About

Professor Joshua Chan's current research focuses on high-dimensional Bayesian time series and state space models applied to empirical macroeconomic analysis, forecasting, and real-time measurement. His work encompasses large Bayesian vector autoregressions (VARs) with innovations such as shrinkage priors, flexible covariance structures, stochastic volatility, and time variation. He also develops high-dimensional state space models that enable efficient Bayesian estimation in the presence of missing or mixed-frequency data, emphasizing scalable computation techniques. Additionally, Professor Chan investigates trend inflation models through flexible unobserved components models and related extensions, contributing to the understanding and measurement of inflation dynamics. His research outputs include MATLAB code and datasets that support reproducibility and practical application of his methodologies. Professor Chan has coauthored books on statistical modeling and computation, as well as Bayesian econometric methods, reflecting his expertise in both theoretical and applied econometrics. His extensive publication record in leading journals demonstrates his significant contributions to the fields of Bayesian econometrics, macroeconometrics, and time series analysis.

Research topics

• Computer Science
• Statistics
• Mathematics
• Applied mathematics
• Econometrics
• Algorithm

Selected publications

• Bayesian Regularized U-MIDAS under Extreme Frequency Mismatch

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Common Statistical Models

  Springer texts in statistics · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Shrinkage and Regularization

  Springer texts in statistics · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Conditional forecasts in large Bayesian VARs with multiple equality and inequality constraints

  Journal of Economic Dynamics and Control · 2025-02-05 · 1 citations

  articleOpen access1st author

  Conditional forecasts, i.e. projections of a set of variables of interest on the future paths of some other variables, are used routinely by empirical macroeconomists in a number of applied settings. In spite of this, the existing algorithms used to generate conditional forecasts tend to be very computationally intensive, especially when working with large Vector Autoregressions or when multiple linear equality and inequality constraints are imposed at once. We introduce a novel precision-based sampler that is fast, scales well, and yields conditional forecasts from linear equality and inequality constraints. We show in a simulation study that the proposed method produces forecasts that are identical to those from the existing algorithms but in a fraction of the time. We then illustrate the performance of our method in a large Bayesian Vector Autoregression. Within this setting, we first highlight how we can simultaneously impose a mix of linear equality and inequality constraints on the future trajectories of several key US macroeconomic indicators over a forecast horizon spanning multiple years. Next, we test the benefits of using inequality constraints in an out-of-sample exercise spanning the period between 1995Q1 and 2022Q3 and find that imposing these constraints on the future path of Real GDP leads to significant improvement in point and density forecasts of the large BVAR model.

  PublisherDOI

• Large Structural VARs with Multiple Sign and Ranking Restrictions

  ArXiv.org · 2025-03-26 · 1 citations

  preprintOpen access1st authorCorresponding

  Large VARs are increasingly used in structural analysis as a unified framework to study the impacts of multiple structural shocks simultaneously. However, the concurrent identification of multiple shocks using sign and ranking restrictions poses significant practical challenges to the point where existing algorithms cannot be used with such large VARs. To address this, we introduce a new numerically efficient algorithm that facilitates the estimation of impulse responses and related measures in large structural VARs identified with a large number of structural restrictions on impulse responses. The methodology is illustrated using a 35-variable VAR with over 100 sign and ranking restrictions to identify 8 structural shocks.

  PublisherOA PDFDOI

• Likelihood

  Springer texts in statistics · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Bayesian model comparison for large Bayesian VARs after the COVID-19 pandemic

  Journal of Econometrics · 2025-08-01

  article1st author
  PublisherDOI

• Large Bayesian matrix autoregressions

  Journal of Econometrics · 2025-01-01 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Monte Carlo Sampling

  Springer texts in statistics · 2025-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Large Bayesian VARs for Binary and Censored Variables

  ArXiv.org · 2025-06-02

  preprintOpen access1st authorCorresponding

  We extend the standard VAR to jointly model the dynamics of binary, censored and continuous variables, and develop an efficient estimation approach that scales well to high-dimensional settings. In an out-of-sample forecasting exercise, we show that the proposed VARs forecast recessions and short-term interest rates well. We demonstrate the utility of the proposed framework using a wide rage of empirical applications, including conditional forecasting and a structural analysis that examines the dynamic effects of a financial shock on recession probabilities.

  PublisherOA PDFDOI

Frequent coauthors

• Gary Koop

  University of Strathclyde

  50 shared

• Eric Eisenstat

  University of Queensland

  37 shared

• Rodney W. Strachan

  University of Miami

  29 shared

• Angelia L. Grant

  27 shared

• Dirk P. Kroese

  University of Queensland

  26 shared

• Justin L. Tobias

  26 shared

• Dale J. Poirier

  University of California, Irvine

  21 shared

• Liana Jacobi

  University of Melbourne

  18 shared

Education

• Ph.D.

  Purdue University

• M.S.

  Australian National University

• B.S.

  University of Queensland

• M.S.

  University of Technology Sydney

Awards & honors

• Fellow at International Association for Applied Econometrics
• Chair for the Economics, Finance and Business Section of the…
• ARC Discovery Early Career Researcher Award (2015)