Bayesian Analysis of Dynamic Factor Models: An Ex-Post Approach towards the Rotation Problem
AbstractDue to their indeterminacies, static and dynamic factor models require identifying assumptions to guarantee uniqueness of the parameter estimates. The indeterminacy of the parameter estimates with respect to orthogonal transformations is known as the rotation problem. The typical strategy in Bayesian factor analysis to solve the rotation problem is to introduce ex-ante constraints on certain model parameters via degenerate and truncated prior distributions. This strategy, however, results in posterior distributions whose shapes depend on the ordering of the variables in the data set. We propose an alternative approach where the rotation problem is solved ex-post using Procrustean postprocessing. The resulting order invariance of the posterior estimates is illustrated in a simulation study and an empirical application using a well-known data set containing 120 macroeconomic time series. Favorable properties of the ex-post approach with respect to convergence, statistical and numerical accuracy are revealed
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1902.
Length: 29 pages
Date of creation: Jan 2014
Date of revision:
Bayesian Estimation; Factor Models; Multimodality; Rotation Problem; Ordering Problem; Orthogonal Transformation;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Robert Lissitz & Peter Schönemann & James Lingoes, 1976. "A solution to the weighted procrustes problem in which the transformation is in agreement with the loss function," Psychometrika, Springer, vol. 41(4), pages 547-550, December.
- Grn, Bettina & Leisch, Friedrich, 2009. "Dealing with label switching in mixture models under genuine multimodality," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 851-861, May.
- Carvalho, Carlos M. & Chang, Jeffrey & Lucas, Joseph E. & Nevins, Joseph R. & Wang, Quanli & West, Mike, 2008. "High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1438-1456.
- Ben Bernanke & Jean Boivin & Piotr S. Eliasz, 2005.
"Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach,"
The Quarterly Journal of Economics,
MIT Press, vol. 120(1), pages 387-422, January.
- Tom Doan, . "RATS programs to replicate Bernanke, Boivin, Eliasz FAVAR paper," Statistical Software Components RTZ00012, Boston College Department of Economics.
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach," Finance and Economics Discussion Series 2004-03, Board of Governors of the Federal Reserve System (U.S.).
- Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
- Donald Rubin & Dorothy Thayer, 1982. "EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 47(1), pages 69-76, March.
- Shawn Ni & Dongchu Sun, 2005. "Bayesian Estimates for Vector Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 105-117, January.
- John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
- Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
- John Geweke & Guofu Zhou, 1996.
"Measuring the Pricing Error of the Arbitrage Pricing Theory,"
CEMA Working Papers
276, China Economics and Management Academy, Central University of Finance and Economics.
- Geweke, John & Zhou, Guofu, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 557-87.
- John Geweke & Guofu Zhou, 1995. "Measuring the pricing error of the arbitrage pricing theory," Staff Report 189, Federal Reserve Bank of Minneapolis.
- P. Bentler & Jeffrey Tanaka, 1983. "Problems with EM algorithms for ML factor analysis," Psychometrika, Springer, vol. 48(2), pages 247-251, June.
- Martin Koschat & Deborah Swayne, 1991. "A weighted procrustes criterion," Psychometrika, Springer, vol. 56(2), pages 229-239, June.
- Joshua C.C. Chan & Roberto Leon-Gonzalez & Rodney W. Strachan, 2013. "Invariant Inference and Efficient Computation in the Static Factor Model," CAMA Working Papers 2013-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Aguilar, Omar & West, Mike, 2000. "Bayesian Dynamic Factor Models and Portfolio Allocation," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(3), pages 338-57, July.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dieter Stribny).
If references are entirely missing, you can add them using this form.