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Applied Econometrics Using the SAS® System

Book Description

The first cutting-edge guide to using the SAS® system for the analysis of econometric data

Applied Econometrics Using the SAS® System is the first book of its kind to treat the analysis of basic econometric data using SAS®, one of the most commonly used software tools among today's statisticians in business and industry. This book thoroughly examines econometric methods and discusses how data collected in economic studies can easily be analyzed using the SAS® system.

In addition to addressing the computational aspects of econometric data analysis, the author provides a statistical foundation by introducing the underlying theory behind each method before delving into the related SAS® routines. The book begins with a basic introduction to econometrics and the relationship between classical regression analysis models and econometric models. Subsequent chapters balance essential concepts with SAS® tools and cover key topics such as:

  • Regression analysis using Proc IML and Proc Reg

  • Hypothesis testing

  • Instrumental variables analysis, with a discussion of measurement errors, the assumptions incorporated into the analysis, and specification tests

  • Heteroscedasticity, including GLS and FGLS estimation, group-wise heteroscedasticity, and GARCH models

  • Panel data analysis

  • Discrete choice models, along with coverage of binary choice models and Poisson regression

  • Duration analysis models

Assuming only a working knowledge of SAS®, this book is a one-stop reference for using the software to analyze econometric data. Additional features include complete SAS® code, Proc IML routines plus a tutorial on Proc IML, and an appendix with additional programs and data sets. Applied Econometrics Using the SAS® System serves as a relevant and valuable reference for practitioners in the fields of business, economics, and finance. In addition, most students of econometrics are taught using GAUSS and STATA, yet SAS® is the standard in the working world; therefore, this book is an ideal supplement for upper-undergraduate and graduate courses in statistics, economics, and other social sciences since it prepares readers for real-world careers.

Table of Contents

  1. Cover
  2. Title page
  3. Copyright page
  4. Dedication
  5. Preface
  6. Acknowledgments
  7. 1: Introduction to Regression Analysis
    1. 1.1 Introduction
    2. 1.2 Matrix Form of the Multiple Regression Model
    3. 1.3 Basic Theory of Least Squares
    4. 1.4 Analysis of Variance
    5. 1.5 The Frisch–Waugh Theorem
    6. 1.6 Goodness of Fit
    7. 1.7 Hypothesis Testing and Confidence Intervals
    8. 1.8 Some Further Notes
  8. 2: Regression Analysis Using Proc IML and Proc Reg
    1. 2.1 Introduction
    2. 2.2 Regression Analysis Using Proc IML
    3. 2.3 Analyzing the Data Using Proc Reg
    4. 2.4 Extending the Investment Equation Model to the Complete Data Set
    5. 2.5 Plotting the Data
    6. 2.6 Correlation Between Variables
    7. 2.7 Predictions of the Dependent Variable
    8. 2.8 Residual Analysis
    9. 2.9 Multicollinearity
  9. 3: Hypothesis Testing
    1. 3.1 Introduction
    2. 3.2 Using SAS to Conduct the General Linear Hypothesis
    3. 3.3 The Restricted Least Squares Estimator
    4. 3.4 Alternative Methods of Testing the General Linear Hypothesis
    5. 3.5 Testing for Structural Breaks in Data
    6. 3.6 The CUSUM Test
    7. 3.7 Models with Dummy Variables
  10. 4: Instrumental Variables
    1. 4.1 Introduction
    2. 4.2 Omitted Variable Bias
    3. 4.3 Measurement Errors
    4. 4.4 Instrumental Variable Estimation
    5. 4.5 Specification Tests
  11. 5: Nonspherical Disturbances and Heteroscedasticity
    1. 5.1 Introduction
    2. 5.2 Nonspherical Disturbances
    3. 5.3 Detecting Heteroscedasticity
    4. 5.4 Formal Hypothesis Tests to Detect Heteroscedasticity
    5. 5.5 Estimation of β Revisited
    6. 5.6 Weighted Least Squares and FGLS Estimation
    7. 5.7 Autoregressive Conditional Heteroscedasticity
  12. 6: Autocorrelation
    1. 6.1 Introduction
    2. 6.2 Problems Associated with OLS Estimation Under Autocorrelation
    3. 6.3 Estimation Under the Assumption of Serial Correlation
    4. 6.4 Detecting Autocorrelation
    5. 6.5 Using SAS to Fit the AR Models
  13. 7: Panel Data Analysis
    1. 7.1 What is Panel Data?
    2. 7.2 Panel Data Models
    3. 7.3 The Pooled Regression Model
    4. 7.4 The Fixed Effects Model
    5. 7.5 Random Effects Models
  14. 8: Systems of Regression Equations
    1. 8.1 Introduction
    2. 8.2 Estimation Using Generalized Least Squares
    3. 8.3 Special Cases of the Seemingly Unrelated Regression Model
    4. 8.4 Feasible Generalized Least Squares
  15. 9: Simultaneous Equations
    1. 9.1 Introduction
    2. 9.2 Problems with OLS Estimation
    3. 9.3 Structural and Reduced Form Equations
    4. 9.4 The Problem of Identification
    5. 9.5 Estimation of Simultaneous Equation Models
    6. 9.6 Hausman’s Specification Test
  16. 10: Discrete Choice Models
    1. 10.1 Introduction
    2. 10.2 Binary Response Models
    3. 10.3 Poisson Regression
  17. 11: Duration Analysis
    1. 11.1 Introduction
    2. 11.2 Failure Times and Censoring
    3. 11.3 The Survival and Hazard Functions
    4. 11.4 Commonly Used Distribution Functions in Duration Analysis
    5. 11.5 Regression Analysis with Duration Data
  18. 12: Special Topics
    1. 12.1 Iterative FGLS Estimation Under Heteroscedasticity
    2. 12.2 Maximum Likelihood Estimation Under Heteroscedasticity
    3. 12.3 Harvey’s Multiplicative Heteroscedasticity
    4. 12.4 Groupwise Heteroscedasticity
    5. 12.5 Hausman–Taylor Estimator for the Random Effects Model
    6. 12.6 Robust Estimation of Covariance Matrices in Panel Data
    7. 12.7 Dynamic Panel Data Models
    8. 12.8 Heterogeneity and Autocorrelation in Panel Data Models
    9. 12.9 Autocorrelation in Panel Data
  19. Appendix A: Basic Matrix Algebra for Econometrics
    1. A.1 Matrix Definitions
    2. A.2 Matrix Operations
    3. A.3 Basic Laws of Matrix Algebra
    4. A.4 Identity Matrix
    5. A.5 Transpose of a Matrix
    6. A.6 Determinants
    7. A.7 Trace of a Matrix
    8. A.8 Matrix Inverses
    9. A.9 Idempotent Matrices
    10. A.10 Kronecker Products
    11. A.11 Some Common Matrix Notations
    12. A.12 Linear Dependence and Rank
    13. A.13 Differential Calculus in Matrix Algebra
    14. A.14 Solving a System of Linear Equations in Proc IML
  20. Appendix B: Basic Matrix Operations in Proc IML
    1. B.1 Assigning Scalars
    2. B.2 Creating Matrices and Vectors
    3. B.3 Elementary Matrix Operations
    4. B.4 Comparison Operators
    5. B.5 Matrix-Generating Functions
    6. B.6 Subset of Matrices
    7. B.7 Subscript Reduction Operators
    8. B.8 The Diag and VecDiag Commands
    9. B.9 Concatenation of Matrices
    10. B.10 Control Statements
    11. B.11 Calculating Summary Statistics in Proc IML
  21. Appendix C: Simulating the Large Sample Properties of the OLS Estimators
  22. Appendix D: Introduction to Bootstrap Estimation
    1. D.1 Introduction
    2. D.2 Calculating Standard Errors
    3. D.3 Bootstrapping in SAS
    4. D.4 Bootstrapping in Regression Analysis
  23. Appendix E: Complete Programs and Proc IML Routines
    1. E.1 Program 1
    2. E.2 Program 2
    3. E.3 Program 3
    4. E.4 Program 4
    5. E.5 Program 5
    6. E.6 Program 6
    7. E.7 Program 7
    8. E.8 Program 8
    9. E.9 Program 9
    10. E.10 Program 10
    11. E.11 Program 11
    12. E.12 Program 12
    13. E.13 Program 13
    14. E.14 Program 14
    15. E.15 Program 15
    16. E.16 Program 16
    17. E.17 Program 17
  24. References
  25. Index