Biography:Siddhartha Chib

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Siddhartha Chib
Alma materUniversity of California, Santa Barbara
Indian Institutes of Management
Delhi University
Known forFramework for understanding the Metropolis–Hastings algorithm and its extensions in high-dimensional settings
AwardsFellow of the American Statistical Association
Scientific career
FieldsEconometrics, statistics
InstitutionsWashington University in St. Louis
ThesisSome Contributions to Likelihood Based Prediction Methods (1986)
Academic advisorsSreenivasa Rao Jammalamadaka
Thomas F. Cooley
Websitesiddharthachib.org

Siddhartha Chib is an econometrician, statistician, and the Harry C. Hartkopf Professor of Econometrics and Statistics at the Olin Business School 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.

Career

Chib pioneered a latent variable framework in Albert and Chib (1993)[1], that greatly simplifies the Bayesian estimation of binary and categorical response models. It is a foundational method in Bayesian statistics. Along with the work in Chib and Greenberg (1998)[2], the Albert and Chib (1993) latent variable framework provides a unified approach in the Bayesian context for handling univariate and multivariate categorical outcomes.

Another widely cited and influential paper is Chib and Greenberg (1995),[3] which provides an intuitive framework for understanding the Metropolis–Hastings algorithm and its extensions in high-dimensional settings. Central contributions of this work are the included derivations of the single block and multiple block versions of the algorithm using the principles of global and local reversibility, the first such derivations, and the guidance on the choice of proposal distributions for efficient implementation of the algorithm in practice.

For the problem of comparing Bayesian models, Chib developed a method for calculating marginal likelihoods from the MCMC output in Chib (1995) [4] that has been shown to be applicable to parametric and nonparametric models, and to models estimated by the Gibbs or Metropolis-Hastings algorithm. It is also straightforward to implement. The method is based 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 developed an approach for estimating this ordinate from the MCMC output. For models estimated by the Metropolis-Hastings algorithm, a generalization is given in Chib and Jeliazkov (2001)[5]. Basu and Chib (2003)[6] further extend the method to nonparametric Dirichlet process mixture models.

Chib has also worked on a model jump approach for comparing Bayesian models. The idea, developed in Carlin and Chib (1995)[7], is to sample models and model-specific parameters by Markov chain Monte Carlo methods on a product of model spaces. The posterior distribution over models emerges from the frequency of visits to each model. This product-space approach has proved useful for comparing complex Bayesian models.

Chib has also written extensively on the problem of estimating stochastic volatility models in time series. The simple to implement and effficent method developed in Kim, Shephard, and Chib (1998)[8] is widely used. Extensions of the basic method to student-t models, covariates and multivariate stochastic volatility models are discussed in Chib, Nardari and Shephard (2002),[9] Chib, Nardari and Shephard (2006)[10] and Omori et al. (2007).[11]

Again, within the time series context, Chib (1998)[12] introduced a reparameterization of the 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]

In Chib, Shin, and Simoni (2018, 2022)[27][28] he has developed estimation and model comparison tools for conducting Bayesian inference in models that rely only on moment restrictions and do not specify a parametric or non-parametric data generating process. In this work, he has supplied finite sample computational methods and large sample Bernstein--von Mises and model consistency theory under both correct and mis-specified moment restrictions.

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

References

  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. https://www.jstor.org/stable/2290350. 
  2. ↑ Chib, Siddhartha; Greenberg, Edward (1998). "Analysis of multivariate probit models". Biometrika 85 (2): 347–361. doi:10.1093/biomet/85.2.347. https://academic.oup.com/biomet/article-abstract/85/2/347/298820. Retrieved 2020-04-24. 
  3. ↑ Chib, Siddhartha; Greenberg, Edward (1995). "Understanding the Metropolis Hastings Algorithm". American Statistician 49 (4): 327–335. doi:10.1080/00031305.1995.10476177. http://web1.sph.emory.edu/users/hwu30/teaching/statcomp/papers/chibGreenbergMH.pdf. Retrieved 2020-04-24. 
  4. ↑ Chib, Siddhartha (1995). "Marginal Likelihood from the Gibbs Output". Journal of the American Statistical Association 90 (432): 1313–1321. doi:10.1080/01621459.1995.10476635. https://siddharthachib.org/papers/chib95.pdf. Retrieved 2020-04-30. 
  5. ↑ Chib, Siddhartha; Jeliazkov, Ivan (2001). "Marginal Likelihood from the Metropolis-Hastings Output". Journal of the American Statistical Association 96 (1): 270–281. doi:10.1198/016214501750332848. https://siddharthachib.org/papers/chibJeliazkov2001.pdf. 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. doi:10.1198/01621450338861947. https://www.jstor.org/stable/30045209. 
  7. ↑ Carlin, Bradley; Chib, Siddhartha (1995). "Bayesian Model Choice via Markov Chain Monte Carlo". Journal of the Royal Statistical Society, Series B 57: 473–484. doi:10.1111/j.2517-6161.1995.tb02042.x. https://people.eecs.berkeley.edu/~jordan/sail/readings/archive/carlin-chib.pdf. 
  8. ↑ Kim, Sangjoon; Shephard, Neil; Chib, Siddhartha (1998). "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models". Review of Economic Studies 65 (3): 361–393. doi:10.1111/1467-937X.00050. http://www.nuff.ox.ac.uk/economics/papers/1996/w26/chib8.pdf. 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. https://doi.org/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. https://doi.org/10.1016/j.jeconom.2006.07.008. 
  12. ↑ Chib, Siddhartha (1998). "Estimation and comparison of multiple change-point models". Journal of Econometrics 86 (2): 221–241. doi:10.1016/S0304-4076(97)00115-2. https://siddharthachib.org/papers/chib98.pdf. 
  13. ↑ Chib, Siddhartha (1996). "Calculating Posterior Distributions and Modal Estimates in Markov Mixture Models". Journal of Econometrics 75: 79–97. doi:10.1016/0304-4076(95)01770-4. https://siddharthachib.org/papers/chib96.pdf. 
  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. https://www.jstor.org/stable/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. https://dx.doi.org/10.1016/0304-4076%2892%2990030-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. https://ora.ox.ac.uk/objects/uuid:32366322-5b0e-4f38-b18b-89c11fd44265. 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. https://www.sciencedirect.com/science/article/abs/pii/0304407694900639. 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. https://doi.org/10.1016/0304-4076(94)01653-H. 
  19. ↑ Chib, Siddhartha; Winkelmann, Rainer (2001). "Markov Chain Monte Carlo Analysis of Correlated Count Data". Journal of Business and Economic Statistics 19 (4): 428–435. doi:10.1198/07350010152596673. https://siddharthachib.org/papers/chibWinkelmann2001.pdf. 
  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. https://doi.org/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". Econometric Theory 39 (3): 481–533. doi:10.1017/S0266466622000019. https://siddharthachib.org/papers/cgsrdd2021.pdf. 
  22. ↑ Chib, Siddhartha; Carlin, Bradley (1998). "On MCMC sampling in hierarchical longitudinal models". Statistics and Computing 9: 17–26. doi:10.1023/A:1008853808677. https://link.springer.com/article/10.1023/A:1008853808677. 
  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. https://www.jstor.org/stable/27590727. 
  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. https://doi.org/10.1016/j.jeconom.2009.11.002. 
  25. ↑ Chib, Siddhartha; Greenberg, Edward (2013). "On conditional variance estimation in nonparametric regression". Statistics and Computing 23: 261–270. https://siddharthachib.org/papers/chibGreenberg2013.pdf. 
  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. https://doi.org/10.1016/j.jeconom.2009.09.013. 
  27. ↑ Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2018). "Bayesian Analysis and Comparison of Moment Condition Models". Journal of the American Statistical Association 113 (4): 1656–1668. doi:10.1080/01621459.2017.1358172. https://siddharthachib.org/papers/chibshinsimoni2018.pdf. 
  28. ↑ Chib, Siddhartha; Shin, Minchul; Simoni, Anna (2022). "Bayesian Estimation and Comparison of Conditional Moment Models". Journal of the Royal Statistical Society, Series B (Statistical Methodology) 84 (3): 740–764. doi:10.1111/rssb.12484. https://siddharthachib.org/papers/chibshinsimoni2022.pdf. 
  29. ↑ "Faculty". https://olin.wustl.edu/EN-US/Faculty-Research/Faculty/Pages/FacultyDetail.aspx?username=chib. 
  30. ↑ "ASA Fellows List". https://www.amstat.org/ASA/Your-Career/Awards/ASA-Fellows-list.aspx. 
  31. ↑ "ISBA Fellows". https://bayesian.org/membership/isba-fellows/. 
  32. ↑ "Journal of Econometrics Fellows". Journal of Econometrics 78 (1): 131–133. January 1997. doi:10.1016/S0304-4076(97)80004-8. https://reader.elsevier.com/reader/sd/pii/S0304407697800048?token=3CB52A7A74D8909CCDC2B54EF41AC2CE12E41293FCF4A3997F86AFF2009D14E2F2DEC7CB39D21C6F4774D8E1DF69AF02&originRegion=us-east-1&originCreation=20221213210022. 



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