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) Alienation and 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.