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Non-parametric transformation regression with non-stationary data

Linton, Oliver; Wang, Qiying - 2013

We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.

Persistent link: https://www.econbiz.de/10009734305

Nonparametric rank tests for non-stationary panels

Pedroni, Peter Louis; Vogelsang, Timothy J.; Wagner, Martin - 2011

This study develops new rank tests for panels that include panel unit root tests as a special case. The tests are …-unit cointegration, without the need to specify the form of dependence or estimate nuisance parameters associated with the dependence … roots ; cointegration ; cross-sectional dependence …

Persistent link: https://www.econbiz.de/10009686205

Nonparametric rank tests for non-stationary panels

Pedroni, Peter Louis; Vogelsang, Timothy J.; Wagner, Martin - 2011

Persistent link: https://www.econbiz.de/10009228950

On the Estimation of Panel Regression Models with Fixed Effects

Kruiniger, Hugo - 2002

This paper considers estimation of panel data models with fixed effects. First, we will show that a consistent … "unrestricted fixed effects" estimator does not exist for autoregressive panel data models with initial conditions. We will derive … widely used GMM estimators for the conditional AR(1) panel model are inconsistent under trending fixed effects sequences …

Persistent link: https://www.econbiz.de/10014120610

Penalized quantile regression with semiparametric correlated effects : applications with heterogeneous preferences

Harding, Matthew C.; Lamarche, Carlos - 2013

This paper proposes new ℓ1-penalized quantile regression estimators for panel data, which explicitly allows for …

Persistent link: https://www.econbiz.de/10010238040

Functional Coefficient Nonstationary Regression

Gao, Jiti - 2013

This paper studies a general class of nonlinear varying coefficient time series models with possible nonstationarity in both the regressors and the varying coffiecient components. The model accommodates a cointegrating structure and allows for endogeneity with contemporaneous correlation among...

Persistent link: https://www.econbiz.de/10013075943

Functional Coefficient Nonstationary Regression

Gao, Jiti - 2013

This paper studies a general class of nonlinear varying coefficient time series models with possible nonstationarity in both the regressors and the varying coefficient components. The model accommodates a cointegrating structure and allows for endogeneity with contemporaneous correlation among...

Persistent link: https://www.econbiz.de/10013078440

Semiparametric Estimation in Time Series Regression with Long-Range Dependence

Nielsen, Morten Ørregaard - 2005

We consider semiparametric estimation in time series regression in the presence of long range dependence in both the errors and the stochastic regressors. A central limit theorem is established for a class of semiparametric frequency domain weighted least squares estimates, which includes both...

Persistent link: https://www.econbiz.de/10014099599

Estimation and inference for varying-coeffcient models with nonstationary regressors using penalized splines

Chen, Haiqiang; Fang, Ying; Li, Yingxing - 2013

This paper considers estimation and inference for varying-coefficient models with nonstationary regressors. We propose a nonparametric estimation method using penalized splines, which achieves the same optimal convergence rate as kernel-based methods, but enjoys computation advantages. Utilizing...

Persistent link: https://www.econbiz.de/10009767261

Trend estimation with penalized splines as mixed models for series with structural breaks

Blöchl, Andreas - 2014

On purpose to extract trend and cycle from a time series many competing techniques have been developed. The probably most prevalent is the Hodrick Prescott filter. However this filter suffers from diverse shortcomings, especially the subjective choice of its penalization parameter. To this point...

Persistent link: https://www.econbiz.de/10010350102