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Expectations lie at the heart of all macroeconomic and financial decision-making. At the individual level, our expectations determine how much we consume and how much we save. At the aggregate level, expectations drive credit market conditions, a country's price level, investment, and output.
Using survey data, I study the expectations of households, firms, and professional forecasters, assessing the role of psychologically founded behavioral biases that influence their forecasts.
I evaluate the implications of my findings for researchers and policymakers everywhere.
This paper implements a model with a population of heterogeneous macro forecasters. Their objectives are to forecast output and inflation, both inputs in standard New Keynesian macro models. The model is implemented by first calibrating the agents to professional forecasters at the micro level. Model runs then try to replicate both the dynamics, bias, and cross-sectional heterogeneity of forecasts and the economy. These are done both in a model with static forecasters, and one where the forecasters are learning from each other in a social/epidemiological fashion.
Cross-Country Inflation Expectations: Evidence of Heterogeneous and Synchronized 'Mistakes'
"Generalizations about the outcome of well-known tests of full-information rational expectations are not ubiquitous across countries. The implication is that there is no one-size-fits-all approach to modeling the Expectation Formation Process." Excerpt from Paper
This paper presents an empirical analysis of the global landscape of departures from the assumption of Full-Information Rational Expectations (FIRE) in the inflation predictions of professional forecasters, across forty-six countries from 1990 to 2020. I make the central argument that there is a need for a more international approach to understanding the Expectation Formation Process (EFP). My analysis has three main findings. Firstly, I find widespread heterogeneity in the magnitude and direction of departures from FIRE across countries and forecast horizons. Secondly, I present novel evidence about violations of FIRE that at times contradict well-established stylized facts about belief formation. Thirdly, using a Bayesian Dynamic Factor Model (BDFM), I provide evidence of the existence of a common factor in cross-country forecast errors, accounting for as much as 3% of the variability in domestic forecast errors across all countries and 6% across advanced economies. I discuss how this synchronization of ‘mistakes’, particularly among advanced economies, has likely contributed to generalizations about the departure of survey-based expectations from the assumption of FIRE.
Expectations' Formation and Boom-Bust Cycles in Mortgage Lending - [Best Third-Year Paper ]
I study the role of forecaster sentiment amongst credit spread forecasters in the mortgage lending process. I report two key findings: forecasters over-react to new information on current conditions, and this over-reaction bears significance in the loan-to-value (LTV) and debt-to-income (DTI) ratio decisions of mortgage lenders. I present new evidence which suggests that over-reaction varies asymmetrically amongst forecaster groups and discuss the relevant psychologically founded biases which may contribute to this result. In examining how these biases impact the lending decision, I propose a novel two-step disaggregation of the credit spread forecast. I show that the forecast error matters disproportionately more in the LTV versus the DTI decision.
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