Author: Oliver B. Linton
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
Book Description
We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is examined, and cases when efficiency gain can be achieved via weighting is investigated. We show that in many popular nonparametric regression models our method has lower asymptotic variance than the usual unweighted procedures. A Monte Carlo investigation is conducted and confirms the efficiency gain over conventional nonparametric regression estimators in finite samples. We use our method in several common applications concerning stock returns.
Efficient Estimation of Nonparametric Regression in the Presence of Dynamic Heteroskedasticity
Author: Oliver B. Linton
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
Book Description
We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is examined, and cases when efficiency gain can be achieved via weighting is investigated. We show that in many popular nonparametric regression models our method has lower asymptotic variance than the usual unweighted procedures. A Monte Carlo investigation is conducted and confirms the efficiency gain over conventional nonparametric regression estimators in finite samples. We use our method in several common applications concerning stock returns.
Publisher:
ISBN:
Category :
Languages : en
Pages : 75
Book Description
We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is examined, and cases when efficiency gain can be achieved via weighting is investigated. We show that in many popular nonparametric regression models our method has lower asymptotic variance than the usual unweighted procedures. A Monte Carlo investigation is conducted and confirms the efficiency gain over conventional nonparametric regression estimators in finite samples. We use our method in several common applications concerning stock returns.
Efficient Estimation of Nonparametric Regression in the Presence of Dynamic Heteroskedasticity
Author: Oliver Linton
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Efficient Estimation of the Error Distribution Function in Heteroskedastic Nonparametric Regression with Missing Data
Author: Justin Chown
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Efficient Estimation of Transformation Parameters in Nonparametric Regression
Author: William James Hooper
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 106
Book Description
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 106
Book Description
Efficient Estimation of the Regression Parameter in a Heteroscedastic Regression Model where Heteroscedasticity is Modeled as a Function of the Mean Response
Author: Jeffrey Scott Forrester
Publisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 158
Book Description
Publisher:
ISBN:
Category : Heteroscedasticity
Languages : en
Pages : 158
Book Description
The Oxford Handbook of Panel Data
Author: Badi Hani Baltagi
Publisher:
ISBN: 0199940045
Category : Business & Economics
Languages : en
Pages : 705
Book Description
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Publisher:
ISBN: 0199940045
Category : Business & Economics
Languages : en
Pages : 705
Book Description
The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.
Efficient Estimation of Regression Models with User-specified Parametric Model for Heteroskedasticty
Author: Saraswata Chaudhuri
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Book Description
Semiparametrically Efficient Estimation of the Average Linear Regression Function
Author: Bryan S. Graham
Publisher:
ISBN:
Category : Analysis of covariance
Languages : en
Pages : 45
Book Description
ELet Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b0 (w) be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average b0 = E[b0 (W)]. When X is binary-valued (multi-valued) our procedure recovers the (a vector of) average treatment effect(s). When X is continuously-valued, or consists of multiple non-exclusive treatments, our estimand coincides with the average partial effect (APE) of X on Y when the underlying potential response function is linear in X, but otherwise heterogenous across agents. When the potential response function takes a general nonlinear/heterogenous form, and X is continuously-valued, our procedure recovers a weighted average of the gradient of this response across individuals and values of X. We provide a simple, and semiparametrically efficient, method of covariate adjustment for settings with complicated treatment regimes. Our method generalizes familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the partially linear regression model).
Publisher:
ISBN:
Category : Analysis of covariance
Languages : en
Pages : 45
Book Description
ELet Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b0 (w) be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average b0 = E[b0 (W)]. When X is binary-valued (multi-valued) our procedure recovers the (a vector of) average treatment effect(s). When X is continuously-valued, or consists of multiple non-exclusive treatments, our estimand coincides with the average partial effect (APE) of X on Y when the underlying potential response function is linear in X, but otherwise heterogenous across agents. When the potential response function takes a general nonlinear/heterogenous form, and X is continuously-valued, our procedure recovers a weighted average of the gradient of this response across individuals and values of X. We provide a simple, and semiparametrically efficient, method of covariate adjustment for settings with complicated treatment regimes. Our method generalizes familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the partially linear regression model).
Efficient Estimation of Nonstationary Time Series Regression
Author: A C. Harvey
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 29
Book Description
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 29
Book Description
Testing and Efficient Estimation of Autoregressions with Conditional Heteroskedasticity
Author: Binbin Benjamin Guo
Publisher:
ISBN:
Category :
Languages : en
Pages : 356
Book Description
Publisher:
ISBN:
Category :
Languages : en
Pages : 356
Book Description