Invariant Inference and Efficient Computation in the Static Factor Model

Joshua Chan, Roberto Leon-Gonzalez and Rodney W. Strachan

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: Factor models are used in a wide range of areas. Two issues with Bayesian versions of these models are a lack of invariance to ordering of the variables and computational inefficiency. This paper develops invariant and efficient Bayesian methods for estimating static factor models. This approach leads to inference on the number of factors that does not depend upon the ordering of the variables, and we provide arguments to explain this invariance. Beginning from identified parameters which have nonstandard forms, we use parameter expansions to obtain a specification with standard conditional posteriors. We show significant gains in computational efficiency. Identifying restrictions that are commonly employed result in interpretable factors or loadings and, using our approach, these can be imposed ex-post. This allows us to investigate several alternative identifying schemes without the need to respecify and resample the model. We apply our methods to a simple example using a macroeconomic dataset.

Pages: 29 pages
Date: 2013-05
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Invariant Inference and Efficient Computation in the Static Factor Model (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2013-32

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