Mohammad Arshad Rahman

Department of Economic Sciences
Indian Institute of Technology Kanpur

marshad@iitk.ac.in

About

Welcome to my homepage!

I am a tenured Associate Professor in the Department of Economic Sciences at the Indian Institute of Technology Kanpur (IIT Kanpur), India, where I joined as an Assistant Professor in July 2013. Before joining IIT Kanpur, I completed my Ph.D. in Economics from the Department of Economics at the University of California, Irvine, United States, where I worked under the supervision of Ivan Jeliazkov (Chair), Dale J. Poirier, David Brownstone, and Fabio Milani.

As a Bayesian econometrician, I develop econometric models and methods for diverse cross-sectional and panel data settings. My research spans quantile regression, discrete choice models, Bayesian computation, Markov chain Monte Carlo (MCMC) methods, and simulation-based inference. Alongside theoretical and methodological research, I apply advanced Bayesian modeling and techniques to problems in economics, finance, and the social sciences, aiming to uncover insights that conventional econometric analysis may overlook. These interests also shape my teaching, where I integrate econometric theory, statistical methods, and their applications across undergraduate and graduate courses in Econometrics, Statistics, and Finance.

My academic career spans appointments in the United States (US), United Arab Emirates (UAE), and India. This includes an Associate Professor position at Zayed University (UAE), teaching appointments at UC Irvine (US) and the Chennai Mathematical Institute (India), and a visiting position at the Indian Statistical Institute. My academic experience also includes a range of administrative responsibilities at IIT Kanpur and Zayed University (see CV for details). I remain engaged with the broader academic community through organizing workshops and conferences, mentoring, and peer review, having reviewed for more than 50 journals, including the Journal of Econometrics, Bayesian Analysis, and The Annals of Applied Statistics.