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Bayesian Statistics from Methods to Models and Applications pp 75–90Cite as

Application of Interweaving in DLMs to an Exchange and Specialization Experiment

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 126))

Abstract

Markov chain Monte Carlo is often particularly challenging in dynamic models. In state space models, the data augmentation algorithm (Tanner and Hung Wong, J. Am. Stat. Assoc. 82(398):528–540, 1987) is a commonly used approach, e.g. (Carter and Kohn, Biometrika 81(3):541–553, 1994) and (Frühwirth-Schnatter, J. Time Ser. Anal. 15(2):183–202, 1994) in dynamic linear models. Using two data augmentations, Yu and Meng (J. Comput. Graph. Stat. 20(3): 531–570, 2011) introduces a method of “interweaving” between the two augmentations in order to construct an improved algorithm. Picking up on this, Simpson et al. (Interweaving Markov chain Monte Carlo strategies for efficient estimation of dynamic linear models, Working Paper, 2014) introduces several new augmentations for the dynamic linear model and builds interweaving algorithms based on these augmentations. In the context of a multivariate model using data from an economic experiment intended to study the disequilibrium dynamics of economic efficiency under a variety of conditions, we use these interweaving ideas and show how to implement them simply despite complications that arise because the model has latent states with a higher dimension than the data.

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Notes

  1. 1.

    For a more detailed description of the experimental design, see [4] especially, but also [16].

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Author information

Authors and Affiliations

  1. Department of Economics, Iowa State University, Ames, IA, USA

    Matthew Simpson

Authors
  1. Matthew Simpson
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Correspondence to Matthew Simpson .

Editor information

Editors and Affiliations

  1. Institute for Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna, Austria

    Sylvia Frühwirth-Schnatter

  2. Institute for Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna, Austria

    Angela Bitto

  3. Institute for Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna, Austria

    Gregor Kastner

  4. Institute for Statistics and Mathematics, WU Vienna University of Economics and Business, Vienna, Austria

    Alexandra Posekany

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Simpson, M. (2015). Application of Interweaving in DLMs to an Exchange and Specialization Experiment. In: Frühwirth-Schnatter, S., Bitto, A., Kastner, G., Posekany, A. (eds) Bayesian Statistics from Methods to Models and Applications. Springer Proceedings in Mathematics & Statistics, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-16238-6_7

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