Discipline of Business Analytics

Invariant Inference and Efficient Computation in the Static Factor Model

Professor Rodney Strachan, Australian National University

3rd May 2013  03:00 pm - Room 498 Merewether Building H04

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 in which no ordering is imposed, we use parameter expansions to obtain a specification with standard conditional posteriors. 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 show significant gains in computational efficiency. We apply our methods to a simple example using a macroeconomic
dataset.

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