Name
Abstract | ||
|---|---|---|
The problem of consistent estimation of the regression coefficients when some prior information about the regression coefficients is available is considered. Such prior information is expressed in the form of exact linear restrictions. The knowledge of covariance matrix of measurement errors that is associated with explanatory variables is used to construct the consistent estimators. Some consistent estimators are suggested which satisfy the exact linear restrictions also. Their asymptotic properties are derived and analytically analyzed under a multivariate ultrastructural model with not necessarily normally distributed measurement errors. The finite sample properties of the estimators are studied through a Monte-Carlo simulation experiment. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.csda.2007.05.011 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
covariance matrix,consistent estimator,measurement error model,monte-carlo simulation experiment,measurement error,asymptotic property,regression coefficient,restricted regression estimation,explanatory variable,consistent estimation,prior information,exact linear restriction,normal distribution,satisfiability,monte carlo simulation,measurement errors | Econometrics,Errors-in-variables models,Regression analysis,Covariance matrix,Statistics,Prior probability,Mathematics,Covariance,Linear regression,Consistent estimator,Estimator | Journal |
Volume | Issue | ISSN |
52 | 2 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
7 | 1.34 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shalabh | 1 | 18 | 4.96 |
| Gaurav Garg | 2 | 232 | 20.61 |
| Neeraj Misra | 3 | 22 | 5.51 |