Seemingly unrelated regression equations models estimation and inference 1st Edition by Virendera K Srivastava, David E A Giles ISBN 0367451484 9780367451486 by Srivastava, Virendra K;giles, David E A 9780824776107, 9781000105728, 9781000127607, 9781000148930, 9781003065654, 0824776100, 1000105725, 1000127605, 1000148939 instant download after payment.

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Product details:

ISBN 10: 0367451484 
ISBN 13: 9780367451486
Author: Virendera K Srivastava, David E A Giles

This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.

Seemingly unrelated regression equations models estimation and inference 1st Table of contents:

1 The Seemingly Unrelated Regression Equations Model
1.1 Introduction
1.2 The Model
1.3 Outline
Exercises
References
2 The Least Squares Estimator and Its Variants
2.1 Introduction
2.2 The Ordinary Least Squares and Generalized Least Squares Estimators
2.3 The Feasible Generalized Least Squares Estimator
2.4 Optimality of Ordinary Least Squares
2.5 Simplifications of Feasible Generalized Least Squares
2.6 Some Asymptotic Properties
Exercises
References
3 Approximate Distribution Theory for Feasible Generalized Least Squares Estimators
3.1 Introduction
3.2 Asymptotic Approximations for Bias Vectors
3.3 Asymptotic Approximations for Variance Covariance Matrices
3.4 Asymptotic Approximations for Density and Distribution Functions
3.5 Monte Carlo Evidence
3.6 Some Remarks on Large-Sample Approximation Methodology and the Monte Carlo Technique
Exercises
References
4 Exact Finite-Sample Properties of Feasible Generalized Least Squares Estimators
4.1 Introduction
4.2 Unbiasedness
4.3 The Two Equation Model
4.4 Efficiency Properties Under Orthogonal Regressors
4.5 Efficiency Properties Under Subset Regressors
4.6 Efficiency Properties Under Unconstrained Regressors
4.7 Some Further Results
4.8 The Multi-Equation Model
Exercises
References
5 Iterative Estimators
5.1 Introduction
5.2 The Iterative Feasible Generalized Least Squares Estimator
5.3 The Iterative Ordinary Least Squares Estimator
5.4 The Maximum Likelihood Estimator
5.5 Computational Issues
5.6 Unbiasedness
5.7 Efficiency Properties
Exercises
References
6 Shrinkage Estimators
6.1 Introduction
6.2 Stein-Rule Estimators
6.3 Ridge-Type Estimators
6.4 Weighted-Combination Estimators
6.5 Lindley-Like Mean Corrections
Exercises
References
7 Autoregressive Disturbances
7.1 Introduction
7.2 First-Order Scalar Autoregressive Disturbances
7.3 First-Order Vector Autoregressive Disturbances
7.4 Efficiency Comparisons
7.5 Testing the Autoregressive Structure
7.6 Research Suggestions
Exercises
References
8 Heteroscedastic Disturbances
8.1 Introduction
8.2 Model Specification and Estimation
8.3 Estimator Properties
8.4 Efficiency Comparisons
8.5 Research Suggestions
Exercises
References
9 Constrained Error Covariance Structures
9.1 Introduction
9.2 Variance Inequalities and Positivity of Correlations
9.3 Error Components Structure
9.4 Research Suggestions
Exercises
References
10 Prior Information
10.1 Introduction
10.2 Restrictions on the Parameters
10.3 Specification Analysis
10.4 Bayesian Analysis
10.5 Research Suggestions
Exercises
References
11 Some Miscellaneous Topics
11.1 Introduction
11.2 The Varying Coefficients Model
11.3 Missing Observations
11.4 Goodness-of-Fit Measures
11.5 Dynamic Models
11.6 Non-Linear Models
11.7 Further Research
Exercises
References
Appendix

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Tags: Virendera K Srivastava, David E A Giles, Seemingly, regression