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Invariant Inference and Efficient Computation in the Static Factor Model

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Author Info

  • Joshua C.C. Chan
  • Roberto Leon-Gonzalez
  • Rodney W. Strachan

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.

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File URL: http://cama.crawford.anu.edu.au/pdf/working-papers/2013/322013.pdf
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Bibliographic Info

Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2013-32.

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Length: 29 pages
Date of creation: May 2013
Date of revision:
Handle: RePEc:een:camaaa:2013-32

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Cited by:
  1. Sylvia Kaufmann & Christian Schumacher, 2013. "Bayesian estimation of sparse dynamic factor models with order-independent identification," Working Papers 13.04, Swiss National Bank, Study Center Gerzensee.
  2. Bin Peng & Giovanni Forchini, 2014. "Consistent Estimation of Panel Data Models with a Multifactor Error Structure when the Cross Section Dimension is Large," Working Paper Series 20, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
  3. Christian AƟmann & Jens Boysen-Hogrefe & Markus Pape, 2014. "Bayesian Analysis of Dynamic Factor Models: An Ex-Post Approach towards the Rotation Problem," Kiel Working Papers 1902, Kiel Institute for the World Economy.
  4. KiHoon Jimmy Hong & Bin Peng & Xiaohui Zhang, 2014. "Capturing the Impact of Latent Industry-Wide Shocks with Dynamic Panel Model," Research Paper Series 347, Quantitative Finance Research Centre, University of Technology, Sydney.

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