Working Papers

Expectations for Gasoline Prices and Inflation: Evidence from Households With Karl Whelan, Constantin Burgi.

Conditionally Accepted JMCB

Abstract

Gasoline prices are highly salient to consumers and, for this reason, they may have an outsized influence on their thinking about inflation. We examine how people’s expectations about gasoline prices influence their expectations for overall inflation. We find little evidence from two US household surveys that people over-react to their beliefs about expected gasoline prices when formulating their expectations about overall inflation.

Firm Climate Investment: A Glass Half-Full With Ivan Yotzov, Nicholas Bloom, Philip Bunn, Paul Mizen, Gregory Thwaites.

NBER, VOXEU

Abstract

We analyze the importance of climate-related investment using a large economy-wide survey of UK firms. Over half of firms expect climate change to have a positive impact on their investment in the medium term, with around a quarter expecting a large impact of over 10%. Around two-thirds of these investments are expected to be in addition to normal capital expenditure, with some firms investing less elsewhere. These investments will be driven by larger firms as well as those in more energy-intensive sectors. Climate investments are expected mainly in switching to green energy sources and improving energy efficiency, and firms expect to finance these mainly using internal cash reserves. Overall, although firms are expecting to invest more resources in adapting to climate change, under reasonable assumptions, these investments are still not sufficient to meet the estimated targets implied by the UK Net Zero Pathway.

The Drivers of Household Inflation Uncertainty With Philip Schnattinger.

Draft

Abstract

Applying the round-number methods proposed in Binder (2017), we infer an individual's cognitive uncertainty about product groups. We use the responses of individuals point expectations about gasoline, food, medical, education, rent, and gold prices in the NYFed Survey of Consumers Expectation. Of these individual product groups, food prices are found to be the main driver of an individual's aggregate uncertainty about future inflation. We then show that a monetary policy is most effective at reducing food price uncertainty.

Work in Progress

Inflation Uncertainty and Household Wage Growth Expectations With Philip Schnattinger.

Draft coming soon!

Abstract

In this paper, we study the heterogeneous effects of individual-level uncertainty about prices (subjective uncertainty) on individual wage growth expectation decisions using the FRBNY Survey of Consumer Expectations. We focus on the transmission of the first (level) and second moment (uncertainty) of expected inflation on the level of wage growth expectations during uncertain times and how these differ along the income distribution using a novel instrument constructed from the survey responses. We find that individual-level expected inflation uncertainty is positively correlated with wage growth expectations. Moreover, higher inflation uncertainty is linked to a greater rise in wage expectations for poorer households. We explain this observation with a novel mechanism: workers precautiously bargain for higher wages when uncertainty about inflation risks the erosion of their real wages. Thus, inflation uncertainty may be an additional driver of wage growth, especially for lower-income workers.

Salience in Inflation Expectations: Evidence from the UK.

Draft coming soon!

Abstract

Shedding light on economic growth: Nowcasting in Venezuela With Eurydice Fotopoulou, Maria Belen Sbrancia

Draft coming soon!

Abstract

Economic forecasting in Venezuela presents significant challenges due to the discontinuation of key macroeconomic data publication by the Banco Central de Venezuela since Q1 2019. The economy has experienced a sharp and prolonged contraction, particularly between 2013 and 2020, shrinking to a quarter of its 2012 size. The absence of reliable data on fundamental economic indicators—such as real GDP, trade flows, and manufacturing activity—combined with structural shifts in the economy, has severely constrained the ability to conduct accurate macroeconomic analysis and forecasting. This paper explores alternative approaches to addressing these data limitations by integrating traditional and non-traditional data sources with machine learning and econometric techniques to estimate real GDP. Specifically, it evaluates the applicability of Random Forest, Stacking methods, and the Dynamic Factor Model in the case of Venezuela. The analysis assesses the advantages and limitations of these methodologies, highlighting their potential to bridge critical data gaps and enhance economic forecasting in environments where direct data collection is limited. These approaches may offer broader applicability for forecasting in data-scarce economies, informing policy design and economic decision-making in similarly constrained contexts.

Media Sentiments, Politics and Expectations: Survey data from Fed Cleveland With Constantin Burgi, Edward S. Knotek II

Draft coming soon!

Abstract

Subjective Household Philips Curve: Survey data from ECB With Anushka Mitra, Einar Paz

Draft coming soon!

Abstract