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


University of Notre Dame


  • 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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Energy Markets and Global Economic Conditions

Christiane Baumeister Dimitris Korobilis Thomas K. Lee

in: Review of Economics and Statistics, im Erscheinen


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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Tracking Weekly State-Level Economic Conditions

Christiane Baumeister Danilo Leiva-León Eric Sims

in: Review of Economics and Statistics, im Erscheinen


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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A Comparison of Monthly Global Indicators for Forecasting Growth

Christiane Baumeister Pierre Guérin

in: International Journal of Forecasting, Nr. 3, 2021


This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.

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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes

Christiane Baumeister James D. Hamilton

in: UC San Diego, 2021


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



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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