About
I'm an Associate Professor of Economics at the University of Virginia and an Associate Editor at the Journal of Money, Credit and Banking.
My research is in macroeconomics, finance, and econometrics. I study how households, firms, and professional forecasters form beliefs about the economy — and what those beliefs imply for inflation, interest rates, and asset prices. I also develop econometric methods for estimating nonlinear state-space models.
At UVA I teach time series econometrics (ECON 8720) to Ph.D. students and introductory econometrics (ECON 3720) to undergraduates.
I received my Ph.D. in Economics from UC San Diego in 2017 and my B.S. with Honors in Mathematical and Computational Science from Stanford in 2011.
Working Papers
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Revise and Resubmit, Journal of Monetary EconomicsNew draft coming soonThis paper develops a framework for analyzing individual forecasters' inflation expectations across horizons. We show that expectations are well-summarized by two factors, level and slope, and decompose them into contributions from long-term beliefs, public information, and private information. Our model uniquely captures heterogeneous responses to public information and how they amplify disagreement. Estimating the model using data from the Survey of Professional Forecasters, we find that in normal times, long-horizon disagreement is driven by long-term beliefs, whereas short-horizon disagreement reflects private information. During downturns and periods of heightened uncertainty, heterogeneous responses to public information become the dominant source of disagreement at all horizons. The Fed's response to news reduces disagreement about public information but not about private information or long-term beliefs. Public-information disagreement is associated with delayed policy responses and a price puzzle, underscoring the importance of clear monetary policy communication. Compared with the Great Recession, public-information disagreement about long-run inflation was smaller and shorter-lived during COVID-19, indicating better-anchored expectations.
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Revise and Resubmit, Journal of the European Economic AssociationNew draft coming soonWe propose a methodology to estimate the market value of pharmaceutical drugs. Our approach combines an event study with a model of discounted cash flows and uses stock market responses to drug development announcements to infer the values. We estimate that, on average, a successful drug is valued at $1.62 billion, and its value at the discovery stage is $64.3 million, with substantial heterogeneity across major diseases. Leveraging these estimates, we also determine the average drug development costs at various stages. Furthermore, we explore applying our estimates to design policies that support drug development through drug buyouts and cost-sharing agreements.
Publications
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Learning About the Long Run PublishedForecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia.
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Pockets of Predictability PublishedNote, a minor coding error impacted some of the results using the original method in the paper. In this note, we describe a simple adjustment to the estimation procedure which restores the key results of the published paper.For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability ("pockets") are interspersed with long periods with no return predictability. We document this result empirically using a flexible time-varying parameter model that estimates predictive coefficients as a nonparametric function of time and explore possible explanations of this finding, including time-varying risk premia for which we find limited support. Conversely, pockets of return predictability are consistent with a sticky expectations model in which investors slowly update their beliefs about a persistent component in the cash flow process.
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Existing methods for estimating nonlinear dynamic models are either highly computationally costly or rely on local approximations which often fail adequately to capture the nonlinear features of interest. I develop a new method, the discretization filter, for approximating the likelihood of nonlinear, non-Gaussian state space models. I establish that the associated maximum likelihood estimator is strongly consistent, asymptotically normal, and asymptotically efficient. Through simulations, I show that the discretization filter is orders of magnitude faster than alternative nonlinear techniques for the same level of approximation error in low-dimensional settings and I provide practical guidelines for applied researchers. It is my hope that the method's simplicity will make the quantitative study of nonlinear models easier for and more accessible to applied researchers. I apply my approach to estimate a New Keynesian model with a zero lower bound on the nominal interest rate. After accounting for the zero lower bound, I find that the slope of the Phillips Curve is 0.076, which is less than 1/3 of typical estimates from linearized models. This suggests a strong decoupling of inflation from the output gap and larger real effects of unanticipated changes in interest rates in the post-Great Recession period.
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Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing models with various underlying stochastic processes for the fundamentals, including a rare disasters model. Our method outperforms the solution accuracy of existing methods by orders of magnitude, while drastically simplifying the solution algorithm. The performance of our method is robust to parameters such as the number of grid points and the persistence of the process.
Work in Progress
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Forecasting Fertility Work in Progress
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Fed-induced Underreaction Work in Progress
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What Does the Market Think? Work in Progress
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Forecast Anomalies and Parameter Uncertainty Work in Progress
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Estimating High-Dimensional State Space Models Work in Progress
Contact
lefarmer@virginia.edu
Department of Economics
University of Virginia
248 McCormick Rd
Charlottesville, VA 22904-4182