| Description |
1 online resource (487 pages) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 |
| Summary |
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 developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mi |
| Notes |
3.3.1 Linear Model with First-Order Residual Autoregression |
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Print version record |
| Subject |
Econometrics.
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Econometric models.
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Data mining.
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Data Mining
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Data mining
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Econometric models
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Econometrics
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| Form |
Electronic book
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| Author |
Kumar, T. Krishna
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Huang, Yangxin
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| ISBN |
9781119954248 |
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111995424X |
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