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Professorin Christiane Baumeister, Ph.D.

Professorin Christiane Baumeister, Ph.D.
Aktuelle Position

seit 6/19

Research Fellow der Abteilung Makroökonomik

Leibniz-Institut für Wirtschaftsforschung Halle (IWH)

seit 7/20

Professorin

University of Notre Dame

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Forschungsschwerpunkte
  • empirische Makroökonomik
  • Prognosen
  • angewandte Ökonometrie
  • Energiemärkte

Christiane Baumeister ist seit Juni 2019 Research Fellow am IWH. Ihre Forschungsinteressen umfassen empirische Makroökonomik, Energiemärkte, angewandte Zeitreihenökonometrie, Geldpolitik und Prognosen.

Christiane Baumeister ist Professorin an der wirtschaftswissenschaftlichen Fakultät der University of Notre Dame. Sie ist auch ein Faculty Research Fellow des NBER und ein Research Affiliate des CEPR. Zuvor war sie als Principal Researcher in der internationalen Wirtschaftsabteilung der Bank of Canada beschäftigt. Sie war als Gastforscherin am IWF und verschiedenen Zentralbanken wie den Federal Reserve Banks von St. Louis, Dallas, Cleveland und Kansas City, der Reserve Bank of New Zealand, der Banque de France und der Bank of Finland.

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Professorin Christiane Baumeister, Ph.D.
Professorin Christiane Baumeister, Ph.D.
Mitglied - Abteilung Makroökonomik
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Publikationen

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Tracking Weekly State-Level Economic Conditions

Christiane Baumeister Danilo Leiva-León Eric Sims

in: Review of Economics and Statistics, im Erscheinen

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, Nr. 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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Arbeitspapiere

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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