Econometrics

Inhaltsverzeichnis

• 1. Elementary Aspects of Multivariate Analysis.
• 1.1 Preliminaries.
• 1.2 Joint, Marginal, and Conditional Distributions.
• 1.3 A Mathematical Digression.
• 1.4 The Multivariate Normal Distribution.
• 1.5 Correlation Coefficients and Related Topics.
• 1.6 Estimators of the Mean Vector and Covariance Matrix and their Distribution.
• 1.7 Tests of Significance.
• 2. Applications of Multivariate Analysis.
• 2.1 Canonical Correlations and Canonical Variables.
• 2.2 Principal Components.
• 2.3 Discriminant Analysis.
• 2.4 Factor Analysis.
• 3. Probability Limits, Asymptotic Distributions, and Properties of Maximum Likelihood Estimators.
• 3.1 Introduction.
• 3.2 Estimators and Probability Limits.
• 3.3 Convergence to a Random Variable: Convergence in Distribution and Convergence of Moments.
• 3.4 Central Limit Theorems and Related Topics.
• 3.5 Miscellaneous Useful Convergence Results.
• 3.6 Properties of Maximum Likelihood (ML) Estimators.
• 3.7 Estimation for Distribution Admitting of Sufficient Statistics.
• 3.8 Minimum Variance Estimation and Sufficient Statistics.
• 4. Estimation of Simultaneous Equations Systems.
• 4.1 Review of Classical Methods.
• 4.2 Asymptotic Distribution of Aitken Estimators.
• 4.3 Two-Stage Least Squares (2SLS).
• 4.4 2SLS as Aitken and as OLS Estimator.
• 4.5 Asymptotic Properties of 2SLS Estimators.
• 4.6 The General k-Class Estimator.
• 4.7 Three-Stage Least Squares (3SLS).
• 5. Applications of Classical and Simultaneous Equations Techniques and Related Problems.
• 5.1 Estimation of Production and Cost Functions and Specification Error Analysis.
• 5.2 An Example of Efficient Estimation of a Set of General Linear (Regression) Models.
• 5.3 An Example of 2SLS and 3SLS Estimation.
• 5.4 Measures of Goodness of Fit in Multiple Equations Systems: Coeficient of (Vector) Alienationand Correlation.
• 5.5 Canonical Correlations and Goodness of Fit in Econometric Systems.
• 5.6 Applications of Principal Component Theory in Econometric Systems.
• 5.7 Alternative Asymptotic Tests of Significance for 2SLS Estimated Parameters.
• 6. Alternative Estimation Methods; Recursive Systems.
• 6.1 Introduction.
• 6.2 Indirect Least Squares (ILS).
• 6.3 The Identification Problem.
• 6.4 Instrumental Variables Estimation.
• 6.5 Recursive Systems.
• 7. Maximum Likelihood Methods.
• 7.1 Formulation of the Problem and Assumptions.
• 7.2 Reduced Form (RF) and Full Information Maximum Likelihood (FIML) Estimation.
• 7.3 Limited Information (LIML) Estimation.
• 8. Relations Among Estimators;. Monte Carlo Methods.
• 8.1 Introduction.
• 8.2 Relations Among Double k-Class Estimators.
• 8.3 I. V., ILS, and Double Ar-Class Estimators.
• 8.4 Limited Information Estimators and Just Identification.
• 8.5 Relationships Among Full Information Estimators.
• 8.6 Monte Carlo Methods.
• 9. Spectral Analysis.
• 9.1 Stochastic Processes.
• 9.2 Spectral Representation of Covariance Stationary Series.
• 9.3 Estimation of the Spectrum.
• 10. Cross-Spectral Analysis.
• 10.1 Introduction.
• 10.2 Cross Spectrum: Cospectrum, Quadrature Spectrum, and Coherency.
• 10.3 Estimation of the Cross Spectrum.
• 10.4 An Empirical Application of Cross-Spectral Analysis.
• 11. Approximate Sampling Distributions and Other Statistical Aspects of Spectral Analysis.
• 11.1 Aliasing.
• 11.2 “Prewhitening,” “Recoloring,” and Related Issues.
• 11.3 Approximate Asymptotic Distributions; Considerations of Design and Analysis.
• 12 Applications of Spectral Analysis to Simultaneous Equations Systems.
• 12.1 Generalities.
• 12.2 Lag Operators.
• 12.3 An Operator Representation of the Final Form.
• 12.4 Dynamic Multipliers and the Final Form.
• 12.5 Spectral Properties of the Final Form.
• 12.6 An Empirical Application.
• Mathematical Appendix.
• A.1 Complex Numbers and Complex-Valued Functions.
• A.2 The Riemann-Stieltjes Integral.
• A.3 Monotonie Functions and Functions of Bounded Variation.
• A.4 Fourier Series.
• A.5 Systems of Difference Equations with Constant Coefficients.
• A.6 Matrix Algebra.