Computer-Aided Introduction to Econometrics, Softcover reprint of the original 1st ed. 2003

The advent of low cost computation has made many previously intractable econometric models empirically feasible and computational methods are now realized as an integral part of the theory.
This book provides graduate students and researchers not only with a sound theoretical introduction to the topic, but allows the reader through an internet based interactive computing method to learn from theory to practice the different techniques discussed in the book. Among the theoretical issues presented are linear regression analysis, univariate time series modelling with some interesting extensions such as ARCH models and dimensionality reduction techniques.
The electronic version of the book including all computational possibilites can be viewed at
http://www.xplore-stat.de/ebooks/ebooks.html

1 Univariate Linear Regression Model.- 1.1 Probability and Data Generating Process.- 1.2 Estimators and Properties.- 1.3 Inference.- 1.4 Forecasting.- 2 Multivariate Linear Regression Model.- 2.1 Introduction.- 2.2 Classical Assumptions of the MLRM.- 2.3 Estimation Procedures.- 2.4 Properties of the Estimators.- 2.5 Interval Estimation.- 2.6 Goodness of Fit Measures.- 2.7 Linear Hypothesis Testing.- 2.8 Restricted and Unrestricted Regression.- 2.9 Three General Test Procedures.- 2.10 Dummy Variables.- 2.11 Forecasting.- 3 Dimension Reduction and Its Applications.- 3.1 Introduction.- 3.2 Average Outer Product of Gradients and its Estimation.- 3.3 A Unified Estimation Method.- 3.4 Number of E.D.R. Directions.- 3.5 The Algorithm.- 3.6 Simulation Results.- 3.7 Applications.- 3.8 Conclusions and Further Discussion.- 3.9 Appendix. Assumptions and Remarks.- 4 Univariate Time Series Modelling.- 4.1 Introduction.- 4.2 Linear Stationary Models for Time Series.- 4.3 Nonstationary Models for Time Series.- 4.4 Forecasting with ARIMA Models.- 4.5 ARIMA Model Building.- 4.6 Regression Models for Time Series.- 5 Multiplicative SARIMA models.- 5.1 Introduction.- 5.2 Modeling Seasonal Time Series.- 5.3 Identification of Multiplicative SARIMA Models.- 5.4 Estimation of Multiplicative SARIMA Models.- 6 Auto Regressive Conditional Heteroscedastic Models.- 6.1 Introduction.- 6.2 ARCH(1) Model.- 6.3 ARCH(q) Model.- 6.4 Testing Heteroscedasticity and ARCH(1) Disturbances.- 6.5 ARCH(1) Regression Model.- 6.6 GARCH(p,q) Model.- 6.7 Extensions of ARCH Models.- 6.8 Two Examples of Spanish Financial Markets.- 7 Numerical Optimization Methods in Econometrics.- 7.1 Introduction.- 7.2 Solving a Nonlinear Equation.- 7.3 Solving a System of Nonlinear Equations.- 7.4 Minimization of a Function: One-dimensional Case.- 7.5 Minimization of a Function: Multidimensional Case.- 7.6 Auxiliary Routines for Numerical Optimization.

Includes supplementary material: sn.pub/extras