MARKOV CHAIN MONTE CARLO METHODS: COMPUTATION AND INFERENCE

By Siddhartha Chib. 2001

470 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f9f39dae77662f7f23916b725a5bdde8ec9f6a01

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Citations in the corpus

Bayesian inference for multivariate copulas using pair-copula constructions. (2010, methodology): “Markov chain Monte Carlo (MCMC) methods (see e.g. Chib (2001) for a comprehensive overview) are necessary to approximate the posterior distribution of the joint parameter vector θ for the PCC specified in (3.2).”
Analysis of high dimensional multivariate stochastic volatility models (2006, methodology): “We utilize Markov chain Monte Carlo (MCMC) methods to develop a practical Bayesian estimation approach for this model; Chib (2001) provides an extensive review of these methods.”
Euro Area Inflation Persistence in an Estimated Nonlinear DSGE Model (2008, unknown): “Equations (3), (6)-(8), (10)-(11) describe aggregate economic dynamics.”
Stochastic volatility with leverage: Fast and efficient likelihood inference (2007, methodology): “is a consistent estimate of the conditional log-likelihood and can be used in the method of Chib (1995) to calculate the marginal likelihood. Likewise the sequence of W t, and its reflected version 2 W t 12 , can be used to check for model fit as these are approximately i.i.d. standard uniform if th”
Likelihood-based Analysis for Dynamic Factor Models (2008, methodology): “Reviews of MCMC algorithms are given by, amongst others, Gilks, Richardson, and Spiegelhalter (1996) and Chib (2001).”
Semiparametric Bayes analysis of longitudinal data treatment models (2002, methodology): “The model is then estimated by a tuned Markov chain Monte Carlo (MCMC) simulation method (Chib, 2001; Chib and Greenberg, 1995, 1996; Tiernay, 1994) based on the algorithm reported in Chib and Hamilton (2000).”
On Leverage in a Stochastic Volatility Model (2005, unknown): “I refer readers to Chib (2001) for a recent survey on MCMC in a general context.”
Multivariate Stochastic Volatility: A Review (2006, methodology): “The former can be assessed by correlograms, and the latter by using alternative priors (for further details, see Chib, 2001; Chib et al., 2005; Kim et al., 1998).”
Modeling Realized Covariances and Returns (2013, methodology): “Suppose A is the identity matrix and d1 = 1, then by equation (17), E(Σt|Σ1:t−1) = νSt−1 = Σt−1, which is a random walk in matrix form.”
Pair-Copula Constructions of Multivariate Copulas (2010, methodology): “Here parameters are estimated using Markov Chain Monte Carlo (MCMC) methods (see for example [9]) and they employ the Metropolis Hastings algorithm (see [28, 46]) for D-vines with pair-copulas to be chosen as a bivariate t-copula.”
Bayesian Poisson log-bilinear mortality projections (2005, methodology): “This changed in the early 1990’s with computer-intensive Markov Chain Monte Carlo (MCMC) simulation methods (see Chib (2001) for a summary and Gilks et al. (1996) for applications).”
Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy (2011, unknown): “The spirit of its specification is derived from Cogly and Sargent (2005). Benati and Mumtaz (2005) provide empirical results for the TVP-VAR model for the UK economy and Baumeister et al.”
Estimating Marginal Likelihoods for Mixture and Markov Switching Models Using Bridge Sampling Techniques (2004, unknown): “We reanalyze an example given in Chib (1996) which is based on data analyzed in Leroux and Puterman (1992).”
Regime-switching stochastic volatility: Evidence from the crude oil market (2009, background): “The reader is referred to Chib (2001) for a survey on MCMC.”
Forecasting recessions using the yield curve (2005, methodology): “Then, as shown in the appendix, 12See Geweke 1999 and Chib 2001 for excellent introductions to modern Bayesian computational techniques and references to earlier work on probit models that we implicitly draw on. we have a priori the conditional normal distribution with mean: eXT−1 + θ " V (eYT−1) V…”
State Dependence and Alternative Explanations for Consumer Inertia (2010, unknown): “Standard hybrid MCMC methods for hierarchical logit models (see the excellent survey on MCMC methods by Chib (2001)) are simply infeasible for data that include incomplete purchase histories.”
Bayesian inference on structural impulse response functions (2019, methodology): “31See Chib (2001, Ch. 10), Geweke (2010, Ch. 2), and Gelman et al. (2013, Ch. 6). 32Chib & Greenberg (1994) estimate univariate reduced-form Autoregressive Moving Average models by MCMC, but their algorithm is only effective in low-dimensional problems.”
Multivariate stochastic volatility (2009, methodology): “Chib et al. (2006) assumed that the variables ζjt = ln(1 + kjt), j ≤ p, are distributed as N (−0.”