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Developing Econometrics.
Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2011.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Developing Econometrics; Contents; Foreword; Preface; Acknowledgements; 1 Introduction; 1.1 Nature and Scope of Econometrics; 1.1.1 What is Econometrics and Why Study Econometrics?; 1.1.2 Econometrics and Scientific Credibility of Business and Economic Decisions; 1.2 Types of Economic Problems, Types of Data, and Types of Models; 1.2.1 Experimental Data from a Marketing Experiment; 1.2.2 Cross-Section Data: National Sample Survey Data on Consumer Expenditure; 1.2.3 Non-Experimental Data Taken from Secondary Sources: The Case of Pharmaceutical Industry in India.
- 1.2.4 Loan Default Risk of a Customer and the Problem Facing Decision on a Loan Application1.2.5 Panel Data: Performance of Banks in India by the Type of Ownership after Economic Reforms; 1.2.6 Single Time Series Data: The Bombay Stock Exchange (BSE) Index; 1.2.7 Multiple Time Series Data: Stock Prices in BRIC Countries; 1.3 Pattern Recognition and Exploratory Data Analysis; 1.3.1 Some Basic Issues in Econometric Modeling; 1.3.2 Exploratory Data Analysis Using Correlations and Scatter Diagrams: The Relative Importance of Managerial Function and Labor.
- 1.3.3 Cleaning and Reprocessing Data to Discover Patterns: BSE Index Data1.4 Econometric Modeling: The Roadmap of This Book; 1.4.1 The Econometric Modeling Strategy; 1.4.2 Plan of the Book; Electronic References for Chapter 1; References; 2 Independent Variables in Linear Regression Models; 2.1 Brief Review of Linear Regression; 2.1.1 Brief Review of Univariate Linear Regression; 2.1.2 Brief Review of Multivariate Linear Regression; 2.2 Selection of Independent Variable and Stepwise Regression; 2.2.1 Principles of Selection of Independent Variables; 2.2.2 Stepwise Regression.
- 2.3 Multivariate Data Transformation and Polynomial Regression2.3.1 Linear Regression after Multivariate Data Transformation; 2.3.2 Polynomial Regression on an Independent Variable; 2.3.3 Multivariable Polynomial Regression; 2.4 Column Multicollinearity in Design Matrix and Ridge Regression; 2.4.1 Effect of Column Multicollinearity of Design Matrix; 2.4.2 Ridge Regression; 2.4.3 Ridge Trace Analysis and Ridge Parameter Selection; 2.4.4 Generalized Ridge Regression; 2.5 Recombination of Independent Variable and Principal Components Regression; 2.5.1 Concept of Principal Components Regression.
- 2.5.2 Determination of Principal ComponentElectronic References for Chapter 2; References; 3 Alternative Structures of Residual Error in Linear Regression Models; 3.1 Heteroscedasticity: Consequences and Tests for Its Existence; 3.1.1 Consequences of Heteroscedasticity; 3.1.2 Tests for Heteroscedasticity; 3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix; 3.2.1 Diagonal Covariance Matrix and Weighted Least Squares; 3.2.2 Model with Two Unknown Variances; 3.2.3 Multiplicative Heteroscedastic Model; 3.3 Autocorrelation in a Linear Model.