Developing Econometrics
November 2011
486 Seiten, Hardcover
Wiley & Sons Ltd
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
mining
* Highlights recent advances in statistical theory and methods
that benefit econometric practice.
* Features examples with real life data.
* Accompanying software featuring DASC (Data Analysis and
Statistical Computing).
Essential reading for practitioners in any area of econometrics;
business analysts involved in economics and management; and
Graduate students and researchers in economics and statistics.
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.
10.8 Bayesian Inference.
Index.
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
