Developing Econometrics

Description

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.Content: Chapter 1 Introduction (pages 1–28): Chapter 2 Independent Variables in Linear Regression Models (pages 29–81): Chapter 3 Alternative Structures of Residual Error in Linear Regression Models (pages 83–127): Chapter 4 Discrete Variables and Nonlinear Regression Model (pages 129–192): Chapter 5 Nonparametric and Semiparametric Regression Models (pages 193–214): Chapter 6 Simultaneous Equations Models and Distributed Lag Models (pages 215–251): Chapter 7 Stationary Time Series Models (pages 253–295): Chapter 8 Multivariate and Nonstationary Time Series Models (pages 297–355): Chapter 9 Multivariate Statistical Analysis and Data Analysis (pages 357–414): Chapter 10 Summary and Further Discussion (pages 415–460):