An introduction to econometric theory
A guide to economics, statistics and finance that explores the mathematical foundations underling econometric methods An Introduction to Econometric Theory offers a text to help in the mastery of the mathematics that underlie econometric methods and includes a detailed study of matrix algebra and di...
| Otros Autores: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons, Inc
2018.
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| Colección: | Wiley ebooks.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40630766*spi |
Tabla de Contenidos:
- Cover; Title Page; Copyright; Contents; List of Figures; Preface; About the Companion Website; Part I Fitting; Chapter 1 Elementary Data Analysis; 1.1 Variables and Observations; 1.2 Summary Statistics; 1.3 Correlation; 1.4 Regression; 1.5 Computing the Regression Line; 1.6 Multiple Regression; 1.7 Exercises; Chapter 2 Matrix Representation; 2.1 Systems of Equations; 2.2 Matrix Algebra Basics; 2.3 Rules of Matrix Algebra; 2.4 Partitioned Matrices; 2.5 Exercises; Chapter 3 Solving the Matrix Equation; 3.1 Matrix Inversion; 3.2 Determinant and Adjoint; 3.3 Transposes and Products.
- 3.4 Cramer's Rule3.5 Partitioning and Inversion; 3.6 A Note on Computation; 3.7 Exercises; Chapter 4 The Least Squares Solution; 4.1 Linear Dependence and Rank; 4.2 The General Linear Regression; 4.3 Definite Matrices; 4.4 Matrix Calculus; 4.5 Goodness of Fit; 4.6 Exercises; Part II Modelling; Chapter 5 Probability Distributions; 5.1 A Random Experiment; 5.2 Properties of the Normal Distribution; 5.3 Expected Values; 5.4 Discrete Random Variables; 5.5 Exercises; Chapter 6 More on Distributions; 6.1 Random Vectors; 6.2 The Multivariate Normal Distribution; 6.3 Other Continuous Distributions.
- 6.4 Moments6.5 Conditional Distributions; 6.6 Exercises; Chapter 7 The Classical Regression Model; 7.1 The Classical Assumptions; 7.2 The Model; 7.3 Properties of Least Squares; 7.4 The Projection Matrices; 7.5 The Trace; 7.6 Exercises; Chapter 8 The Gauss-Markov Theorem; 8.1 A Simple Example; 8.2 Efficiency in the General Model; 8.3 Failure of the Assumptions; 8.4 Generalized Least Squares; 8.5 Weighted Least Squares; 8.6 Exercises; Part III Testing; Chapter 9 Eigenvalues and Eigenvectors; 9.1 The Characteristic Equation; 9.2 Complex Roots; 9.3 Eigenvectors; 9.4 Diagonalization.
- 9.5 Other Properties9.6 An Interesting Result; 9.7 Exercises; Chapter 10 The Gaussian Regression Model; 10.1 Testing Hypotheses; 10.2 Idempotent Quadratic Forms; 10.3 Confidence Regions; 10.4 t Statistics; 10.5 Tests of Linear Restrictions; 10.6 Constrained Least Squares; 10.7 Exercises; Chapter 11 Partitioning and Specification; 11.1 The Partitioned Regression; 11.2 Frisch-Waugh-Lovell Theorem; 11.3 Misspecification Analysis; 11.4 Specification Testing; 11.5 Stability Analysis; 11.6 Prediction Tests; 11.7 Exercises; Part IV Extensions; Chapter 12 Random Regressors.
- 12.1 Conditional Probability12.2 Conditional Expectations; 12.3 Statistical Models Contrasted; 12.4 The Statistical Assumptions; 12.5 Properties of OLS; 12.6 The Gaussian Model; 12.7 Exercises; Chapter 13 Introduction to Asymptotics; 13.1 The Law of Large Numbers; 13.2 Consistent Estimation; 13.3 The Central Limit Theorem; 13.4 Asymptotic Normality; 13.5 Multiple Regression; 13.6 Exercises; Chapter 14 Asymptotic Estimation Theory; 14.1 Large Sample Efficiency; 14.2 Instrumental Variables; 14.3 Maximum Likelihood; 14.4 Gaussian ML; 14.5 Properties of ML Estimators; 14.6 Likelihood Inference.
