IDEAS home Printed from https://ideas.repec.org/p/ins/quaeco/qf0405.html
   My bibliography  Save this paper

Local robustness measures for posterior summaries

Author

Listed:
  • Passarin Katia

    (Università della Svizzera Italiana, Istituto di Finanza, Facoltà di Economia)

Abstract

This paper deals with measures of local robustness for particular Bayesian quantities, i.e. posterior summaries. We build a framework where any Bayesian quantity can be seen as a posterior functional and its sensitivity to all inputs is checked. First, we use the Gateaux derivatives to measure the impact on posterior summaries of perturbations of prior or sampling models, giving some general expressions. Such quantities capture both a ’data effect’ and a ’model effect’ on the functional. Secondly, we check the sensitivity to one observation in the sample, once a particular combination of prior/sampling models has been chosen. Moreover, we propose a new estimator of the Bayes factor for practical implementation. Finally, illustrative examples on sensitivity analysis are provided and discussed.

Suggested Citation

  • Passarin Katia, 2004. "Local robustness measures for posterior summaries," Economics and Quantitative Methods qf0405, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0405
    as

    Download full text from publisher

    File URL: https://www.eco.uninsubria.it/RePEc/pdf/QF2004_10.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Peña, Daniel & Zamar, Ruben, 1997. "A simple diagnostic tool for local prior sensitivity," Statistics & Probability Letters, Elsevier, vol. 36(2), pages 205-212, December.
    2. Sanjib Basu, 1999. "Posterior Sensitivity to the Sampling Distribution and the Prior: More than One Observation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 51(3), pages 499-513, September.
    3. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    4. Sivaganesan, Siva, 1999. "A likelihood based robust Bayesian summary," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 5-12, May.
    5. Mira Antonietta & Nicholls Geoff, 2001. "Bridge estimation of the probability density at a point," Economics and Quantitative Methods qf0105, Department of Economics, University of Insubria.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    2. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    3. Kakamu, Kazuhiko & Yunoue, Hideo & Kuramoto, Takashi, 2014. "Spatial patterns of flypaper effects for local expenditure by policy objective in Japan: A Bayesian approach," Economic Modelling, Elsevier, vol. 37(C), pages 500-506.
    4. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.
    5. Gary Bolton & Duncan Fong & Paul Mosquin, 2003. "Bayes Factors with an Application to Experimental Economics," Experimental Economics, Springer;Economic Science Association, vol. 6(3), pages 311-325, November.
    6. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate stochastic volatility with co-heteroscedasticity," CAMA Working Papers 2018-52, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Jouchi Nakajima & Yasuhiro Omori, 2007. "Leverage, Heavy-Tails and Correlated Jumps in Stochastic Volatility Models (Revised in January 2008; Published in "Computational Statistics and Data Analysis", 53-6, 2335-2353. April 2009. )," CARF F-Series CARF-F-107, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    8. Mike K. P. So & C. Y. Choi, 2009. "A threshold factor multivariate stochastic volatility model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(8), pages 712-735.
    9. Moeltner, Klaus, 2019. "Bayesian nonlinear meta regression for benefit transfer," Journal of Environmental Economics and Management, Elsevier, vol. 93(C), pages 44-62.
    10. Will Penny & Biswa Sengupta, 2016. "Annealed Importance Sampling for Neural Mass Models," PLOS Computational Biology, Public Library of Science, vol. 12(3), pages 1-25, March.
    11. Asai, Manabu, 2009. "Bayesian analysis of stochastic volatility models with mixture-of-normal distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2579-2596.
    12. Nakajima, Jouchi & Omori, Yasuhiro, 2012. "Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student’s t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3690-3704.
    13. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    14. Malley, Jim & Woitek, Ulrich, 2010. "Technology shocks and aggregate fluctuations in an estimated hybrid RBC model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1214-1232, July.
    15. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    16. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    17. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
    18. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    19. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
    20. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ins:quaeco:qf0405. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Segreteria Dipartimento (email available below). General contact details of provider: https://edirc.repec.org/data/feinsit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.