LTM: Bayesian Analysis of Latent Threshold Dynamic Models
Jouchi Nakajima & Mike West
2022 update: link to additional software for LTMs:
An R package for LTMs created by Julio Trecenti, Fernando Tassinari and Daniel Falbel is available here on github with this documentation. This R package will be of interest to users of R. Note that this is an external link and this R package is not related to the Duke software below; neither Duke related researchers nor Duke University bear any responsibility nor assume any liability for any end-use of this software.

  • Publication: Journal of Business and Economic Statistics 31(2): 151-164, 2013 doi: 10.1080/07350015.2012.747847
  • Original technical report
  • Appendices to the paper. This supplementary document includes technical details of posterior computation via MCMC, further empirical evaluation of MCMC sampling in data analyses reported in the paper, and results of the second application to Japanese macroeconomic data. (Note: The supplement here was updated in December 2013 to correct two minor typos in the original appendix/supplement. The code was updated in one place as a result of one of these typos. We thank Kaoru Irie for pointing these out. The results reported in the original paper were not affected by these typos).
  • Code and data, including the Ox code for the MCMC analysis and both the US and Japanese economic data sets.

The software is made freely available to any interested user. The authors can provide no support nor assistance with implementations beyond the example in the code, nor extensions of the code for other purposes. The download has been tested to confirm all details are operational as described.

It is understood by the user that neither the authors nor Duke University bear any responsibility nor assume any liability for any end-use of this software. It is expected that appropriate credit/acknowledgment be given should the software be included as an element in other software development or in publications.

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