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Bayesian Inference for a 1-Factor Copula Model

Author

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  • Ban Kheng Tan
  • Anastasios Panagiotelis
  • George Athanasopoulos

Abstract

We develop efficient Bayesian inference for the 1-factor copula model with two significant contributions over classical inference. First, our approach leads to straightforward inference on the latent factor since iterates of the latent factor are generated as a by-product in the proposed Markov chain Monte Carlo algorithm. In contrast, there is no known classical approach for inference on the latents. Second, by developing a reversible jump Markov chain Monte Carlo scheme, we are able to select or average over factor copula specifications that are constructed from a large set of candidate parametric bivariate copula building blocks. Our approach can accommodate margins that are discrete, continuous or a combination of both. Through extensive simulations multiple schemes are compared on the basis of computational and Monte Carlo efficiency. The preferred schemes provide reliable inference on all parameters including the latent factor and model space. The potential of the proposed methodology is highlighted in an empirical study of ten binary variables measuring the multidimensional nature of poverty collected for 11463 East Timorese households. We construct a poverty index using estimates of the latent factor. Compared to a classical analysis, our method yields better out-of-sample fit and uncovers a variety of flexible relationships between the latent measure and observed variables by averaging over a diverse set of copulas.

Suggested Citation

  • Ban Kheng Tan & Anastasios Panagiotelis & George Athanasopoulos, 2017. "Bayesian Inference for a 1-Factor Copula Model," Monash Econometrics and Business Statistics Working Papers 6/17, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2017-6
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    File URL: http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp06-17.pdf
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    References listed on IDEAS

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    1. David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    2. Sabina Alkire, Maria Emma Santos, 2010. "Acute Multidimensional Poverty: A New Index for Developing Countries," OPHI Working Papers 38, Queen Elizabeth House, University of Oxford.
    3. Olivier Jean Blanchard & Stanley Fischer, 1989. "Editorial in "NBER Macroeconomics Annual 1989, Volume 4"," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 1-8, National Bureau of Economic Research, Inc.
    4. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    5. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
    6. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    7. Aristidis Nikoloulopoulos & Harry Joe, 2015. "Factor Copula Models for Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 126-150, March.
    8. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    9. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    10. Krupskii, Pavel & Joe, Harry, 2013. "Factor copula models for multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 85-101.
    11. Rippin, Nicole, 2011. "A response to the weaknesses of the Multidimensional Poverty Index (MPI): the correlation Sensitive Poverty Index (CSPI)," Briefing Papers 19/2011, German Institute of Development and Sustainability (IDOS).
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    Cited by:

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    2. Nguyen, Hoang & Ausín, M. Concepción & Galeano, Pedro, 2020. "Variational inference for high dimensional structured factor copulas," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).

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    More about this item

    Keywords

    model averaging; reversible jump MCMC; vine copulas; dimension reduction; multidimensional poverty index.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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