Author: Yanquin FanPublisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 17
Book Description
Adaptive Estimation in Semiparametric Regression Models with Conditionally Heteroskedastic Disturbances
Author: Yanquin FanPublisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 17
Book Description
Efficient and Adaptive Estimation for Semiparametric Models
Author: Peter J. BickelPublisher: Springer
ISBN: 0387984739
Category : Mathematics
Languages : en
Pages : 588
Book Description
This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Partially Adaptive Estimation of Truncated Regression Modles
Author: Patrick A. TurleyPublisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 42
Book Description
Applying traditional regression methods or parametric methods (such as OLS or the Tobit estimator) to truncated regression models leads to biased and inconsistent estimators when the error distribution is misspecified. This paper proposes using partially adaptive estimators based on flexible error distributions to account for possibly skewed or leptokurtotic errors. Monte Carlo simulations and empirical applications are used to compare the performance of these estimators to several semi-parametric estimators. These results suggest an improved performance of partially adaptive estimators over the other estimators considered based on the sample RMSE for the data considered. A study of how the impact of education on earnings varies among income levels shows significant variation in the results based on the estimation method used.
Adaptive Estimation in Time Series Regression Models
Author: Douglas Gardiner SteigerwaldPublisher:
ISBN:
Category :
Languages : en
Pages : 180
Book Description
Econometric Theory and Practice
Author: P. C. B. PhillipsPublisher: Cambridge University Press
ISBN: 9780521807234
Category : Business & Economics
Languages : en
Pages : 390
Book Description
The essays in this book explore important theoretical and applied advances in econometrics.
Adaptive Estimation of Non-linear Regression Models
Author: Charles F. ManskiPublisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 51
Book Description
Adaptive Regression
Author: Yadolah DodgePublisher: Springer Science & Business Media
ISBN: 1441987665
Category : Mathematics
Languages : en
Pages : 188
Book Description
While there have been a large number of estimation methods proposed and developed for linear regression, none has proved good for all purposes. This text focuses on the construction of an adaptive combination of two estimation methods so as to help users make an objective choice and combine the desirable properties of two estimators.
Generalized Adaptive Estimation for Econometric and Financial Models
Author: Douglas SteigerwaldLocally Adaptive Semiparametric Estimation of the Mean and Variance Functions in Regression Models
Author: David X. ChanPublisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
This article considers the estimation of a regression model with Gaussian errors, where the mean and the log variance are modeled as a linear combination of explanatory variables. We consider Bayesian variable selection priors and model averaging to obtain efficient estimators when the number of explanatory variables is large. To make the model semiparametric using this framework we allow explanatory variables to enter the mean and log variance models flexibly by representing a covariate effect as a linear combination of basis functions. Our methodology for estimating flexible effects is locally adaptive in the sense that it works well when the flexible effects vary rapidly in some parts of the predictor space but only slowly in other parts. The whole model is estimated using a Markov chain simulation method that samples the posterior distribution with coefficients in the mean model integrated out analytically and highly dependent parameters generated in blocks. The methodology in the paper is applied to a number of simulated and real examples and is shown to work well.
Seemingly Unrelated Regression Equations Models
Author: Virendera K. SrivastavaPublisher: CRC Press
ISBN: 9780824776107
Category : Mathematics
Languages : en
Pages : 398
Book Description
The seemingly unrelated regression equations model; The least squares estimator and its variants; Approximate destribution theory for feasible generalized least squares estimators; Exact finite-sample properties of feasible generalized least squares estimators; Iterative estimators; Shrinkage estimators; Autoregressive disturbances; Heteroscedastic disturbances; Constrained error covariance structures; Prior information; Some miscellaneous topics.