scholarly journals Feasible generalized least squares for panel data with cross-sectional and serial correlations

2020 ◽  
Author(s):  
Jushan Bai ◽  
Sung Hoon Choi ◽  
Yuan Liao
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Generalized Least Squares ◽  
Cross Sectional ◽  
Serial Correlations

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

scholarly journals Panel Data Estimation for Correlated Random Coefficients Models

Econometrics ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 7
Author(s):  
Cheng Hsiao ◽  
Qi Li ◽  
Zhongwen Liang ◽  
Wei Xie
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Asymptotic Properties ◽  
Generalized Least Squares ◽  
Random Coefficients ◽  
Least Squares Estimator ◽  
Finite Sample ◽  
Correlation Pattern ◽  
Panel Data Estimation ◽  
Generalized Least Squares Estimator

This paper considers methods of estimating a static correlated random coefficient model with panel data. We mainly focus on comparing two approaches of estimating unconditional mean of the coefficients for the correlated random coefficients models, the group mean estimator and the generalized least squares estimator. For the group mean estimator, we show that it achieves Chamberlain (1992) semi-parametric efficiency bound asymptotically. For the generalized least squares estimator, we show that when T is large, a generalized least squares estimator that ignores the correlation between the individual coefficients and regressors is asymptotically equivalent to the group mean estimator. In addition, we give conditions where the standard within estimator of the mean of the coefficients is consistent. Moreover, with additional assumptions on the known correlation pattern, we derive the asymptotic properties of panel least squares estimators. Simulations are used to examine the finite sample performances of different estimators.

scholarly journals Governance quality impact on health economics in selected countries: The panel data approach

2015 ◽  
Vol 4 (2) ◽  
pp. 148-154
Author(s):  
Parvaneh Salatin ◽  
Naahid Noorpoor
Keyword(s):  
Panel Data ◽  
Health Economics ◽  
Life Expectancy ◽  
Least Squares ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Generalized Least Squares ◽  
Middle Income ◽  
Middle Income Countries ◽  
Governance Quality

The purpose of this paper is investigating the theoretical relationship between the effectiveness of governance quality on health economics in selected middle-income countries, using panel data. The Results of the estimation by using the Method of Generalized Least Squares (GLS) & Generalized Method of Moments (GMM) in selected countries for the period 2002-2011 show that governance quality has positive & significant effect on the life expectancy as an index showing the health economics in the group of the selected countries.

ASYMPTOTICALLY EFFICIENT MODEL SELECTION FOR PANEL DATA FORECASTING

2018 ◽  
Vol 35 (4) ◽  
pp. 842-899 ◽  
Author(s):  
Ryan Greenaway-McGrevy
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Least Squares ◽  
Cross Sections ◽  
Fixed Effects ◽  
Selection Criterion ◽  
Vector Autoregressions ◽  
Cross Sectional ◽  
Time Series Approach ◽  
Asymptotically Efficient

This article develops new model selection methods for forecasting panel data using a set of least squares (LS) vector autoregressions. Model selection is based on minimizing the estimated quadratic forecast risk among candidate models. We provide conditions under which the selection criterion is asymptotically efficient in the sense of Shibata (1980) as n (cross sections) and T (time series) approach infinity. Relative to extant selection criteria, this criterion places a heavier penalty on model dimensionality in order to account for the effects of parameterized forms of cross sectional heterogeneity (such as fixed effects) on forecast loss. We also extend the analysis to bias-corrected least squares, showing that significant reductions in forecast risk can be achieved.

UNEQUALLY SPACED PANEL DATA REGRESSIONS WITH AR(1) DISTURBANCES

1999 ◽  
Vol 15 (6) ◽  
pp. 814-823 ◽  
Author(s):  
Badi H. Baltagi ◽  
Ping X. Wu
Keyword(s):  
Panel Data ◽  
Least Squares ◽  
Serial Correlation ◽  
Weighted Least Squares ◽  
Generalized Least Squares ◽  
Unequally Spaced ◽  
Panel Data Regression ◽  
Data Regression ◽  
Wide Range ◽  
Invariant Test

This paper deals with the estimation of unequally spaced panel data regression models with AR(1) remainder disturbances. A feasible generalized least squares (GLS) procedure is proposed as a weighted least squares that can handle a wide range of unequally spaced panel data patterns. This procedure is simple to compute and provides natural estimates of the serial correlation and variance components parameters. The paper also provides a locally best invariant test for zero first-order serial correlation against positive or negative serial correlation in case of unequally spaced panel data.

A Moving Cross-Section Analysis of Demand for Toothpaste

1970 ◽  
Vol 7 (4) ◽  
pp. 439-449 ◽  
Author(s):  
Kristian S. Palda ◽  
Larry M. Blair
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Least Squares ◽  
Cross Section ◽  
Single Equation ◽  
Cross Sectional ◽  
Least Squares Estimates ◽  
Section Analysis

MRCA panel data on toothpaste expenditures are used to demonstrate how time series and cross-sectional bias can be eliminated by the method of covariance regression from single-equation demand least squares estimates.

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