On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
From MaRDI portal
Publication:528088
DOI10.1016/j.jeconom.2012.06.004zbMath1443.62499OpenAlexW2031294093MaRDI QIDQ528088
Ralph dos Santos Silva, Michael K. Pitt, Robert Kohn, Paolo E. Giordani
Publication date: 12 May 2017
Published in: Journal of Econometrics (Search for Journal in Brave)
Full work available at URL: http://www.sciencedirect.com/science/article/pii/S0304407612001510
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Monte Carlo methods (65C05)
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Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The pseudo-marginal approach for efficient Monte Carlo computations
- A unified approach to nonlinearity, structural change, and outliers
- A nonasymptotic theorem for unnormalized Feynman-Kac particle models
- Particle filters for continuous likelihood evaluation and maximisation
- Particle learning and smoothing
- Inference in hidden Markov models.
- Sequential Monte Carlo Methods in Practice
- BAYESIAN INFERENCE BASED ONLY ON SIMULATED LIKELIHOOD: PARTICLE FILTER ANALYSIS OF DYNAMIC ECONOMIC MODELS
- Sequential Monte Carlo Samplers
- Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
- A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
- Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
- Multivariate Stochastic Variance Models
- Filtering via Simulation: Auxiliary Particle Filters
- Bayesian Inference in Econometric Models Using Monte Carlo Integration
- Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models
- A Survey of Sequential Monte Carlo Methods for Economics and Finance
- Estimating Macroeconomic Models: A Likelihood Approach
- Likelihood-Based Estimation of Latent Generalized ARCH Structures
- Contemporary Bayesian Econometrics and Statistics
- Monte Carlo strategies in scientific computing
- Handbook of econometrics. Vol. 4