SIDDHARTHA CHIB

Research and Teaching

He teaches statistics and econometrics to students in the MBA, specialized MS, and doctoral programs.

In his research, which is available here, he has developed novel inferential approaches and methods for diverse problems, including the analysis of binary, categorical, and censored data, the Metropolis-Hastings algorithm, the computation of marginal likelihoods in parametric and non-parametric Bayesian models, and techniques for estimating and comparing complex models. His work has been widely used in statistics, economics, biostatistics, social sciences and the physical sciences.

Current research is focused on developing moment-based Bayesian nonparametric methods, Bayesian causal inference from moments, endogeneity testing in distribution-free regression, scalable estimation of discrete choice models with unknown choice sets, nonparametric slope factors for asset pricing, and consistent change-point detection in non-conjugate VAR models.

Sampling of Papers

• Binary and Polychotomous Response Data
  • Bayesian Analysis of Binary and Polychotomous Response Data, Albert and Chib (1993)
  • Bayes Inference in the Tobit Censored Regression Model, Chib (1992)
  • Bayesian Residual Analysis for Binary Response Regression Models, Albert and Chib (1995)
  • Analysis of Multivariate Probit Models, Chib and Greenberg (1998)
  • MCMC Methods for Fitting and Comparing Multinomial Response Models, Chib, Greenberg and Chen (1998)
  • Sequential Ordinal Modeling with Applications to Survival Data, Albert and Chib (2001)
• Understanding the Metropolis-Hastings algorithm
  • Understanding the Metropolis-Hastings Algorithm, Chib and Greenberg (1995)
  • Markov Chain Monte Carlo Simulation Methods in Econometrics, Chib and Greenberg (1996)
  • Markov Chain Monte Carlo Methods: Computation and Inference, Chib (2001)
  • Tailored Randomized-block MCMC Methods with Application to DSGE Models, Chib and Ramamurthy (2010)
  • Introduction to Simulation and MCMC Methods, Chib (2011)
• Marginal likelihood from the Gibbs and Metropolis-Hastings output
  • Marginal Likelihood From the Gibbs Output, Chib (1995)
  • Bayesian Model Choice via Markov Chain Monte Carlo Methods, Carlin and Chib (1995)
  • Marginal Likelihood from the Metropolis-Hastings Output, Chib and Jeliazkov (2001)
  • Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models, Basu and Chib (2003)
  • Accept-Reject Metropolis-Hastings Sampling and Marginal Likelihood Estimation, Chib and Jeliazkov (2005)
• Stochastic volatility
  • Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models, Kim, Shephard and Chib (1998)
  • Markov Chain Monte Carlo Methods for Stochastic Volatility Models, Chib, Nardari and Shephard (2002)
  • Analysis of High Dimensional Multivariate Stochastic Volatility Models, Chib, Nardari and Shephard (2006)
  • Stochastic Volatility with Leverage: Fast and Efficient Likelihood Inference, Omori, Chib, Nakajima and Shephard (2007)
  • Multivariate Stochastic Volatility, Chib, Omori and Asai (2009)
• Hidden Markov models
  • Bayesian Analysis via Gibbs Sampling of Autoregressive Time Series Subject to Markov Shifts, Albert and Chib (1993)
  • Calculating Posterior Distributions and Modal Estimates in Markov Mixture Models, Chib (1996)
• Muliple change point models
  • Estimation and Comparison of Multiple Change Point Models , Chib (1998)
  • Semiparametric Multivariate and Multiple Change-Point Modelling, Peluso, Chib and Mira (2018)
• Discretely observed nonlinear diffusions
  • Likelihood Inference for Discretely Observed Nonlinear Diffusions, Elerian, Chib and Shephard (2001)
  • Likelihood based inference for diffusion driven models, Chib, Pitt and Shephard (2004)
• AR, ARMA and SUR time-varying parameter models
  • Bayes Estimation of Regressions with Autoregressive Errors: A Gibbs Sampling Approach, Chib (1993)
  • Bayes Inference in Regression Models with ARMA (p,q) Errors, Chib and Greenberg (1994)
  • Hierarchical Analysis of SUR Models with Extensions to Correlated Serial Errors and Time Varying Parameter Models, Chib and Greenberg (1995)
• Multivariate and correlated count outcomes
  • Markov Chain Monte Carlo Analysis of Correlated Count Data, Chib and Winkelmann (2001)
• Panel, longitudinal and hierarchical models
  • Bayesian Tests and Model Diagnostics in Conditionally Independent Hierarchical Models Albert and Chib (1997)
  • Posterior Simulation and Bayes Factors in Panel Count Data Models, Chib, Greenberg and Winkelmann (1998)
  • On MCMC Sampling in Hierarchical Longitudinal Models, Chib and Carlin (1999)
  • Markov Chain Monte Carlo and Models of Consideration Set and Parameter Heterogeneity, Chiang, Chib and Narasimhan (1999)
  • Model of Brand Choice with a No-Purchase Option Calibrated to Scanner Panel Data, Chib, Seetharaman and Strijnev (2004)
  • Inference in Semiparametric Dynamic Models for Binary Longitudinal Data, Chib and Jeliazkov (2006)
  • Panel Data Modeling and Inference: A Bayesian Primer, Chib (2008)
• Causal inference in cross-sectional and longitudinal settings
  • Bayesian Analysis of Cross Section and Clustered Data Treatment Models, Chib and Hamilton (2000)
  • Semiparametric Bayes Analysis of Longitudinal Data Treatment Models, Chib and Hamilton (2002)
  • Analysis of Treatment Response Data Without the Joint Distribution of Potential Outcomes, Chib (2007)
  • Semiparametric Modeling and Estimation of Instrumental Variable Models, Chib and Greenberg (2007)
  • Estimation of Semiparametric Models in the Presence of Endogeneity and Sample Selection, Chib, Greenberg and Jeliazkov (2009)
  • Modeling and Calculating the Effect of Treatment at Baseline from Panel Outcomes, Chib and Jacobi (2007)
  • Analysis of Treatment Response Data from Eligibility Designs, Chib and Jacobi (2008)
  • Bayesian Fuzzy Regression Discontinuity Analysis and Returns to Compulsory Schooling, Chib and Jacobi (2016)
  • Nonparametric Bayes Analysis of the Sharp and Fuzzy Regression Discontinuity Designs, Chib, Greenberg and Simoni (2023)
• Dynamic stochastic general equilibrium models
  • Tailored Randomized-block MCMC Methods with Application to DSGE Models, Chib and Ramamurthy (2010)
  • DSGE Models with Student-t Errors, Chib and Ramamurthy (2014)
• Dirchlet process mixtures and splines
  • Bayes Prediction Density and Regression Estimation: A Semi Parametric Approach, Chib, Jammalamadaka and Tiwari (1988)
  • Semiparametric Bayes Analysis of Longitudinal Data Treatment Models, Chib and Hamilton (2002)
  • Marginal Likelihood and Bayes Factors for Dirichlet Process Mixture Models, Basu and Chib (2003)
  • Additive Cubic Spline Regression with Dirichlet Process Mixture Errors, Chib and Greenberg (2010)
  • On Conditional Variance Estimation in Nonparametric Regression, Chib and Greenberg (2013)
• Term-structure and asset pricing models
  • Analysis of Multi-Factor Affine Yield Curve Models, Chib and Ergashev (2009)
  • Change Points in Affine Arbitrage-free Term Structure Models, Chib and Kang (2013)
  • Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework, Chib and Zeng (2020)
  • On Comparing Asset Pricing Models, Chib, Zeng and Zhao (2020)
  • Winners from Winners: A Tale of Risk Factors, Chib, Zhao, Zhou (2022)
• Moment condition models
  • Bayesian Estimation and Comparison of Moment Condition Models, Chib, Shin and Simoni (2018)
  • Bayesian Estimation and Comparison of Conditional Moment Models, Chib, Shin and Simoni (2022)