Mohammad Arshad RahmanIndian Institute of Technology Kanpur | IIT Kanpur · Economic Sciences
Mohammad Arshad Rahman
Ph.D. (Economics)
Looking for a new position and collaborators.
About
36
Publications
5,205
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226
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Introduction
I am a tenured Associate Professor in the Department of Economic Sciences at the Indian Institute of Technology Kanpur (IITK), India. While on leave from IITK, I have worked as an Associate Professor in the College of Business at Zayed University, UAE.
My research interest includes Bayesian Econometrics, Quantile Regression, Markov chain Monte Carlo Techniques, Empirical Finance, and Applied Econometrics.
Additional affiliations
December 2020 - December 2022
May 2017 - December 2019
July 2013 - May 2017
Education
September 2008 - June 2013
July 2004 - April 2006
Delhi School of Economics, New Delhi, India
Field of study
- Economics
Publications
Publications (36)
This article develops multiple novel climate risk measures (or variables) based on the television news coverage by Bloomberg, CNBC, and Fox Business, and examines how they affect the systematic and idiosyncratic risks of clean energy firms in the United States (US). The measures are built on climate related keywords and cover the volume of coverage...
This chapter presents an overview of a specific form of limited dependent variable models, namely, discrete choice models, where the dependent (response or outcome) variable takes values which are discrete and inherently ordered. Within this setting, the dependent variable may take only two values (such as 0 and 1) giving rise to binary models (e.g...
This article describes an R package bqror that estimates Bayesian quantile regression in ordinal models introduced in Rahman (2016). The paper classifies ordinal models into two types and offers computationally efficient yet simple Markov chain Monte Carlo (MCMC) algorithms for estimating ordinal quantile regression. The generic ordinal model with...
This article develops a random effects quantile regression model for panel data that allows for increased distributional flexibility, multivariate heterogeneity, and time-invariant covariates in situations where mean regression may be unsuitable. Our approach is Bayesian and builds upon the generalized asymmetric Laplace distribution to decouple th...
The paper introduces a Bayesian estimation method for quantile regression in univariate ordinal models. Two algorithms are presented that utilize the latent variable inferential framework of Albert and Chib (1993) and the normal-exponential mixture representation of the asymmetric Laplace distribution. Estimation utilizes Markov chain Monte Carlo s...
This article describes an R package bqror that estimates Bayesian quantile regression for ordinal models introduced in \citet{Rahman-2016}. The paper classifies ordinal models into two types and offers two computationally efficient, yet simple, MCMC algorithms for estimating ordinal quantile regression. The generic ordinal model with more than 3 ou...
This chapter presents an overview of a specific form of limited dependent variable models, namely discrete choice models, where the dependent (response or outcome) variable takes values which are discrete, inherently ordered, and characterized by an underlying continuous latent variable. Within this setting, the dependent variable may take only two...
Education has traditionally been classroom-oriented with a gradual growth of online courses in recent times. However, the outbreak of the COVID-19 pandemic has dramatically accelerated the shift to online classes. Associated with this learning format is the question: what do people think about the educational value of an online course compared to a...
Empirical studies on food expenditure are largely based on cross-section data and for a few studies based on longitudinal (or panel) data the focus has been on the conditional mean. While the former, by construction, cannot model the dependencies between observations across time, the latter cannot look at the relationship between food expenditure a...
This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace error distribution and combine it with suitable prior distributions to obtain the complete joint posterior distribution. For posterior inf...
Empirical studies on food expenditure are largely based on cross-section data and for a few studies based on longitudinal (or panel) data the focus has been on the conditional mean. While the former, by construction, cannot model the dependencies between observations across time, the latter cannot look at the relationship between food expenditure a...
Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation...
Education has traditionally been classroom-oriented with a gradual growth of online courses in recent times. However, the outbreak of the COVID-19 pandemic has dramatically accelerated the shift to online classes. Associated with this learning format is the question: what do people think about the educational value of an online course compared to a...
Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation...
Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation...
This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain the complete joint posterior distribution. For posterio...
This article develops a Bayesian approach for estimating panel quantile regression with binary outcomes in the presence of correlated random effects. We construct a working likelihood using an asymmetric Laplace (AL) error distribution and combine it with suitable prior distributions to obtain the complete joint posterior distribution. For posterio...
The existing literature on Bayesian updating of structural models have
assigned equal variances (homoscedasticity) in the measured observables across all modes by assuming a Gaussian error
distribution. This paper relaxes the assumption by allowing the error distribution to be conditionally heteroscedastic, but marginally follows the Student's t-d...
This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as w...
This article is motivated by the lack of flexibility in Bayesian quantile regression for ordinal
models where the error follows an asymmetric Laplace (AL) distribution. The inflexibility
arises because the skewness of the distribution is completely specified when a quantile is chosen. To overcome this shortcoming, we derive the cumulative distribut...
This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as w...
Consider a non-parametric regression model y = µ(x)+✏, where y is the response variable, x is the scalar covariate, ✏ is the error and µ is the unknown non-parametric regression function. For this model, we propose a new graphical device to check whether v-th (v > 1) derivative of the regression function µ is positive or not, which includes checkin...
The paper utilizes the entire cricketing data between England and Australia-Test and one-day international (ODI) matches played between 1877-2015 and 1971-2015, respectively-to provide an econometric perspective on the England-Australia rivalry. We employ the production function approach of Schofield (1988) and model Test match outcomes (loss, draw...
The paper introduces a Bayesian estimation method for quantile regression
in univariate ordinal models. Two algorithms are presented that utilize the
latent variable inferential framework of Albert and Chib (1993) and the
normal-exponential mixture representation of the asymmetric Laplace
distribution. Estimation utilizes Markov chain Monte Carlo s...
This paper demonstrates that metaheuristic algorithms can provide a
useful general framework for estimating both linear and nonlinear econometric
models. Two metaheuristic algorithms – firefly and accelerated particle swarm
optimisation – are employed in the context of several quantile regression
models. The algorithms are stable and robust to the...
Introduction Theoretical Foundations Estimation Applications Conclusions
We consider the Bayes estimation of a multivariate sample selection
model with p pairs of selection and outcome variables. Each of the
variables may be discrete or continuous with a parametric marginal
distribution, and their dependence structure is modeled through a
Gaussian copula function. Markov chain Monte Carlo methods are used
to simulate fr...