Efficiency of Mansson’s method: Some numerical findings about the role of biasing parameter in the estimation of distributed lag model
From MaRDI portal
Publication:5088114
DOI10.1080/03610918.2018.1517215OpenAlexW2915183929WikidataQ128347522 ScholiaQ128347522MaRDI QIDQ5088114
Publication date: 4 July 2022
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2018.1517215
Applications of statistics to economics (62P20) Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Related Items
Addressing the distributed lag models with heteroscedastic errors ⋮ The Almon M-estimator for the distributed lag model in the presence of outliers ⋮ Restricted estimation of distributed lag model from a Bayesian point of view
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Mean Square Error Comparisons of the Alternative Estimators For The Distributed Lag Models
- A Simulation Study of Some Ridge Regression Estimators under Different Distributional Assumptions
- Some Modifications for Choosing Ridge Parameters
- On Some Ridge Regression Estimators: An Empirical Comparisons
- Ridge regression:some simulations
- A Distributed Lag Estimator Derived from Smoothness Priors
- Some Properties of the Almon Lag Technique when One Searches for Degree of Polynomial and Lag
- A simulation study of ridge and other regression estimators
- A Class of Biased Estimators in Linear Regression
- A Note on Bias in the Almon Distributed Lag Estimator
- Choosing Ridge Parameter for Regression Problems
- Improvement of the Liu‐type Shiller estimator for distributed lag models
- Performance of Some New Ridge Regression Estimators
- Comparisons of the alternative biased estimators for the distributed lag models
- Developing Ridge Parameters for SUR Model
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
