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Siddhartha Chib

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Siddhartha Chib
Alma materUniversity of California, Santa Barbara
Scientific career
FieldsEconometrics, Statistics
InstitutionsWashington University in St. Louis
Thesis Some Contributions to Likelihood Based Prediction Methods  (1986)
Academic advisorsSreenivasa Rao Jammalamadaka
Thomas F. Cooley
Websiteapps.olin.wustl.edu/faculty/chib/

Siddhartha Chib is an econometrician and statistician, 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.

In the seminal Albert and Chib (1993),[1] he introduced a latent variable framework that greatly simplified Bayesian estimation in binary and categorical response models and became a foundational method in the field. 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.

In another landmark paper, Chib and Greenberg (1995)[3] elucidated the theoretical foundations and implementation of the Metropolis–Hastings algorithm. This paper is one of the most highly cited and pedagogically influential expositions of the method. The paper presents a derivation of both the single-block and multiple-block versions of the algorithm from the principles of global and local reversibility, providing a unified and intuitive framework for understanding Metropolis-Hastings sampling and its extensions in high-dimensional settings.

In Chib (1995) [4] he made a major contribution to the field of Bayesian model comparisons, introducing a general method for computing marginal likelihoods directly from Gibbs sampler output, based on an identity that expresses the marginal likelihood as the product of the likelihood and 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 of which can be estimated from MCMC output. The method has been widely adopted in both theoretical and applied Bayesian work. It 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.

In another influential contribution, Carlin and Chib (1995)[7] introduced a Markov chain Monte Carlo method for model selection that involves jumps between model spaces, an approach that has since become widely adopted for comparing complex Bayesian models.

In Kim, Shephard, and Chib (1998),[8] Chib made a major contribution to the development of simulation-based likelihood methods for stochastic volatility models. The method is now widely used in empirical finance and macroeconomics. Extensions of this method to student-t models, covariates, high dimensional time series and models with leverage were developed in Chib, Nardari and Shephard (2002),[9] Chib, Nardari and Shephard (2006)[10] and Omori et al. (2007).[11]

In Chib (1998),[12] he made a significant contribution to the analysis of structural breaks in time series by developing a method for estimating multiple change-point models through a reparameterization as a unidirectional hidden Markov model (HMM). This structure 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 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]

In recent work, Chib, Shin, and Simoni (2018),[27] he has developed a novel Bayesian approach for analyzing and comparing models defined by unconditional moment restrictions, an important class of models that avoid distributional assumptions. Methods for likelihood construction and inference when the model is defined by conditional moment conditions are developed in Chib, Shin, and Simoni (2022).[28] These papers represent a broadening of Bayesian inference to models that do not specify a parametric or non-parametric data generating process, but still admit efficient and coherent Bayesian analysis.

Biography

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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

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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

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References

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  1. ^ Albert, Jim; Chib, Siddhartha (1993). "Bayesian Analysis of Binary and Polychotomous Response Data". Journal of the American Statistical Association. 88 (422): 669–679. doi:10.1080/01621459.1993.10476321. JSTOR 2290350.
  2. ^ Chib, Siddhartha; Greenberg, Edward (1998). "Analysis of multivariate probit models". Biometrika. 85 (2): 347–361. CiteSeerX 10.1.1.198.8541. doi:10.1093/biomet/85.2.347. Archived from the original on 2019-03-21. Retrieved 2020-04-24 – via Oxford Academic.
  3. ^ Chib, Siddhartha; Greenberg, Edward (1995). "Understanding the Metropolis Hastings Algorithm" (PDF). American Statistician. 49 (4): 327–335. doi:10.1080/00031305.1995.10476177. Archived (PDF) from the original on 2019-11-13. Retrieved 2020-04-24.
  4. ^ Chib, Siddhartha (1995). "Marginal Likelihood from the Gibbs Output" (PDF). Journal of the American Statistical Association. 90 (432): 1313–1321. doi:10.1080/01621459.1995.10476635. Archived (PDF) from the original on 2019-07-15. Retrieved 2020-04-30.
  5. ^ Chib, Siddhartha; Jeliazkov, Ivan (2001). "Marginal Likelihood from the Metropolis-Hastings Output" (PDF). Journal of the American Statistical Association. 96 (1): 270–281. CiteSeerX 10.1.1.722.3656. doi:10.1198/016214501750332848. S2CID 44046690. Archived (PDF) from the original on 2019-07-15. Retrieved 2020-04-30.
  6. ^ 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. CiteSeerX 10.1.1.722.3907. doi:10.1198/01621450338861947. JSTOR 30045209. S2CID 17496626.
  7. ^ Carlin, Bradley; Chib, Siddhartha (1995). "Bayesian Model Choice via Markov Chain Monte Carlo" (PDF). Journal of the Royal Statistical Society, Series B. 57: 473–484. doi:10.1111/j.2517-6161.1995.tb02042.x.
  8. ^ Kim, Sangjoon; Shephard, Neil; Chib, Siddhartha (1998). "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models" (PDF). Review of Economic Studies. 65 (3): 361–393. doi:10.1111/1467-937X.00050. S2CID 18381818. Archived (PDF) from the original on 2017-08-11. Retrieved 2020-09-29.
  9. ^ Chib, Siddhartha; Nardari, Federico; Shephard, Neil (2002). "Markov chain Monte Carlo methods for stochastic volatility models". Journal of Econometrics. 108 (2): 281–316. doi:10.1016/S0304-4076(01)00139-6.
  10. ^ Chib, Siddhartha; Nardari, Federico (2006). "Analysis of high dimensional multivariate stochastic volatility models". Journal of Econometrics. 134 (2): 341–371. doi:10.1016/j.jeconom.2005.06.026.
  11. ^ Omori, Yasuhiro; Chib, Siddhartha; Shephard, Neil; Nakajima, Jouchi (2007). "Stochastic volatility with leverage: Fast and efficient likelihood inference". Journal of Econometrics. 140 (2): 425–449. doi:10.1016/j.jeconom.2006.07.008.
  12. ^ Chib, Siddhartha (1998). "Estimation and comparison of multiple change-point models" (PDF). Journal of Econometrics. 86 (2): 221–241. doi:10.1016/S0304-4076(97)00115-2.
  13. ^ Chib, Siddhartha (1996). "Calculating Posterior Distributions and Modal Estimates in Markov Mixture Models" (PDF). Journal of Econometrics. 75: 79–97. CiteSeerX 10.1.1.119.4348. doi:10.1016/0304-4076(95)01770-4.
  14. ^ Albert, Jim; Chib, Siddhartha (1993). "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts". Journal of Business and Economic Statistics. 11 (1): 1–15. doi:10.2307/1391303. JSTOR 1391303.
  15. ^ Chib, Siddhartha (1992). "Bayes inference in the Tobit censored regression model". Journal of Econometrics. 51 (1–2): 79–99. doi:10.1016/0304-4076(92)90030-U.
  16. ^ 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.
  17. ^ Chib, Siddhartha; Greenberg, Edward (1994). "Bayes inference in regression models with ARMA (p, q) errors". Journal of Econometrics. 64 (1–2): 183–206. doi:10.1016/0304-4076(94)90063-9. Archived from the original on 2020-07-24. Retrieved 2020-08-22.
  18. ^ Chib, Siddhartha; Greenberg, Edward (1995). "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models". Journal of Econometrics. 68 (2): 339–360. doi:10.1016/0304-4076(94)01653-H.
  19. ^ Chib, Siddhartha; Winkelmann, Rainer (2001). "Markov Chain Monte Carlo Analysis of Correlated Count Data" (PDF). Journal of Business and Economic Statistics. 19 (4): 428–435. doi:10.1198/07350010152596673.
  20. ^ Chib, Siddhartha (2007). "Analysis of treatment response data without the joint distribution of potential outcomes". Journal of Econometrics. 140 (2): 401–412. doi:10.1016/j.jeconom.2006.07.009.
  21. ^ Chib, Siddhartha; Greenberg, Edward; Simoni, Anna (2022). "Nonparametric Bayes Analysis of the Sharp and Fuzzy Regression Discontinuity Designs" (PDF). Econometric Theory. 39 (3): 481–533. doi:10.1017/S0266466622000019. S2CID 28242828.
  22. ^ Chib, Siddhartha; Carlin, Bradley (1998). "On MCMC sampling in hierarchical longitudinal models". Statistics and Computing. 9: 17–26. doi:10.1023/A:1008853808677. S2CID 15267509.
  23. ^ 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. S2CID 10169747.
  24. ^ Chib, Siddhartha; Greenberg, Edward (2010). "Additive cubic spline regression with Dirichlet process mixture errors". Journal of Econometrics. 156 (2): 322–336. doi:10.1016/j.jeconom.2009.11.002.
  25. ^ Chib, Siddhartha; Greenberg, Edward (2013). "On conditional variance estimation in nonparametric regression" (PDF). Statistics and Computing. 23: 261–270.
  26. ^ Chib, Siddhartha; Ramamurthy, Srikanth (2010). "Tailored randomized block MCMC methods with application to DSGE models". Journal of Econometrics. 155 (1): 19–38. doi:10.1016/j.jeconom.2009.09.013.
  27. ^ Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2018). "Bayesian Analysis and Comparison of Moment Condition Models" (PDF). Journal of the American Statistical Association. 113 (4): 1656–1668. arXiv:1606.02931. doi:10.1080/01621459.2017.1358172. S2CID 56211599.
  28. ^ Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2022). "Bayesian Estimation and Comparison of Conditional Moment Models" (PDF). Journal of the Royal Statistical Society, Series B (Statistical Methodology). 84 (3): 740–764. arXiv:2110.13531. doi:10.1111/rssb.12484. S2CID 209455901.
  29. ^ "Faculty". Washington University in St. Louis. Archived from the original on 23 April 2020. Retrieved 24 April 2020.
  30. ^ "ASA Fellows List". American Statistical Association. Archived from the original on 21 May 2020. Retrieved 24 April 2020.
  31. ^ "ISBA Fellows". The International Society for Bayesian Analysis. Archived from the original on 9 February 2018. Retrieved 24 April 2020.
  32. ^ "Journal of Econometrics Fellows". Journal of Econometrics. 78 (1): 131–133. January 1997. doi:10.1016/S0304-4076(97)80004-8.
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