Book contents
- Frontmatter
- Contents
- List of figures
- List of tables
- List of contributors
- Preface
- Acknowledgements
- Michael Magdalinos 1949–2002
- Introduction
- 1 Conditional Heteroskedasticity Models with Pearson Disturbances
- 2 The Instrumental Variables Method Revisited: On the Nature and Choice of Optimal Instruments
- 3 Nagar-Type Moment Approximations in Simultaneous Equation Models: Some Further Results
- 4 Local GEL Methods for Conditional Moment Restrictions
- 5 Limit Theory for Moderate Deviations From a Unit Root Under Weak Dependence
- 6 The Structure of Multiparameter Tests
- 7 Cornish-Fisher Size Corrected t and F Statistics for the Linear Regression Model with Heteroscedastic Errors
- 8 Non-Parametric Specification Testing of Non-Nested Econometric Models
- 9 Testing for Autocorrelation in Systems of Equations
- 10 Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling Asset Returns
- 11 Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models
- 12 A Statistical Proof of the Transformation Theorem
- 13 On the Joint Density of the Sum and Sum of Squares of Non-Negative Random Variables
- 14 Conditional Response Analysis
- References
- Index
Introduction
Published online by Cambridge University Press: 22 September 2009
- Frontmatter
- Contents
- List of figures
- List of tables
- List of contributors
- Preface
- Acknowledgements
- Michael Magdalinos 1949–2002
- Introduction
- 1 Conditional Heteroskedasticity Models with Pearson Disturbances
- 2 The Instrumental Variables Method Revisited: On the Nature and Choice of Optimal Instruments
- 3 Nagar-Type Moment Approximations in Simultaneous Equation Models: Some Further Results
- 4 Local GEL Methods for Conditional Moment Restrictions
- 5 Limit Theory for Moderate Deviations From a Unit Root Under Weak Dependence
- 6 The Structure of Multiparameter Tests
- 7 Cornish-Fisher Size Corrected t and F Statistics for the Linear Regression Model with Heteroscedastic Errors
- 8 Non-Parametric Specification Testing of Non-Nested Econometric Models
- 9 Testing for Autocorrelation in Systems of Equations
- 10 Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling Asset Returns
- 11 Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models
- 12 A Statistical Proof of the Transformation Theorem
- 13 On the Joint Density of the Sum and Sum of Squares of Non-Negative Random Variables
- 14 Conditional Response Analysis
- References
- Index
Summary
Twenty-two authors have contributed to this book which comprises 14 chapters. Chapters 1–5 are primarily concerned with econometric estimation and examine the properties of estimators in finite and asymptotic samples. The first of these, by Michael Magdalinos and Mitsopoulos, derives a partial solution for the maximum likelihood normal equations in models with autoregressive conditionally heteroscedastic errors under the assumption that the errors belong to the Pearson family of distributions. It is shown through Monte Carlo simulations that there may be significant efficiency gains for maximum likelihood estimation compared to quasi maximum likelihood estimation. This is followed in Chapter 2 by Spanos who revisits the statistical foundations of instrumental variable (IV) estimation to ascertain the reliability and precision of instrumental variable-based inference. The paper stresses that the choice of instruments and the optimality of the resulting IV estimator entails both theoretical as well as statistical considerations. Chapter 3 is by Garry Phillips and takes another look at the problem of deriving moment approximations for two-stage least squares in the classical simultaneous equation model. In particular, approximations for the first and second moments are found in a simultaneous equation model in which the disturbances follow a system autoregressive scheme. The results are compared to Nagar's original approximations for serially independent disturbances. In Chapter 4, Smith is concerned to adapt the general empirical likelihood (GEL) unconditional moment methods developed earlier, to the conditional moment context. In particular, GEL estimators are developed which achieve the semi-parametric efficiency lower bound.
- Type
- Chapter
- Information
- The Refinement of Econometric Estimation and Test ProceduresFinite Sample and Asymptotic Analysis, pp. xxv - xxviiiPublisher: Cambridge University PressPrint publication year: 2007