Contents of Multivariate Reduced-Rank Regression, Theory and Applications

• 1. Multivariate Linear Regression
  
  1.1 Introduction
  1.2 Multivariate Linear Regression Model and Least Squares Estimator
  1.3 Further Inference Properties in the Multivariate Regression Model
  1.4 Prediction in the Multivariate Linear Regression Model
  1.5 A Numerical Example
  
  
• 2. Reduced-Rank Regression Model
  
  2.1 The Basic Reduced-Rank Model and Background
  2.2 Some Examples of Application of the Reduced-Rank Model
  2.3 Estimation of Parameters in the Reduced-Rank Model
  2.4 Relation to Principal Components and Canonical Correlation Analysis
  2.5 Asymptotic Distribution of Estimators in Reduced-Rank Model
  2.6 Identification of Rank of the Regression Coefficient Matrix
  2.7 Numerical Example Using Biochemical Data
  
  
• 3. Reduced-Rank Regression Models With Two Sets of Regressors
  
  3.1 Reduced-Rank Model of Anderson
  3.2 Application to One-Way ANOVA and Linear Discriminant Analysis
  3.3 Numerical Example Using Chemometrics Data
  3.4 Both Regression Matrices of Lower Ranks - Model and Its Applications
  3.5 Estimation and Inference for the Model
  3.6 Identification of Ranks of Coefficient Matrices
  3.7 An Example on Ozone Data
  
  
• 4. Reduced-Rank Regression Model With Autoregressive Errors
  
  4.1 Introduction and the Model
  4.2 Example on U.K. Economy - Basic Data and Their Descriptions
  4.3 Maximum Likelihood Estimators for the Model
  4.4 Computational Algorithms for Efficient Estimators
  4.5 Alternate Estimators and Their Properties
  4.6 Identification of Rank of the Regression Coefficient Matrix
  4.7 Inference for the Numerical Example
  
  
• 5. Multiple Time Series Modeling With Reduced Ranks
  
  5.1 Introduction and Time Series Models
  5.2 Reduced-Rank Autoregressive Models
  5.3 Extended and Nested Reduced-Rank Autoregressive Models
  5.4 Numerical Example on U.S. Hog Data
  5.5 Relationship Between Nonstationarity and Canonical Correlations
  5.6 Cointegration for Nonstationary Series - Reduced Rank in Long Term
  5.7 Unit Root and Cointegration Aspects for U.S. Hog Data Example
  
  
• 6. The Growth Curve Model and Reduced-Rank Regression Methods
  
  6.1 Introduction and the Growth Curve Model
  6.2 Estimation of Parameters in the Growth Curve Model
  6.3 Likelihood Ratio Testing of Linear Hypotheses in Growth Curve Model
  6.4 An Extended Model for Growth Curve Data
  6.5 Modification of Basic Growth Curve Model to Reduced-Rank Model
  6.6 Reduced-Rank Growth Curve Models
  6.7 A Numerical Example
  
  
• 7. Seemingly Unrelated Regressions Models With Reduced Ranks
  
  7.1 Introduction and the Seemingly Unrelated Regressions Model
  7.2 Relation of Growth Curve Model to the Seemingly Unrelated Regressions Model
  7.3 Reduced-Rank Coefficient in Seemingly Unrelated Regressions Model
  7.4 Maximum Likelihood Estimators for Reduced-Rank Model
  7.5 An Alternate Estimator and Its Properties
  7.6 Identification of Rank of the Regression Coefficient Matrix
  7.7 A Numerical Example With Scanner Data
  
  
• 8. Applications of Reduced-Rank Regression in Financial Economics
  
  8.1 Introduction to Asset Pricing Models
  8.2 Estimation and Testing in the Asset Pricing Model
  8.3 Additional Applications of Reduced-Rank Regression in Finance
  8.4 Empirical Studies and Results on Asset Pricing Models
  
  
• 9. Alternate Procedures for Analysis of Multivariate Regression Models
  
  
• Appendix: Data Sets
  
• References
  
• Subject Index
  
• Author Index