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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
Econometric Theory, forthcoming
Abstract
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. 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 from both a frequentist and a Bayesian perspective about the way that results are typically reported for VARs that are set-identified using sign and other restrictions. We call attention to a common but previously unrecognized error in estimating structural elasticities and show how to correctly estimate elasticities even in the case when one only knows the effects of a single structural shock.
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Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics, No. 2, 2024
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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Measuring Market Expectations
Christiane Baumeister
Handbook of Economic Expectations, November 2022
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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Energy Markets and Global Economic Conditions
Christiane Baumeister, Dimitris Korobilis, Thomas K. Lee
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
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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Advances in Using Vector Autoregressions to Estimate Structural Magnitudes
Christiane Baumeister, James D. Hamilton
UC San Diego, 2021
published in:
Econometric Theory
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
NBER Working Paper, No. 29232, 2021
published in:
Handbook of Economic Expectations
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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A Comparison of Monthly Global Indicators for Forecasting Growth
Christiane Baumeister, Pierre Guérin
International Journal of Forecasting, No. 3, 2021
Abstract
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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Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions
Christiane Baumeister, James D. Hamilton
Journal of International Money and Finance, December 2020
Abstract
This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha’s (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.
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A Comparison of Monthly Global Indicators for Forecasting Growth
Christiane Baumeister, Pierre Guérin
NBER Working Paper, No. 28014, 2020
published in:
International Journal of Forecasting
Abstract
This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world GDP using mixed-frequency models. We find that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecast accuracy, while other monthly measures have more mixed success. This global economic conditions indicator contains valuable information also for assessing the current and future state of the economy for a set of individual countries and groups of countries. We use this indicator to track the evolution of the nowcasts for the US, the OECD area, and the world economy during the coronavirus pandemic and quantify the main factors driving the nowcasts.
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