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| Research Update |
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| New Titles in the Staff
Reports Series |
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| Number 4, 2009 |
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| Quantitative Methods |
| No. 412, December 2009 |
| Dynamic Hierarchical Factor Models |
| Emanuel Moench, Serena Ng, and Simon Potter |
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| This paper uses multi-level factor models to characterize within- and between-block variations as well as idiosyncratic noise in large dynamic panels. Block-level shocks are distinguished from genuinely common shocks, and the estimated block-level factors are easy to interpret. The framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks. The model is estimated using a Markov-chain Monte Carlo algorithm that takes into account the hierarchical structure of the factors. The authors organize a panel of 447 series into blocks according to the timing of data releases and use a four-level model to study the dynamics of real activity at both the block and aggregate levels. While the effect of the economic downturn of 2007-09 is pervasive, growth cycles are synchronized only loosely across blocks. The state of the leading and the lagging sectors, as well as that of the overall economy, is monitored in a coherent framework. |
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| No. 415, December 2009 |
| Measuring Consumer Uncertainty about Future Inflation |
Wandi Bruine de Bruin, Charles F. Manski, Giorgio Topa, and Wilbert van der Klaauw |
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| Current survey measures of consumer inflation expectations contain no information about an individual’s uncertainty about future inflation. This information is important not only for forecasting inflation and other macroeconomic outcomes, but also for assessing a central bank’s credibility and effectiveness of communication. In November 2007, the authors of this paper began administering web-based surveys to participants in RAND’s American Life Panel. In addition to providing point predictions, respondents were asked to provide subjective probability distributions of future inflation outcomes. The authors find that their measures of individual forecast densities and uncertainty are internally consistent and reliable. Those who are more uncertain about year-ahead price inflation are also more uncertain about longer term price inflation and future wage changes. Participants expressing higher uncertainty in their density forecasts make larger revisions to their point forecasts over time. Finally, while the authors’ measure of aggregate consumer uncertainty is correlated with the dispersion in point forecasts among individuals, the two measures are distinct concepts—both relevant to the analysis of inflation expectations. |
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