Anil K Bera

About

Anil K Bera is a professor with appointments in Economics, Finance, Agricultural and Consumer Economics, and Statistics at the University of Illinois. His research expertise is reflected in a fingerprint of key subjects including test statistics, misspecification, normality tests, finite sample properties, score tests, linear regression models, conditionals, and Lagrange multiplier tests, indicating a strong focus on econometric and statistical methodologies. Over the last five years, Professor Bera has contributed extensively to scholarly literature, authoring numerous peer-reviewed articles and chapters that address advanced topics such as spatial panel data models, spatiotemporal volatility models, and time series analysis. His work often involves sophisticated mathematical and statistical techniques applied to economics and finance, including likelihood functions, maximum likelihood estimation, and co-integration analysis. Additionally, Professor Bera has engaged in collaborative research internationally, contributing to the understanding of econometric models and their applications in various economic contexts. His scholarly output also includes reflective pieces on prominent figures in statistics, such as Dr. C.R. Rao, highlighting his engagement with the historical and intellectual development of the field. Overall, Professor Bera's professional biography is characterized by a deep commitment to advancing econometric theory and its practical applications in economics…

Research topics

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

Selected publications

• To use, or not to use the spatial Durbin model? – that is the question

  Spatial Economic Analysis · 2023 · 35 citations

  Senior authorCorresponding

  The spatial Durbin model (SDM) is one of the most widely used models in spatial econometrics. It originated as a generalisation of the spatial error model (SEM) under a non-linear parametric restriction (see Anselin (1988, pp. 110–111)). This restriction should be tested to select an appropriate model between SDM and SEM. Perhaps, due to the complexity of executing a test for a non-linear hypothesis, this restriction is rarely tested in practice, though see Burridge (1981), Mur and Angulo (2006)…

  PublisherDOI

• Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago*

  Oxford Bulletin of Economics and Statistics · 2021 · 26 citations

  Senior authorCorresponding

  Abstract In this study, we propose a spatial stochastic volatility model in which the latent log‐volatility terms follow a spatial autoregressive process. Though there is no spatial correlation in the outcome equation (the mean equation), the spatial autoregressive process defined for the log‐volatility terms introduces spatial dependence in the outcome equation. To introduce a Bayesian Markov chain Monte Carlo (MCMC) estimation algorithm, we transform the model so that the outcome equation take…

  PublisherDOI

• Testing Impact Measures in Spatial Autoregressive Models

  International Regional Science Review · 2019-02-27 · 21 citations

  articleOpen access

  Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the…

  PublisherOA PDFDOI

• A Bayesian robust chi-squared test for testing simple hypotheses

  Journal of Econometrics · 2020 · 14 citations

  Senior authorCorresponding
  PublisherDOI

• Local and global determinants of office rents in Istanbul

  Journal of European real estate research · 2019-06-19 · 12 citations

  article1st authorCorresponding

  Purpose This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not. Design/methodology/approach To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic offi…

  PublisherDOI

Frequent coauthors

• Osman Doğan

  Istanbul Technical University

  26 shared

• Süleyman Taşpınar

  Queens College, CUNY

  25 shared

• Mann J. Yoon

  California State University Los Angeles

  9 shared

• Walter Sosa‐Escudero

  University of San Andrés

  9 shared

• Gabriel Montes‐Rojas

  University of Buenos Aires

  9 shared

• Aurobindo Ghosh

  8 shared

• Matthew Higgins

  Western Michigan University

  8 shared

• Walter Sosa Escudero

  University of San Andrés

  5 shared

Education

• Doctor of Philosophy - PhD, Economics

  Australian National University

  1983

• M. Stat, Econometrics and Planning

  Indian Statistical Institute

  1977

• Bachelor of Science - BS, Statistics

  University of Calcutta

  1975

Similar researchers at University of Illinois Urbana-Champaign

• Marcelo Cunha Medeirosh-27
• David Yves Albouyh-25
• Stefan Krasah-20