Author: Toni Clemens Stocker
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
Book Description
On the Asymptotic Properties of the OLS Estimator in Regression Models with Fractionally Integrated Regressors and Errors
Asymptotic Properties of Estimators for the Linear Panel Regression Model with Individual Effects and Serially Correlated Errors
Author: Badi H. Baltagi
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares, fixed effects, first-difference, and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This paper studies the asymptotic properties of standard panel data estimators in a simple panel regression model with error component disturbances. Both the regressor and the remainder disturbance term are assumed to be autoregressive and possibly non-stationary. Asymptotic distributions are derived for the standard panel data estimators including ordinary least squares, fixed effects, first-difference, and generalized least squares (GLS) estimators when both T and n are large. We show that all the estimators have asymptotic normal distributions and have different convergence rates dependent on the non-stationarity of the regressors and the remainder disturbances. We show using Monte Carlo experiments that the loss in efficiency of the OLS, FE and FD estimators relative to true GLS can be substantial.
Properties of Ordinary Least Squares Estimators in Regression Models with Non-spherical Disturbances
Author: Denzil G. Fiebig
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 44
Book Description
Publisher:
ISBN:
Category : Least squares
Languages : en
Pages : 44
Book Description
Asymptotic Properties of the Estimator of the Long-run Coefficient in a Dynamic Model with Integrated Regressors and Serially Correlated Errors
On the Asymptotic Properties of Parameter Estimates in a Regression Model with Non-Normally Distributed Errors
Author: John L. Maryak
Publisher:
ISBN:
Category :
Languages : en
Pages : 5
Book Description
The usual assumption of normality for the error terms of a regression model is often untenable. When this assumption is dropped, it may be difficult to characterize parameter estimates for the model. For example, it is stated that if the regression errors are non-normal, one is not even sure of their (e.g., the generalized least squares parameter estimates') asymptotic properties. A partial answer presents an asymptotic distribution theory for Kalman filter estimates for cases where the random terms of the state space model are not necessarily Gaussian. Certain of these asymptotic distribution results are also discussed in the context of model validation (diagnostic checking). Keywords: Random coefficient regression, State-space model, Non-Gaussian, Kalman filters, Reprints. (JHD).
Publisher:
ISBN:
Category :
Languages : en
Pages : 5
Book Description
The usual assumption of normality for the error terms of a regression model is often untenable. When this assumption is dropped, it may be difficult to characterize parameter estimates for the model. For example, it is stated that if the regression errors are non-normal, one is not even sure of their (e.g., the generalized least squares parameter estimates') asymptotic properties. A partial answer presents an asymptotic distribution theory for Kalman filter estimates for cases where the random terms of the state space model are not necessarily Gaussian. Certain of these asymptotic distribution results are also discussed in the context of model validation (diagnostic checking). Keywords: Random coefficient regression, State-space model, Non-Gaussian, Kalman filters, Reprints. (JHD).
Asymptotic Properties of a Class of Robust M-estimators for Nonlinear Regression Models with Momentless Distributed Errors and Regressors
Author: Hermanus Josephus Bierens
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 58
Book Description
Asymptotic Properties of Log Odds Ratio Regression Estimators with Sparse Strata and Covariate Measurement Error
Author: Andrew Benjamin Forbes
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 508
Book Description
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 508
Book Description
A Study of the Asymptotic Properties of Lasso Estimates for Correlated Data
Author: Shuva Gupta
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
ABSTRACT: In this thesis we investigate post-model selection properties of L1 penalized weighted least squares estimators in regression models with a large number of variables M and correlated errors. We focus on correct subset selection and on the asymptotic distribution of the penalized estimators. In the simple case of AR(1) errors we give conditions under which correct subset selection can be achieved via our procedure. We then provide a detailed generalization of this result to models with errors that have a weak-dependency structure (Doukhan 1996). In all cases, the number M of regression variables is allowed to exceed the sample size n. We further investigate the asymptotic distribution of our estimates, when M
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
ABSTRACT: In this thesis we investigate post-model selection properties of L1 penalized weighted least squares estimators in regression models with a large number of variables M and correlated errors. We focus on correct subset selection and on the asymptotic distribution of the penalized estimators. In the simple case of AR(1) errors we give conditions under which correct subset selection can be achieved via our procedure. We then provide a detailed generalization of this result to models with errors that have a weak-dependency structure (Doukhan 1996). In all cases, the number M of regression variables is allowed to exceed the sample size n. We further investigate the asymptotic distribution of our estimates, when M
Asymptotic properties of least squares estimators in regression models with forecast feedback
Asymptotic Properties of Maximum Likelihood Estimators in the General Sampling Framework, and Some Results in Non-normal Linear Regression
Author: Robert Ernest Tarone
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
Book Description