R. Andrew Butters is an Assistant Professor in the department of Business Economics & Public Policy. Before joining the department, he worked as an associate economist at the Federal Reserve Bank of Chicago. His areas of expertise include industrial organization, applied econometrics, productivity, market integration, demand fluctuations, and economic cycles.
Industry Expertise (3)
Hotels and Resorts
Areas of Expertise (6)
Rising Star Award, International Industrial Organization Conference
Kellogg School of Management Graduate Fellowship
Kellog School of Management, Northwestern University: Ph.D. 2015
University of North Carolina at Chapel Hill: B.A., Economics 2007
Measures of productivity reveal large differences across producers even within narrowly defined industries. Traditional measures of productivity, however, will associate differences in demand volatility to differences in productivity when adjusting factors of production is costly.
A growing literature suggests that uniform pricing in the face of large demand fluctuations represents a substantial deviation from profit maximization -- a puzzle for economists. We show that this need not be the case, through an investigation of quantity and pricing fluctuations at seasonal frequencies.
I provide three comparative statics involving the level of demand uncertainty for the newsvendor model, two of which lead to robust predictions. I show that for distributions of demand that are greater in the dispersive order, both the expected (censored) sales and share of inventory sold fall.
We document the influence of factor markets in determining the extent of the market, by appealing to the Mundell Hypothesis that trade in goods markets and factor markets are substitutes. We confirm this influence using the U.S. wholesale market for electric power.
Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for forecasting GDP growth. However, certain specification choices such as model size and prior selection can affect their relative performance.
We approach the task of monitoring financial stability
within a framework that balances the costs and benefits of identifying future crisis-like conditions based on past U.S. financial crises. Our results indicate that the National Financial Conditions Index (NFCI) produced by the Federal Reserve Bank of Chicago is a highly predictive and robust indicator of financial stress at leading horizons of up to one year, with measures of leverage playing a crucial role in signaling financial imbalances. At longer forecast horizons, we propose an alternative sub-index of the NFCI that captures the relationship between non-financial leverage, financial stress, and economic activity.
Monitoring financial stability requires an understanding of both how traditional and evolving financial markets relate to each other and how they relate to economic conditions. This article describes two new indexes of financial conditions that aim to quantify these relationships.
By incorporating the Harvey accumulator into the large approximate dynamic factor framework of Doz et al. (2006), we are able to construct a coincident index of financial conditions from a large unbalanced panel of mixed frequency financial indicators. We relate our financial conditions index, or FCI, to the concept of a "financial crisis" using Markov-switching techniques. After demonstrating the ability of the index to capture "crisis" periods in U.S. financial history, we present several policy-geared threshold rules for the FCI using Receiver Operator Characteristics (ROC) curve analysis.
Examining industrialized countries, the authors find that large deficits are not associated with higher inflation contemporaneously, nor are they associated with the emergence of higher inflation in subsequent years. This finding suggests that countries that can afford large deficits have built solid reputations and institutions supporting a sound monetary policy and the reversion to a stable fiscal regime.