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Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit

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Author Info
David Ardia () (Department of Quantitative Economics)
Lennart F. Hoogerheide (Econometric Institute / Erasmus University)
Herman K. van Dijk (Econometric Institute / Erasmus University)

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Abstract

This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest via its kernel function. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.

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Publisher Info
Paper provided by Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland in its series DQE Working Papers with number 9.

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Length: 31 pages
Date of creation: 23 Jun 2008
Date of revision: 07 Jan 2009
Publication status: Published in Journal of Statistical Software, 2009, vol. 29, no.3, pp.1--31, URL http://www.jstatsof
Handle: RePEc:fri:dqewps:wp0009

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Related research
Keywords: adaptive mixture; Student-t distributions; importance sampling; independence chain Metropolis-Hastings algorithm; Bayesian; R software;

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Find related papers by JEL classification:
C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

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References listed on IDEAS
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.:
  1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November. [Downloadable!] (restricted)
  2. Juri Marcucci, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 9(4). [Downloadable!]
  3. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April. [Downloadable!] (restricted)
  4. Hoogerheide, Lennart F. & Kaashoek, Johan F. & van Dijk, Herman K., 2007. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," Journal of Econometrics, Elsevier, vol. 139(1), pages 154-180, July. [Downloadable!] (restricted)
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  5. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-34, April.
  6. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
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  7. Sylvia Frühwirth-Schnatter, 2001. "Fully Bayesian Analysis of Switching Gaussian State Space Models," Annals of the Institute of Statistical Mathematics, Springer, vol. 53(1), pages 31-49, March. [Downloadable!] (restricted)
  8. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January. [Downloadable!] (restricted)
  9. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394. [Downloadable!] (restricted)
  10. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-51, April.
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  11. Herman K. van Dijk & Lennart F. Hoogerheide & David Ardia, 2009. "Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit," Journal of Statistical Software, American Statistical Association, vol. 29(03), 01. [Downloadable!]
    Other versions:
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(explanations, 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.)

  1. David Ardia & Lennart F. Hoogerheide & Herman K. van Dijk, 2008. "Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: the R package AdMit," DQE Working Papers 9, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009. [Downloadable!]
    Other versions:
  2. David Ardia & Lennart F. Hoogerheide & Herman K. van Dijk, 2008. "AdMit: Adaptive Mixtures of Student-t Distributions," DQE Working Papers 10, Department of Quantitative Economics, University of Freiburg/Fribourg Switzerland, revised 07 Jan 2009. [Downloadable!]
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