On the Asymptotic Properties of the OLS Estimator in Regression Models with Fractionally Integrated Regressors and Errors PDF Download
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Author: Toni Clemens Stocker
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Author: Toni Clemens Stocker
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Author: Badi H. Baltagi
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ISBN:
Category :
Languages : en
Pages : 0
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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.
Author: Denzil G. Fiebig
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ISBN:
Category : Least squares
Languages : en
Pages : 44
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Author:
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Category :
Languages : en
Pages :
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Author: John L. Maryak
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Category :
Languages : en
Pages : 5
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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).
Author: Hermanus Josephus Bierens
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ISBN:
Category :
Languages : en
Pages : 58
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Author: Andrew Benjamin Forbes
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Category : Estimation theory
Languages : en
Pages : 508
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Author: Shuva Gupta
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Category :
Languages : en
Pages :
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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
Author: Michael Mohr
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ISBN:
Category :
Languages : de
Pages : 25
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Author: Robert Ernest Tarone
Publisher:
ISBN:
Category :
Languages : en
Pages : 190
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