A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation

DOI10.1016/j.jeconom.2012.06.011zbMath1443.62465OpenAlexW2071489510MaRDI QIDQ528082

Anne Opschoor, Lennart F. Hoogerheide, Hermann K. Van Dijk

Publication date: 12 May 2017

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://papers.tinbergen.nl/12026.pdf

zbMATH Keywords

importance samplingKullback-Leibler divergenceMetropolis-Hastings algorithminstrumental variablespredictive likelihoodexpectation maximizationDCC GARCHmixture GARCHmixture of Student-\(t\) distributions

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items

Striated Metropolis-Hastings sampler for high-dimensional models ⋮ Bayesian Analysis of Instrumental Variable Models: Acceptance-Rejection within Direct Monte Carlo ⋮ Partially censored posterior for robust and efficient risk evaluation ⋮ Particle efficient importance sampling ⋮ A flexible predictive density combination for large financial data sets in regular and crisis periods ⋮ Sequentially adaptive Bayesian learning algorithms for inference and optimization ⋮ Importance sampling from posterior distributions using copula-like approximations ⋮ Forecast density combinations of dynamic models and data driven portfolio strategies ⋮ A class of adaptive importance sampling weighted EM algorithms for efficient and robust posterior and predictive simulation ⋮ Bayesian model averaging in the instrumental variable regression model ⋮ Naive method to test the convergence of simulation and its applications in the computation of bankruptcy probability

Uses Software

• BayesDA
• MitISEM

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