Marno Verbeek
A Guide to Modern Econometrics
Marno Verbeek
A Guide to Modern Econometrics
- Gebundenes Buch
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
The first edition of 'A Guide to Modern Econometrics' has become established as a successful textbook in a crowded and competitive market. It's focus on exploring alternative techniques, combined with a practical emphasis has been appreciated by many readers. In this new edition, Marno Verbeek has built on the strengths of the first edition and brought the text completely up-to-date. Revisions to this new edition include:
- Additional full-length illustrations
- New section on count data models
- Additional tests including the Chow test for parameter stability
- More attention paid…mehr
The first edition of 'A Guide to Modern Econometrics' has become established as a successful textbook in a crowded and competitive market. It's focus on exploring alternative techniques, combined with a practical emphasis has been appreciated by many readers. In this new edition, Marno Verbeek has built on the strengths of the first edition and brought the text completely up-to-date. Revisions to this new edition include:
- Additional full-length illustrations
- New section on count data models
- Additional tests including the Chow test for parameter stability
- More attention paid to robust inference relevant for count data and GARCH intervals
- Additional full-length illustrations
- New section on count data models
- Additional tests including the Chow test for parameter stability
- More attention paid to robust inference relevant for count data and GARCH intervals
Produktdetails
- Produktdetails
- Verlag: Interscience Publishers [Division of John Wiley]
- 2nd edition
- Englisch
- ISBN-13: 9780470857731
- ISBN-10: 0470857730
- Artikelnr.: 09409516
- Verlag: Interscience Publishers [Division of John Wiley]
- 2nd edition
- Englisch
- ISBN-13: 9780470857731
- ISBN-10: 0470857730
- Artikelnr.: 09409516
M. Verbeek, Erasmus University Rotterdam, The Netherlands
Marno Verbeek is Professor of Finance at the Rotterdam School of Management and the Econometric Institute of Erasmus University, Rotterdam. He held previous positions at KU Leuven and Tilburg University, and visiting appointments at Trinity College Dublin and Universite Pantheon-Assas Paris II. He has published in a wide variety of international journals.
Marno Verbeek is Professor of Finance at the Rotterdam School of Management and the Econometric Institute of Erasmus University, Rotterdam. He held previous positions at KU Leuven and Tilburg University, and visiting appointments at Trinity College Dublin and Universite Pantheon-Assas Paris II. He has published in a wide variety of international journals.
Preface
1 Introduction
1.1 About Econometrics
1.2 The Structure of this Book
1.3 Illustrations and Exercises
2 An Introduction to Linear Regression
2.1 Ordinary Least Squares as an Algebraic Tool
2.2 The Linear Regression Model
2.3 Small Sample Properties of the OLS Estimator
2.4 Goodness-of-fit
2.5 Hypothesis Testing
2.6 Asymptotic Properties of the OLS Estimator
2.7 Illustration: The Capital Asset Pricing Model
2.8 Multicollinearity
2.9 Prediction
Exercises
3 Interpreting and Comparing Regression Models
3.1 Interpreting the Linear Model
3.2 Selecting the Set of Regressors
3.3 Misspecifying the Functional Form
3.4 Illustration: Explaining House Prices
3.5 Illustration: Explaining Individual Wages
Exercises
4 Heteroskedasticity and Autocorrelation
4.1 Consequences for the OLS Estimator
4.2 Deriving an Alternative Estimator
4.3 Heteroskedasticity
4.4 Testing for Heteroskedasticity
4.5 Illustration: Explaining Labour Demand
4.6 Autocorrelation
4.7 Testing for First Order Autocorrelation
4.8 Illustration: The Demand for Ice Cream
4.9 Alternative Autocorrelation Patterns
4.10 What to do When you Find Autocorrelation?
4.11 Illustration: Risk Premia in Foreign Exchange Markets
Exercises
5 Endogeneity
- Instrumental Variables and GMM
5.1 A Review of the Properties of the OLS Estimator
5.2 Cases Where the OLS Estimator Cannot be Saved
5.3 The Instrumental Variables Estimator
5.4 Illustration: Estimating the Returns to Schooling
5.5 The Generalized Instrumental Variables Estimator
5.6 The Generalized Method of Moments
5.7 Illustration: Estimating Intertemporal Asset Pricing Models
5.8 Concluding Remarks
Exercises
6 Maximum Likelihood Estimation and Specification Tests
6.1 An Introduction to Maximum Likelihood
6.2 Specification Tests
6.3 Tests in the Normal Linear Regression Model
6.4 Quasi-maximum Likelihood and Moment Conditions Tests
Exercises
7 Models with Limited Dependent Variables
7.1 Binary Choice Models
7.2 Multi-response Models
7.3 Models for Count Data
7.4 Tobit Models
7.5 Extensions of Tobit Models
7.6 Sample Selection Bias
7.7 Estimating Treatment Effects
7.8 Duration Models
Exercises
8 Univariate Time Series Models
8.1 Introduction
8.2 General ARMA Processes
8.3 Stationarity and Unit Roots
8.4 Testing for Unit Roots
8.5 Illustration: Long-run Purchasing Power Parity (Part 1)
8.6 Estimation of ARMA Models
8.7 Choosing a Model
8.8 Predicting with ARMA Models
8.9 Illustration: The Expectations Theory of the Term Structure
8.10 Autoregressive Conditional Heteroskedasticity
8.11 What about Multivariate Models?
Exercises
9 Multivariate Time Series Models
9.1 Dynamic Models with Stationary Variables
9.2 Models with Nonstationary Variables
9.3 Illustration: Long-run Purchasing Power Parity (Part 2)
9.4 Vector Autoregressive Models
9.5 Cointegration: the Multivariate Case
9.6 Illustration: Money Demand and Inflation
9.7 Concluding Remarks
Exercises
10 Models Based on Panel Data
10.1 Advantages of Panel Data
10.2 The Static Linear Model
10.3 Illustration: Explaining Individual Wages
10.4 Dynamic Linear Models
10.5 Illustration: Wage Elasticities of Labour Demand
10.6 Nonstationarity
- Unit Roots and Cointegration
10.7 Models with Limited Dependent Variables
10.8 Incomplete Panels and Selection Bias
Exercises
A Vectors and Matrices
A.1 Terminology
A.2 Matrix Manipulations
A.3 Properties of Matrices and Vectors
A.4 Inverse Matrices
A.5 Idempotent Matrices
A.6 Eigenvalues and Eigenvectors
A.7 Differentiation
A.8 Some Least Squares Manipulations
B Statistical and Distribution Theory
B.1 Discrete Random Variables
B.2 Continuous Random Variables
B.3 Expectations and Moments
B.4 Multivariate Distributions
B.5 Conditional Distributions
B.6 The Normal Distribution
B.7 Related Distributions
Bibliography
Index
1 Introduction
1.1 About Econometrics
1.2 The Structure of this Book
1.3 Illustrations and Exercises
2 An Introduction to Linear Regression
2.1 Ordinary Least Squares as an Algebraic Tool
2.2 The Linear Regression Model
2.3 Small Sample Properties of the OLS Estimator
2.4 Goodness-of-fit
2.5 Hypothesis Testing
2.6 Asymptotic Properties of the OLS Estimator
2.7 Illustration: The Capital Asset Pricing Model
2.8 Multicollinearity
2.9 Prediction
Exercises
3 Interpreting and Comparing Regression Models
3.1 Interpreting the Linear Model
3.2 Selecting the Set of Regressors
3.3 Misspecifying the Functional Form
3.4 Illustration: Explaining House Prices
3.5 Illustration: Explaining Individual Wages
Exercises
4 Heteroskedasticity and Autocorrelation
4.1 Consequences for the OLS Estimator
4.2 Deriving an Alternative Estimator
4.3 Heteroskedasticity
4.4 Testing for Heteroskedasticity
4.5 Illustration: Explaining Labour Demand
4.6 Autocorrelation
4.7 Testing for First Order Autocorrelation
4.8 Illustration: The Demand for Ice Cream
4.9 Alternative Autocorrelation Patterns
4.10 What to do When you Find Autocorrelation?
4.11 Illustration: Risk Premia in Foreign Exchange Markets
Exercises
5 Endogeneity
- Instrumental Variables and GMM
5.1 A Review of the Properties of the OLS Estimator
5.2 Cases Where the OLS Estimator Cannot be Saved
5.3 The Instrumental Variables Estimator
5.4 Illustration: Estimating the Returns to Schooling
5.5 The Generalized Instrumental Variables Estimator
5.6 The Generalized Method of Moments
5.7 Illustration: Estimating Intertemporal Asset Pricing Models
5.8 Concluding Remarks
Exercises
6 Maximum Likelihood Estimation and Specification Tests
6.1 An Introduction to Maximum Likelihood
6.2 Specification Tests
6.3 Tests in the Normal Linear Regression Model
6.4 Quasi-maximum Likelihood and Moment Conditions Tests
Exercises
7 Models with Limited Dependent Variables
7.1 Binary Choice Models
7.2 Multi-response Models
7.3 Models for Count Data
7.4 Tobit Models
7.5 Extensions of Tobit Models
7.6 Sample Selection Bias
7.7 Estimating Treatment Effects
7.8 Duration Models
Exercises
8 Univariate Time Series Models
8.1 Introduction
8.2 General ARMA Processes
8.3 Stationarity and Unit Roots
8.4 Testing for Unit Roots
8.5 Illustration: Long-run Purchasing Power Parity (Part 1)
8.6 Estimation of ARMA Models
8.7 Choosing a Model
8.8 Predicting with ARMA Models
8.9 Illustration: The Expectations Theory of the Term Structure
8.10 Autoregressive Conditional Heteroskedasticity
8.11 What about Multivariate Models?
Exercises
9 Multivariate Time Series Models
9.1 Dynamic Models with Stationary Variables
9.2 Models with Nonstationary Variables
9.3 Illustration: Long-run Purchasing Power Parity (Part 2)
9.4 Vector Autoregressive Models
9.5 Cointegration: the Multivariate Case
9.6 Illustration: Money Demand and Inflation
9.7 Concluding Remarks
Exercises
10 Models Based on Panel Data
10.1 Advantages of Panel Data
10.2 The Static Linear Model
10.3 Illustration: Explaining Individual Wages
10.4 Dynamic Linear Models
10.5 Illustration: Wage Elasticities of Labour Demand
10.6 Nonstationarity
- Unit Roots and Cointegration
10.7 Models with Limited Dependent Variables
10.8 Incomplete Panels and Selection Bias
Exercises
A Vectors and Matrices
A.1 Terminology
A.2 Matrix Manipulations
A.3 Properties of Matrices and Vectors
A.4 Inverse Matrices
A.5 Idempotent Matrices
A.6 Eigenvalues and Eigenvectors
A.7 Differentiation
A.8 Some Least Squares Manipulations
B Statistical and Distribution Theory
B.1 Discrete Random Variables
B.2 Continuous Random Variables
B.3 Expectations and Moments
B.4 Multivariate Distributions
B.5 Conditional Distributions
B.6 The Normal Distribution
B.7 Related Distributions
Bibliography
Index
Preface
1 Introduction
1.1 About Econometrics
1.2 The Structure of this Book
1.3 Illustrations and Exercises
2 An Introduction to Linear Regression
2.1 Ordinary Least Squares as an Algebraic Tool
2.2 The Linear Regression Model
2.3 Small Sample Properties of the OLS Estimator
2.4 Goodness-of-fit
2.5 Hypothesis Testing
2.6 Asymptotic Properties of the OLS Estimator
2.7 Illustration: The Capital Asset Pricing Model
2.8 Multicollinearity
2.9 Prediction
Exercises
3 Interpreting and Comparing Regression Models
3.1 Interpreting the Linear Model
3.2 Selecting the Set of Regressors
3.3 Misspecifying the Functional Form
3.4 Illustration: Explaining House Prices
3.5 Illustration: Explaining Individual Wages
Exercises
4 Heteroskedasticity and Autocorrelation
4.1 Consequences for the OLS Estimator
4.2 Deriving an Alternative Estimator
4.3 Heteroskedasticity
4.4 Testing for Heteroskedasticity
4.5 Illustration: Explaining Labour Demand
4.6 Autocorrelation
4.7 Testing for First Order Autocorrelation
4.8 Illustration: The Demand for Ice Cream
4.9 Alternative Autocorrelation Patterns
4.10 What to do When you Find Autocorrelation?
4.11 Illustration: Risk Premia in Foreign Exchange Markets
Exercises
5 Endogeneity
- Instrumental Variables and GMM
5.1 A Review of the Properties of the OLS Estimator
5.2 Cases Where the OLS Estimator Cannot be Saved
5.3 The Instrumental Variables Estimator
5.4 Illustration: Estimating the Returns to Schooling
5.5 The Generalized Instrumental Variables Estimator
5.6 The Generalized Method of Moments
5.7 Illustration: Estimating Intertemporal Asset Pricing Models
5.8 Concluding Remarks
Exercises
6 Maximum Likelihood Estimation and Specification Tests
6.1 An Introduction to Maximum Likelihood
6.2 Specification Tests
6.3 Tests in the Normal Linear Regression Model
6.4 Quasi-maximum Likelihood and Moment Conditions Tests
Exercises
7 Models with Limited Dependent Variables
7.1 Binary Choice Models
7.2 Multi-response Models
7.3 Models for Count Data
7.4 Tobit Models
7.5 Extensions of Tobit Models
7.6 Sample Selection Bias
7.7 Estimating Treatment Effects
7.8 Duration Models
Exercises
8 Univariate Time Series Models
8.1 Introduction
8.2 General ARMA Processes
8.3 Stationarity and Unit Roots
8.4 Testing for Unit Roots
8.5 Illustration: Long-run Purchasing Power Parity (Part 1)
8.6 Estimation of ARMA Models
8.7 Choosing a Model
8.8 Predicting with ARMA Models
8.9 Illustration: The Expectations Theory of the Term Structure
8.10 Autoregressive Conditional Heteroskedasticity
8.11 What about Multivariate Models?
Exercises
9 Multivariate Time Series Models
9.1 Dynamic Models with Stationary Variables
9.2 Models with Nonstationary Variables
9.3 Illustration: Long-run Purchasing Power Parity (Part 2)
9.4 Vector Autoregressive Models
9.5 Cointegration: the Multivariate Case
9.6 Illustration: Money Demand and Inflation
9.7 Concluding Remarks
Exercises
10 Models Based on Panel Data
10.1 Advantages of Panel Data
10.2 The Static Linear Model
10.3 Illustration: Explaining Individual Wages
10.4 Dynamic Linear Models
10.5 Illustration: Wage Elasticities of Labour Demand
10.6 Nonstationarity
- Unit Roots and Cointegration
10.7 Models with Limited Dependent Variables
10.8 Incomplete Panels and Selection Bias
Exercises
A Vectors and Matrices
A.1 Terminology
A.2 Matrix Manipulations
A.3 Properties of Matrices and Vectors
A.4 Inverse Matrices
A.5 Idempotent Matrices
A.6 Eigenvalues and Eigenvectors
A.7 Differentiation
A.8 Some Least Squares Manipulations
B Statistical and Distribution Theory
B.1 Discrete Random Variables
B.2 Continuous Random Variables
B.3 Expectations and Moments
B.4 Multivariate Distributions
B.5 Conditional Distributions
B.6 The Normal Distribution
B.7 Related Distributions
Bibliography
Index
1 Introduction
1.1 About Econometrics
1.2 The Structure of this Book
1.3 Illustrations and Exercises
2 An Introduction to Linear Regression
2.1 Ordinary Least Squares as an Algebraic Tool
2.2 The Linear Regression Model
2.3 Small Sample Properties of the OLS Estimator
2.4 Goodness-of-fit
2.5 Hypothesis Testing
2.6 Asymptotic Properties of the OLS Estimator
2.7 Illustration: The Capital Asset Pricing Model
2.8 Multicollinearity
2.9 Prediction
Exercises
3 Interpreting and Comparing Regression Models
3.1 Interpreting the Linear Model
3.2 Selecting the Set of Regressors
3.3 Misspecifying the Functional Form
3.4 Illustration: Explaining House Prices
3.5 Illustration: Explaining Individual Wages
Exercises
4 Heteroskedasticity and Autocorrelation
4.1 Consequences for the OLS Estimator
4.2 Deriving an Alternative Estimator
4.3 Heteroskedasticity
4.4 Testing for Heteroskedasticity
4.5 Illustration: Explaining Labour Demand
4.6 Autocorrelation
4.7 Testing for First Order Autocorrelation
4.8 Illustration: The Demand for Ice Cream
4.9 Alternative Autocorrelation Patterns
4.10 What to do When you Find Autocorrelation?
4.11 Illustration: Risk Premia in Foreign Exchange Markets
Exercises
5 Endogeneity
- Instrumental Variables and GMM
5.1 A Review of the Properties of the OLS Estimator
5.2 Cases Where the OLS Estimator Cannot be Saved
5.3 The Instrumental Variables Estimator
5.4 Illustration: Estimating the Returns to Schooling
5.5 The Generalized Instrumental Variables Estimator
5.6 The Generalized Method of Moments
5.7 Illustration: Estimating Intertemporal Asset Pricing Models
5.8 Concluding Remarks
Exercises
6 Maximum Likelihood Estimation and Specification Tests
6.1 An Introduction to Maximum Likelihood
6.2 Specification Tests
6.3 Tests in the Normal Linear Regression Model
6.4 Quasi-maximum Likelihood and Moment Conditions Tests
Exercises
7 Models with Limited Dependent Variables
7.1 Binary Choice Models
7.2 Multi-response Models
7.3 Models for Count Data
7.4 Tobit Models
7.5 Extensions of Tobit Models
7.6 Sample Selection Bias
7.7 Estimating Treatment Effects
7.8 Duration Models
Exercises
8 Univariate Time Series Models
8.1 Introduction
8.2 General ARMA Processes
8.3 Stationarity and Unit Roots
8.4 Testing for Unit Roots
8.5 Illustration: Long-run Purchasing Power Parity (Part 1)
8.6 Estimation of ARMA Models
8.7 Choosing a Model
8.8 Predicting with ARMA Models
8.9 Illustration: The Expectations Theory of the Term Structure
8.10 Autoregressive Conditional Heteroskedasticity
8.11 What about Multivariate Models?
Exercises
9 Multivariate Time Series Models
9.1 Dynamic Models with Stationary Variables
9.2 Models with Nonstationary Variables
9.3 Illustration: Long-run Purchasing Power Parity (Part 2)
9.4 Vector Autoregressive Models
9.5 Cointegration: the Multivariate Case
9.6 Illustration: Money Demand and Inflation
9.7 Concluding Remarks
Exercises
10 Models Based on Panel Data
10.1 Advantages of Panel Data
10.2 The Static Linear Model
10.3 Illustration: Explaining Individual Wages
10.4 Dynamic Linear Models
10.5 Illustration: Wage Elasticities of Labour Demand
10.6 Nonstationarity
- Unit Roots and Cointegration
10.7 Models with Limited Dependent Variables
10.8 Incomplete Panels and Selection Bias
Exercises
A Vectors and Matrices
A.1 Terminology
A.2 Matrix Manipulations
A.3 Properties of Matrices and Vectors
A.4 Inverse Matrices
A.5 Idempotent Matrices
A.6 Eigenvalues and Eigenvectors
A.7 Differentiation
A.8 Some Least Squares Manipulations
B Statistical and Distribution Theory
B.1 Discrete Random Variables
B.2 Continuous Random Variables
B.3 Expectations and Moments
B.4 Multivariate Distributions
B.5 Conditional Distributions
B.6 The Normal Distribution
B.7 Related Distributions
Bibliography
Index