Invariant Inference and Efficient Computation in the Static Factor Model

CAMA Working Paper 32/2013

29 Pages Posted: 8 Jun 2013

See all articles by Joshua C. C. Chan

Joshua C. C. Chan

University of Technology Sydney (UTS) - UTS Business School; Purdue University

Roberto Leon-Gonzalez

National Graduate Institute for Policy Studies (GRIPS)

Rodney W. Strachan

University of Queensland - School of Economics

Date Written: June 1, 2013

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.

Suggested Citation

Chan, Joshua C. C. and Leon-Gonzalez, Roberto and Strachan, Rodney W., Invariant Inference and Efficient Computation in the Static Factor Model (June 1, 2013). CAMA Working Paper 32/2013, Available at SSRN: https://ssrn.com/abstract=2275707 or http://dx.doi.org/10.2139/ssrn.2275707

Joshua C. C. Chan (Contact Author)

University of Technology Sydney (UTS) - UTS Business School ( email )

Sydney
Australia

Purdue University

West Lafayette, IN 47907-1310
United States

Roberto Leon-Gonzalez

National Graduate Institute for Policy Studies (GRIPS) ( email )

7-22-1 Roppongi, Minato-Ku
Tokyo 106-8677, Tokyo 106-8677
Japan

Rodney W. Strachan

University of Queensland - School of Economics ( email )

Brisbane, QLD 4072
Australia

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