## Developing EconometricsStatistical 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. |

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

BSE Index Data | 22 |

Independent Variables in Linear Regression Models | 29 |

Alternative Structures of Residual Error in Linear Regression Models | 83 |

Discrete Variables and Nonlinear Regression Model | 129 |

Regression Model | 164 |

Nonparametric and Semiparametric Regression Models | 193 |

Simultaneous Equations Models and Distributed Lag Models | 215 |

### Other editions - View all

### Common terms and phrases

ˆ ˆ ˆ algorithm AR(p ARMA(p assume asymptotic auto-covariance function autocorrelation autoregressive calculate cointegration column conditional critical value DASC data mining degrees of freedom dependent discussed distributed lag econometric models economic Electronic References endogenous variables estimation of parameters example exploratory data analysis Figure forecast heteroscedasticity independent variables iterative Journal of Statistical kernel least squares estimator linear model linear regression linear regression model logistic Logit model MA(q maximum likelihood estimation method multivariate nonlinear regression nonlinear regression model nonparametric regression normal distribution null hypothesis observations obtain Planning and Inference principal components problem random effects References for Chapter regression coefficients regression equation regression model residual sample semiparametric regression semiparametric regression model series models significant solution stationary time series Statistical Planning stochastic stock prices sum of squares total correlation transformation trend unit root unit root process univariate variance component model vector white noise