Published Date:
1977
Publication:
New York: Academic Press
Pages:
374 pages
The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these examples; and the extensive use of Monte Carlo simulations to demonstrate particular aspects of the problems and estimators being considered. In terms of material being presented, the unique aspects include the first chapter which attempts to address the use of empirical methods in the social sciences, the seventh chapter which considers models with discrete dependent variables and unobserved variables. Clearly these last two topics in particular are quite advancedmore advanced than material that is currently available on the subject. These last two topics are also currently experiencing rapid development and are not adequately described in most other texts.
CONTENTS  
1  Empirical Analyses in the Social Sciences 
Introduction Social Science Theory and Statistical Models Fitting Models to Data The Development of Stochastic Model The Analysis of Nonexperimental Data and the Selection of a Statistical Procedure Simple Methods Review Questions 

2  Estimation with Simple Linear Models 
Introduction The Basic Model Least Squares Estimators Two Examples Conclusion Appendix: Properties of Summations Appendix: Calculus and the Minimization of Functions Review Questions 

3  Least Squares Estimators: Statistical Properties and Hypothesis Testing 
Introduction Properties of Least Squares Estimators Distribution of bA Monte Carlo Experiment Statistical Inference Hypothesis Tests for Schooling/Earnings Model Conclusion Appendix: Estimation of Schooling/Earnings Model Using SPSS Computer Program Review Questions 

4  Ordinary Least Squares in Practice 
Introduction Interpretation of Regression Coefficients Model Specification Model Specification and Multicollinearity in Practice Functional Forms Dummy Explanatory Variables Review Questions 

5  Multivariate Estimation in Matrix Form 
Introduction The Least Squares Estimators Least Squares in Matrix Notation Properties of Least Squares Distributional Aspects of the Error Term Statistical Inference Multivariate Education Example Multicollinearity Conclusion Appendix: Proof of Best Appendix: Proof of Unbiasedness of the Estimator of Variance Review Questions 

6  Generalized Least Squares 
Introduction Heteroskedasticity and Autocorrelation Formal Statement of the Problem Generalized Least Squares Generalized Least Squares and Examples of Heteroskedasticity and Autocorrelation Generalized Least Squares and Weighted Regression Monte Carlo Simulation of Generalized Least Squares Generalized Least Squares in Practice Visual Diagnostics Dynamic Models Conclusion Appendix: Derivation of Generalized Least Squares Estimator Appendix: Unbiased Estimator of Variance Review Questions 

7  Models with Discrete Dependent Variables 
Introduction The Problem of Estimating Models with Discrete Dependent Variables Alternative ModelsDichotomous Dependent Variables Logit Analysis grouped Data Logit AnalysisMicrodata Probit Analysis An Example Monte Carlo Simulation of Dichotomous Dependent Variables Polytomous Variables/Joint Distributions Conclusions 

8  Introduction to Multiequation Models 
Introduction Two Examples of Structural Systems Path Analysis The General Multiequation Model Estimating Hierarchical Models Hierarchical, Nonrecursive Systems Underidentification in Hierarchical Models Nonrecursive Hierarchical Models: Two Examples Conclusion Appendix: Instrumental Variables Estimator Review Questions 

9  Structural Equations: Simultaneous Models 
Introduction Identification in Simultaneous Systems: An Example Identification in Simultaneous Models Estimating Identified Models Simultaneous Equations: The Voting and Aspiration Examples Identification through Assumptions about Error Terms Alternative Estimators Summary and Conclusions Appendix: Variances and Covariances for Peer Influence Data Review Questions 

10  Estimating Models with Erroneous and Unobserved Variables 
Introduction Erroneous Explanatory Variables Unobserved Variables Factor Analysis Linear Structural Models and the General Analysis of Covariances Conclusion 

Appendix I Statistical Review  
Probability Theoretical Distributions Properties of Estimators Hypothesis Testing Maximum Likelihood (ML) Estimation 

Appendix II Matrix Algebra  
Basic Properties Basic Operations Matrix Multiplication Other Operations Systems of Linear Equations Inverses Existence of an InverseRank Review Questions for Appendix 11 

Appendix III Statistical Tables References  
References 
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