Siddhartha Chib

Siddhartha Chib is an econometrician, statistician, and the Harry C. Hartkopf Professor of Econometrics and Statistics at Washington University in St. Louis. His work is primarily in Bayesian statistics, econometrics, and Markov chain Monte Carlo methods. Chib's research spans a wide range of topics in Bayesian statistics, with influential contributions to statistical modeling, computational methods, and Bayesian model comparison techniques.

Siddhartha Chib
Alma mater	University of California, Santa Barbara
Scientific career
Fields	Econometrics, statistics
Institutions	Washington University in St. Louis
Thesis	Some Contributions to Likelihood Based Prediction Methods  (1986)
Academic advisors	Sreenivasa Rao Jammalamadaka Thomas F. Cooley
Website	apps.olin.wustl.edu/faculty/chib/

Career

Albert and Chib (1993)^[1] pioneered a latent variable framework that greatly simplified estimation of binary and categorical response models and became a foundational method in Bayesian statistics. This framework was later extended to the multivariate setting in Chib and Greenberg (1998),^[2] which provided a flexible and coherent approach for modeling correlated discrete outcomes.

Chib and Greenberg (1995)^[3], a widely cited and influential paper, provides a unified and intuitive framework for understanding the Metropolis–Hastings algorithm and its extensions in high-dimensional settings. Drawing on the fundamental principles of global and local reversibility, the authors provide derivations of both the single-block and multiple-block forms of the algorithm, and guidance on implementation.

Chib (1995) ^[4] introduced a scalable and widely adopted solution to calculating the marginal likelihood for Bayesian model comparisons. The method relies on an identity that expresses the marginal likelihood as the product of the likelihood and the prior, divided by the posterior ordinate at a fixed point in the parameter space. Chib showed that this posterior ordinate can be factorized into a sequence of marginal and conditional posterior densities, each estimable from MCMC output. The approach was later extended by Chib and Jeliazkov (2001)^[5] to Metropolis-Hastings chains and by Basu and Chib (2003)^[6] to nonparametric Bayesian models based on Dirichlet process mixtures.

Carlin and Chib (1995)^[7] contains an influential Markov chain Monte Carlo method for model selection that involves jumps between model spaces. The approach has proved useful for comparing complex Bayesian models.

Kim, Shephard, and Chib (1998)^[8] developed a key method for estimating stochastic volatility models. Extensions to student-t models, covariates, high dimensional time series and models with leverage appear in Chib, Nardari and Shephard (2002),^[9] Chib, Nardari and Shephard (2006)^[10] and Omori et al. (2007).^[11]

Chib (1998)^[12] presents a reparameterization of a change point model as a unidirectional hidden Markov model (HMM) that simplifies estimation and inference and enables the use of efficient forward-filtering and backward-sampling techniques for HMMs developed in Chib (1996)^[13] and Albert and Chib (1993).^[14]

Chib has also worked on and developed original methods for Bayesian inference in Tobit censored responses,^[15] discretely observed diffusions,^[16] univariate and multivariate ARMA processes,^[17][18] multivariate count responses,^[19] causal inference,^[20][21] hierarchical models of longitudinal data,^[22] nonparametric regression,^[23][24][25] and tailored randomized block MCMC methods for complex structural models.^[26]

Chib, Shin, and Simoni (2018, 2022)^[27][28] consider Bayesian inference in models that do not specify a parametric or non-parametric data generating process.

Biography

Chib received a bachelor's degree from St. Stephen’s College, Delhi, in 1979, an M.B.A. from the Indian Institute of Management, Ahmedabad, in 1982, and a Ph.D. in economics from the University of California, Santa Barbara, in 1986.^[29] His advisors were Sreenivasa Rao Jammalamadaka and Thomas F. Cooley.

Honors and awards

Chib is a fellow of the American Statistical Association (2001),^[30] an inaugural fellow of the International Society of Bayesian Analysis (2012),^[31] and a fellow of the Journal of Econometrics (1996).^[32]

Selected publications

• Albert, Jim; Chib, Siddhartha (1993). Bayesian Analysis of "Binary and Polychotomous Response Data". Journal of the American Statistical Association, 88(2), 669–679.
• Chib, Siddhartha; Greenberg, Edward (1995). "Understanding the Metropolis–Hastings Algorithm". American Statistician, 49(4), 327–335.
• Chib, Siddhartha (1995). "Marginal Likelihood from the Gibbs Output". Journal of the American Statistical Association, 90(4), 1313–1321.
• Carlin, Brad; Chib, Siddhartha (1995). "Bayesian Model Choice via Markov Chain Monte Carlo Methods". Journal of the Royal Statistical Society, Series B, 57(3), 473–484.
• Chib, Siddhartha (1996). "Calculating Posterior Distributions and Modal Estimates in Markov Mixture Models". Journal of Econometrics, 75, 79–97.
• Chib, Siddhartha; Greenberg, Edward (1996). "Markov Chain Monte Carlo Simulation Methods in Econometrics". Econometric Theory. 12 (3): 409–431. doi:10.1017/S0266466600006794. JSTOR 3532527.
• Kim, Sangjoon; Shephard, Neil; Chib, Siddhartha (1998). "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models", Review of Economic Studies, 65, 361–393.
• Chib, Siddhartha (1998). "Estimation and Comparison of Multiple Change Point Models". Journal of Econometrics, 86, 221-241.
• Chib, Siddhartha; Greenberg, Edward (1998). "Analysis of Multivariate Probit Models". Biometrika, 85, 347-361.
• Chib, Siddhartha; Jeliazkov, Ivan (2001). "Marginal Likelihood from the Metropolis-Hastings Output". Journal of the American Statistical Association, 96(1), 270-281.
• Eleriain, Ola; Chib, Siddhartha; Shephard, Neil (2001). "Likelihood Inference for Discretely Observed Nonlinear Diffusions". Econometrica. 69 (4): 959–993. doi:10.1111/1468-0262.00226. Archived from the original on 2020-10-26. Retrieved 2020-08-28.
• Chib, Siddhartha (2001). "Markov Chain Monte Carlo: Computation and Inference" (PDF). In Heckman, Jim; Leamer, Ed (eds.). Handbook of Econometrics, volume 5. Elsevier. pp. 3569–3649.
• Chib, Siddhartha; Nardari, Federico; Shephard, Neil (2002). "Markov Chain Monte Carlo Methods for Stochastic Volatility Models". Journal of Econometrics, 108, 281-316.
• Basu, Sanjib; Chib, Siddhartha (2003). "Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models". Journal of the American Statistical Association. 98 (461): 224–235. doi:10.1198/01621450338861947. JSTOR 30045209.
• Chib, Siddhartha; Jeliazkov, Ivan (2006). "Inference in Semiparametric Dynamic Models for Binary Longitudinal Data". Journal of the American Statistical Association. 101 (2): 685–700. doi:10.1198/016214505000000871. JSTOR 27590727.
• Chib, Siddhartha; Ergashev, Bakhodir (2009). "Analysis of Multifactor Affine Yield Curve Models" (PDF). Journal of the American Statistical Association. 104 (488): 1324–1337. doi:10.1198/jasa.2009.ap08029.
• Chib, Siddhartha; Ramamurthy, Srikanth (2010). "Tailored randomized block MCMC methods with application to DSGE models". Journal of Econometrics, 155, 19-38.
• Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2018). "Bayesian Estimation and Comparison of Moment Condition Models". Journal of the American Statistical Association, 113(4), 1656-1668.
• Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2022). "Bayesian Estimation and Comparison of Conditional Moment Models". Journal of the Royal Statistical Society, Series B, 84 (3), 740–764.