRACT FROM AUTHOR]