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Professor Christiane Baumeister, PhD

Professor Christiane Baumeister, PhD
Current Position

since 6/19

Research Fellow Department of Macroeconomics

Halle Institute for Economic Research (IWH) – Member of the Leibniz Association

since 7/20

Professor of Economics

University of Notre Dame

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Research Interests
  • empirical macroeconomics
  • forecasting
  • applied econometrics
  • energy economics

Christiane Baumeister joined the Department of Macroeconomics as a Research Fellow in June 2019. Her research focuses on empirical macroeconomics, applied time series econometrics, energy markets, monetary economics, and forecasting.

Christiane Baumeister is the Lambert Family Professor of Economics at the University of Notre Dame. She is also a Faculty Research Fellow of the NBER and a Research Affiliate of the CEPR. Prior to joining Notre Dame in July 2015, she was a Principal Researcher in the International Economic Analysis Department at the Bank of Canada. She has been a visiting scholar at the IMF and various central banks, including the Federal Reserve Banks of St. Louis, Dallas, Cleveland, and Kansas City, the Reserve Bank of New Zealand, the Bank of France, and the Bank of Finland.

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Professor Christiane Baumeister, PhD
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Publications

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Tracking Weekly State-Level Economic Conditions

Christiane Baumeister Danilo Leiva-León Eric Sims

in: Review of Economics and Statistics, forthcoming

Abstract

This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.

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Energy Markets and Global Economic Conditions

Christiane Baumeister Dimitris Korobilis Thomas K. Lee

in: Review of Economics and Statistics, No. 4, 2022

Abstract

We evaluate alternative indicators of global economic activity and other market funda-mentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. World industrial production is one of the most useful indicators. However, by combining measures from several different sources we can do even better. Our analysis results in a new index of global economic conditions and measures for assessing future energy demand and oil price pressures. We illustrate their usefulness for quantifying the main factors behind the severe contraction of the global economy and the price risks faced by shale oil producers in early 2020.

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Structural Vector Autoregressions with Imperfect Identifying Information

Christiane Baumeister James D. Hamilton

in: American Economic Association Papers and Proceedings, May 2022

Abstract

The problem of identification is often the core challenge of empirical economic research. The traditional approach to identification is to bring in additional information in the form of identifying assumptions, such as restrictions that certain magnitudes have to be zero. In this paper, we suggest that what are usually thought of as identifying assumptions should more generally be described as information that the analyst had about the economic structure before seeing the data. Such information is most naturally represented as a Bayesian prior distribution over certain features of the economic structure.

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Working Papers

Advances in Using Vector Autoregressions to Estimate Structural Magnitudes

Christiane Baumeister James D. Hamilton

in: UC San Diego, 2021

Abstract

This paper discusses drawing structural conclusions from vector autoregressions. We call attention to a common error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one knows only the effects of a single structural shock and the covariance matrix of the reduced-form residuals. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error. We raise concerns about the way that results are typically reported for VARs that are set-identified using sign and other restrictions.

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Measuring Market Expectations

Christiane Baumeister

in: NBER Working Paper, No. 29232, 2021

Abstract

Asset prices are a valuable source of information about financial market participants' expectations about key macroeconomic variables. However, the presence of time-varying risk premia requires an adjustment of market prices to obtain the market's rational assessment of future price and policy developments. This paper reviews empirical approaches for recovering market-based expectations. It starts by laying out the two canonical modeling frameworks that form the backbone for estimating risk premia and highlights the proliferation of risk pricing factors that result in a wide range of different asset-price-based expectation measures. It then describes a key methodological innovation to evaluate the empirical plausibility of risk premium estimates and to identify the most accurate market-based expectation measure. The usefulness of this general approach is illustrated for price expectations in the global oil market. Then, the paper provides an overview of the body of empirical evidence for monetary policy and inflation expectations with a special emphasis on market-specific characteristics that complicate the quest for the best possible market-based expectation measure. Finally, it discusses a number of economic applications where market expectations play a key role for evaluating economic models, guiding policy analysis, and deriving shock measures.

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