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Index
Cover Page
Title Page
Copyright Page
Preface
Contents
Detailed Contents
List of Exhibits
Abbreviations
Guide to the Book
Introduction
Econometrics
Purpose of the book
Characteristic features of the book
Target audience and required background knowledge
Brief contents of the book
Study advice
Teaching suggestions
Some possible course structures
1 Review of Statistics
1.1 Descriptive statistics
1.1.1 Data graphs
1.1.2 Sample statistics
1.2 Random variables
1.2.1 Single random variables
1.2.2 Joint random variables
1.2.3 Probability distributions
1.2.4 Normal random samples
1.3 Parameter estimation
1.3.1 Estimation methods
1.3.2 Statistical properties
1.3.3 Asymptotic properties
1.4 Tests of hypotheses
1.4.1 Size and power
1.4.2 Tests for mean and variance
1.4.3 Interval estimates and the bootstrap
Summary, further reading, and keywords
Exercises
2 Simple Regression
2.1 Least squares
2.1.1 Scatter diagrams
2.1.2 Least squares
2.1.3 Residuals and R2
2.1.4 Illustration: Bank Wages
2.2 Accuracy of least squares
2.2.1 Data generating processes
2.2.2 Examples of regression models
2.2.3 Seven assumptions
2.2.4 Statistical properties
2.2.5 Efficiency
2.3 Significance tests
2.3.1 The t-test
2.3.2 Examples
2.3.3 Use under less strict conditions
2.4 Prediction
2.4.1 Point predictions and prediction intervals
2.4.2 Examples
Summary, further reading, and keywords
Exercises
3 Multiple Regression
3.1 Least squares in matrix form
3.1.1 Introduction
3.1.2 Least squares
3.1.3 Geometric interpretation
3.1.4 Statistical properties
3.1.5 Estimating the disturbance variance
3.1.6 Coefficient of determination
3.1.7 Illustration: Bank Wages
3.2 Adding or deleting variables
3.2.1 Restricted and unrestricted models
3.2.2 Interpretation of regression coefficients
3.2.3 Omitting variables
3.2.4 Consequences of redundant variables
3.2.5 Partial regression
3.3 The accuracy of estimates
3.3.1 The t-test
3.3.2 Illustration: Bank Wages
3.3.3 Multicollinearity
3.3.4 Illustration: Bank Wages
3.4 The F-test
3.4.1 The F-test in different forms
3.4.2 Illustration: Bank Wages
3.4.3 Chow forecast test
3.4.4 Illustration: Bank Wages
Summary, further reading, and keywords
Exercises
4 Non-Linear Methods
4.1 Asymptotic analysis
4.1.1 Introduction
4.1.2 Stochastic regressors
4.1.3 Consistency
4.1.4 Asymptotic normality
4.1.5 Simulation examples
4.2 Non-linear regression
4.2.1 Motivation
4.2.2 Non-linear least squares
4.2.3 Non-linear optimization
4.2.4 The Lagrange Multiplier test
4.2.5 Illustration: Coffee Sales
4.3 Maximum likelihood
4.3.1 Motivation
4.3.2 Maximum likelihood estimation
4.3.3 Asymptotic properties
4.3.4 The Likelihood Ratio test
4.3.5 The Wald test
4.3.6 The Lagrange Multiplier test
4.3.7 LM-test in the linear model
4.3.8 Remarks on tests
4.3.9 Two examples
4.4 Generalized method of moments
4.4.1 Motivation
4.4.2 GMM estimation
4.4.3 GMM standard errors
4.4.4 Quasi-maximum likelihood
4.4.5 GMM in simple regression
4.4.6 Illustration: Stock Market Returns
Summary, further reading, and keywords
Exercises
5 Diagnostic Tests and Model Adjustments
5.1 Introduction
5.2 Functional form and explanatory variables
5.2.1 The number of explanatory variables
5.2.2 Non-linear functional forms
5.2.3 Non-parametric estimation
5.2.4 Data transformations
5.2.5 Summary
5.3 Varying parameters
5.3.1 The use of dummy variables
5.3.2 Recursive least squares
5.3.3 Tests for varying parameters
5.3.4 Summary
5.4 Heteroskedasticity
5.4.1 Introduction
5.4.2 Properties of OLS and White standard errors
5.4.3 Weighted least squares
5.4.4 Estimation by maximum likelihood and feasible WLS
5.4.5 Tests for homoskedasticity
5.4.6 Summary
5.5 Serial correlation
5.5.1 Introduction
5.5.2 Properties of OLS
5.5.3 Tests for serial correlation
5.5.4 Model adjustments
5.5.5 Summary
5.6 Disturbance distribution
5.6.1 Introduction
5.6.2 Regression diagnostics
5.6.3 Test for normality
5.6.4 Robust estimation
5.6.5 Summary
5.7 Endogenous regressors and instrumental variables
5.7.1 Instrumental variables and two-stage least squares
5.7.2 Statistical properties of IV estimators
5.7.3 Tests for exogeneity and validity of instruments
5.7.4 Summary
5.8 Illustration: Salaries of top managers
Summary, further reading, and keywords
Exercises
6 Qualitative and Limited Dependent Variables
6.1 Binary response
6.1.1 Model formulation
6.1.2 Probit and logit models
6.1.3 Estimation and evaluation
6.1.4 Diagnostics
6.1.5 Model for grouped data
6.1.6 Summary
6.2 Multinomial data
6.2.1 Unordered response
6.2.2 Multinomial and conditional logit
6.2.3 Ordered response
6.2.4 Summary
6.3 Limited dependent variables
6.3.1 Truncated samples
6.3.2 Censored data
6.3.3 Models for selection and treatment effects
6.3.4 Duration models
6.3.5 Summary
Summary, further reading, and keywords
Exercises
7 Time Series and Dynamic Models
7.1 Models for stationary time series
7.1.1 Introduction
7.1.2 Stationary processes
7.1.3 Autoregressive models
7.1.4 ARMA models
7.1.5 Autocorrelations and partial autocorrelations
7.1.6 Forecasting
7.1.7 Summary
7.2 Model estimation and selection
7.2.1 The modelling process
7.2.2 Parameter estimation
7.2.3 Model selection
7.2.4 Diagnostic tests
7.2.5 Summary
7.3 Trends and seasonals
7.3.1 Trend models
7.3.2 Trend estimation and forecasting
7.3.3 Unit root tests
7.3.4 Seasonality
7.3.5 Summary
7.4 Non-linearities and time-varying volatility
7.4.1 Outliers
7.4.2 Time-varying parameters
7.4.3 GARCH models for clustered volatility
7.4.4 Estimation and diagnostic tests of GARCH models
7.4.5 Summary
7.5 Regression models with lags
7.5.1 Autoregressive models with distributed lags
7.5.2 Estimation, testing, and forecasting
7.5.3 Regression of variables with trends
7.5.4 Summary
7.6 Vector autoregressive models
7.6.1 Stationary vector autoregressions
7.6.2 Estimation and diagnostic tests of stationary VAR models
7.6.3 Trends and cointegration
7.6.4 Summary
7.7 Other multiple equation models
7.7.1 Introduction
7.7.2 Seemingly unrelated regression model
7.7.3 Panel data
7.7.4 Simultaneous equation model
7.7.5 Summary
Summary, further reading, and keywords
Exercises
Appendix A. Matrix Methods
A.1 Summations
A.2 Vectors and matrices
A.3 Matrix addition and multiplication
A.4 Transpose, trace, and inverse
A.5 Determinant, rank, and eigenvalues
A.6 Positive (semi)definite matrices and projections
A.7 Optimization of a function of several variables
A.8 Concentration and the Lagrange method
Exercise
Appendix B. Data Sets
List of Data Sets
Index
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