Knowledge-based business decisions and economic policy decisions are needed in an open competitive business and economic environment. This calls for the search for the best possible econometric model to build the best possible knowledge-base. This book offers the necessary tools for such knowledge-based decisions. This book provides an integrated presentation of statistical theory, methods and computation algorithm, enabling the researcher to make suitable modifications and improvements to suit his or her purpose. A variety of illustrative models, and model selection and estimation methods…mehr
Knowledge-based business decisions and economic policy decisions are needed in an open competitive business and economic environment. This calls for the search for the best possible econometric model to build the best possible knowledge-base. This book offers the necessary tools for such knowledge-based decisions. This book provides an integrated presentation of statistical theory, methods and computation algorithm, enabling the researcher to make suitable modifications and improvements to suit his or her purpose. A variety of illustrative models, and model selection and estimation methods offer a wide menu from which the reader can select the most appropriate and best-fitting model in any practical situation. The topics covered look at how to choose independent variables, the functional forms in which they should appear, the choice between alternate models, best estimation of parameters, and testing of statistical hypotheses. The problem and data setting considered are time series, cross-section, time-series of cross-sections, multivariate and univariate models, single equation and system of equations models, models with unobservable variables, models with limited dependent variables, and models with mixtures of statistical distributions. This book: * Looks at new methods including nonparametric and semi-parametric regression, partial least squares, robust regression, path analysis, canonical correlation, bootstrap and evolutionary algorithm. * Provides user-friendly computing software. * Draws heavily from the teaching and research experience of all the authors in China, India, and the United States. * Statistical theories and methods are illustrated throughout. The book is useful for graduate students in economics and business, and for researchers and consultants involved in business analytics. The research work of this book was supported by National Science Foundation of China.
Hengqing Tong, Department of Mathematics, Wuhan University of Technology, P.R.China T. Krishna Kumar, Indian Institute of Management, Samkhya Analytica India Private Limited, Bangalore, India Yangxin Huang, Department of Epidemiology and Biostatistics, University of South Florida, USA
Inhaltsangabe
Foreword.
Preface.
Chapter 1 Introduction.
1.1 Nature and Scope of Econometrics.
1.2 Types of Economic Problems, Types of Data, and Types of Models.
1.3 Pattern Recognition and Exploratory Data Analysis.
1.4 Econometric Modelling: The Roadmap of This Book.
Chapter 2 Independent Variables in Linear Regression Models.
21 Brief Review of Linear Regression.
2.2 Selection of Independent Variable and Stepwise Regression.
2.3 Multivariate Data Transformation and Polynomial Regression.
2.4 Column Multicollinearity in Design Matrix and Ridge Regression.
2.5 Recombination of Independent Variable and Principal Components Regression.
Chapter 3 Alternate Structures of Residual Error in Linear Regression Models.
31 Heteroscedasticity : Consequences and Tests for its Existence.
3.2 Generalized Linear Model with Covariance Being a Diagonal Matrix.
3.3 Autocorrelation in a Linear Model.
3.4 Generalized Linear Model with Positive Definite Covariance Matrix.
35 Random Effect and Variance Component Model.
Chapter 4 Discrete Variables and Nonlinear Regression Models.
4.1 Regression Model When Independent Variables Are Categorical.
4.2 Models with Categorical or Discrete Dependent Variables.
4.3 Nonlinear Regression Model and Its Algorithm.
4.4 Nonlinear Regression Models in Practice.
Chapter 5 Nonparametric and Semiparametric Regression Models.
5.1 Nonparametric Regression and Weight Function Method.
5.2 Semiparametric Regression Model.
5.3 Stochastic Frontier Regression Model.
Chapter 6 Simultaneous Equations Model and Distributed Lag Models.
6.1 Simultaneous Equations Models and Inconsistency of OLS Estimators.
6.2 Statistical Inference for Simultaneous Equations Model.
6.3 The Concepts of Lag Regression Models.
6.4 Finite Distributed Lag Models.
6.5 Infinite Distributed Lag Models.
Chapter 7 Stationary Time Series Models.
7.1 Autoregression Model AR(p).
7.2 Moving Average Model MA(q).
7.3 Auto-Regressive Moving-Average Process ARMA(p,q).
Chapter 8 Nonstationary and Multivariate Time Series Models.
8.1 Multivariate Stationary Time Series Model.
8.2 Nonstationary Time Series and Unit Root Process.
8.3 Cointegration and Error Correction.
8.4 Autoregression Conditional Heteroscedasticity Model in Time Series.
8.5 Mixed Models of Multivariate Regression with Time.
Chapter 9 Multivariate Statistical Analysis and Data Analysis.
9.1 Model of Analysis of Variance.
9.2 Other Multivariate Statistical Analysis Models.
9.3 Customer Satisfaction Model and Path Analysis.
9.4 Data Analysis and Process.
Chapter 10 Summary and Further Discussions.
10.1 About Probability Distributions: Parametric and Non-parametric.
10.2 Regression.
10.3 Model Specification and Prior Information.
10.4 Classical Theory of Statistical Inference.
10.5 Computation of Maximum Likelihood Estimates.
10.6 Specification Searche.
10.7 Resampling and Sampling Distributions-The Bootstraps Method.
