Introduction to the Theory and Practice of Econometrics

Autores: George G. Judge, R. Carter Hill, William E. Griffiths, Helmut Lütkepohl, Tsoung-Chao Lee
Edição: 1982
Editora: Wiley (John Wiley & Sons)
ISBN: 0-471-08277-5
Série: Wiley Series in Probability and Mathematical Statistics
Título: Introduction to The Theory and Practice of Econometrics
Contents:
Statistical Tables
Chapter 1: Introduction
Part 1: Foundations: Statistical Model Specification, Estimation, and Inference
Chapter 2: Analysis of a Sample of Data
Chapter 3: Analysis of a Sample from Normal Population
Chapter 4: Interval Estimation and Hypothesis Testing in the Normal Linear Model
Chapter 5: The Bayesian Approach to Estimating the Mean and Variance of a Normal Population
Part 2: The General Linear Statistical Model
Chapter 6: The General Linear Statistical Model
Chapter 7: The Normal General Linear Statistical Model
Chapter 8: Bayesian Estimation and Inference for the Normal Linear Statistical Model
Part 3: The Generalized Linear Statistical Model
Chapter 9: Linear Stochastic Regressor Models and Asymptotic Theory
Chapter 10: General Linear Statistical Model with Non-Scalar Identity Covariance Matrix
Chapter 11: Disturbance-Related Sets of regression Equations
Part 4: Simultaneous Linear Statistical Models
Chapter 12: An Introduction to Simultaneous Linear Statistical Models
Chapter 13: Estimation and Inference for Simultaneous Linear Statistical Models
Part 5: Some Procedures for Handling an Unknown Covariance Matrix
Chapter 14: Heteroscedasticity
Chapter 15: Autocorrelation
Part 6: Pooling of Data and Varying Parameter Models
Chapter 16: Using Time Series and Cross-sectional Data
Chapter 17: Variable Parameter Models
Part 7: Unobservable and Qualitative Variables
Chapter 18: Models with Qualitative or Limited Dependent Variables
Chapter 19: Unobservable Variables
Part 8: Nonsample Information, Biased Estimation, and Choosing the Dimension and Form of the Design Matrix
Chapter 20: The Use of Nonsample Information
Chapter 21: Biased Information
Chapter 22: Model Specification - Variable Selection
Chapter 23: Multicollinearity
Part 9: The Nonlinear Statistical Model
Chapter 24: Nonlinear Regression Models
Part 10: Time Series and Distributed Lag Models
Chapter 25: Time Series Analysis and Forecasting
Chapter 26: Analysis of Bivariate Time Series
Chapter 27: Distributed Lag Models
Chapter 28: Summary of Statistical Models, Estimators and Tests
Index