Dynamic hierarchical factor models

Emanuel Moench, Serena Ng () and Simon Potter

No 412, Staff Reports from Federal Reserve Bank of New York

Abstract: 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. We 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.

Keywords: Econometric models; Economic forecasting; Economic indicators; Markov processes (search for similar items in EconPapers)
Date: 2009
New Economics Papers: this item is included in nep-ecm and nep-ets
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Journal Article: Dynamic Hierarchical Factor Model (2013)
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