Andres Ramirez Hassan

Andrés Ramírez-Hassan, Ph.D.

Bayesian Econometrician

Curriculum Vitae (PDF)

Visiting Professor, University of Melbourne

Distinguished Professor, Universidad EAFIT

I am a Bayesian econometrician working on methodological and computational aspects of econometric models with unobserved heterogeneity, selection, and endogeneity. My research develops Bayesian hierarchical and mixture-based frameworks for causal inference, demand analysis, and policy evaluation, with particular emphasis on posterior computation, identification, and predictive inference.

My work combines econometric theory, Bayesian modeling, and scalable computation, and is motivated by empirical applications in microeconomics, including consumption behavior, program evaluation, and policy analysis.

Contact

aramir21@eafit.edu.co

Research

My research focuses on:

Bayesian mixture and hierarchical models for heterogeneous treatment effects and demand systems
Selection and access mechanisms in multi-stage econometric models
Bayesian inference under endogeneity
Computational methods for posterior simulation, model comparison, and prediction

I develop theory and software to support transparent and reproducible Bayesian inference in applied econometrics.

→ Research page

Software

BEsmarter GUI

I am the developer of BEsmarter, a graphical Bayesian econometrics platform built in Shiny that allows users without programming skills to perform Bayesian inference in econometric models.

BEsmarter supports:

Microeconometric and simultaneous equation models
Time series and hierarchical Bayesian models
Bayesian Model Averaging (BMA)

BEsmarter is designed to lower the entry barrier to Bayesian econometrics and to support teaching and applied research.

→ Software & BEsmarter

Teaching

I teach graduate-level econometrics with emphasis on:

Asymptotic theory and M- and Z-estimators
Maximum likelihood and GMM
Bayesian econometrics and computational methods

I am the author of Introduction to Bayesian Econometrics: A GUIded Toolkit Using R, a book designed to help students and applied researchers learn Bayesian econometrics through hands-on implementation and graphical tools.

→ Teaching

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