Anil K Bera
Biographic Data
| ID | 6447703 |
|---|---|
| NAME | Anil K Bera |
| GIVEN NAMES | Anil K |
| FAMILY NAME | Bera |
| SIGNATURE | BERA A K |
| AFFILIATIONS | University of Illinois Urbana-Champaign |
| ORCID | 0009-0000-2206-8143 |
| VERIFIED | Yes |
| TOTAL WORKS | 11 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 11 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1980 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 1 |
Bayesian estimation of stochastic tail index from high-frequency financial data
Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago
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 th…
Information theoretic approaches to income density estimation with an application to the U.S. income data
ARCH and Bilinearity as Competing Models for Nonlinear Dependence
In this article we consider whether the wide acceptance of autoregressive conditional heteroscedasticity (ARCH) models may be at the expense of other nonlinear processes, such as bilinear models. We first propose a joint test for ARCH and bilinearity. A nonnested test is then suggested to determine whether nonlinear dependence should be attributed to ARCH or bilinearity. The tests are then applied to three series. When generalized ARCH (GARCH) mo…
Simple diagnostic tests for spatial dependence
Interaction Between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach
In applied econometrics, we tend to tackle specification problems one at a time rather than considering them jointly. This has serious consequences for statistical inference. One example of this is considering autocorrelation and autoregressive conditional heteroscedasticity (ARCH) separately. In this article we consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze a…
Adoption of high yielding rice varieties in Bangladesh
Tests for Serial Dependence and Other Specification Analysis in Models of Markets in Disequilibrium
The assumption of serial independence of disturbances is the starting point of most of the work done on analyzing market disequilibrium models. We derive tests for serial dependence given normality and homoscedasticity using the Lagrange multiplier (LM) test principle. Although the likelihood function under serial dependence is very complicated and involves multiple integrals of dimensions equal to the sample size, the test statistic we obtain th…
A Test for Normality of Observations and Regression Residuals
Carlos M. Jarque, Anil K. Bera, A Test for Normality of Observations and Regression Residuals, International Statistical Review / Revue Internationale de Statistique, Vol. 55, No. 2 (Aug., 1987), pp. 163-172
Some Exact Tests for Model Specification
Efficient tests for normality, homoscedasticity and serial independence of regression residuals
Efficient tests for normality, homoscedasticity and serial independence of regression residuals
Some Exact Tests for Model Specification
A Test for Normality of Observations and Regression Residuals
Carlos M. Jarque, Anil K. Bera, A Test for Normality of Observations and Regression Residuals, International Statistical Review / Revue Internationale de Statistique, Vol. 55, No. 2 (Aug., 1987), pp. 163-172
Tests for Serial Dependence and Other Specification Analysis in Models of Markets in Disequilibrium
The assumption of serial independence of disturbances is the starting point of most of the work done on analyzing market disequilibrium models. We derive tests for serial dependence given normality and homoscedasticity using the Lagrange multiplier (LM) test principle. Although the likelihood function under serial dependence is very complicated and involves multiple integrals of dimensions equal to the sample size, the test statistic we obtain th…
Adoption of high yielding rice varieties in Bangladesh
Interaction Between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach
In applied econometrics, we tend to tackle specification problems one at a time rather than considering them jointly. This has serious consequences for statistical inference. One example of this is considering autocorrelation and autoregressive conditional heteroscedasticity (ARCH) separately. In this article we consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze a…
Simple diagnostic tests for spatial dependence
ARCH and Bilinearity as Competing Models for Nonlinear Dependence
In this article we consider whether the wide acceptance of autoregressive conditional heteroscedasticity (ARCH) models may be at the expense of other nonlinear processes, such as bilinear models. We first propose a joint test for ARCH and bilinearity. A nonnested test is then suggested to determine whether nonlinear dependence should be attributed to ARCH or bilinearity. The tests are then applied to three series. When generalized ARCH (GARCH) mo…
Information theoretic approaches to income density estimation with an application to the U.S. income data
Bayesian estimation of stochastic tail index from high-frequency financial data
Bayesian Inference in Spatial Stochastic Volatility Models: An Application to House Price Returns in Chicago
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 th…
Econometrics (11 works) · Mathematics (10 works) · Statistics (9 works) · Computer Science (6 works) · Statistical hypothesis testing (5 works) · Economics (4 works) · Heteroscedasticity (4 works) · Applied Mathematics (3 works) · Autoregressive model (3 works) · Financial Risk and Volatility Modeling (3 works)