This book explains how to use R software to teach econometrics by providing interesting examples, using actual data applied to important policy issues. It helps readers choose the best method from a wide array of tools and packages available. The data used in the examples along with R program snippets, illustrate the economic theory and sophisticated statistical methods extending the usual regression. The R program snippets are not merely given as black boxes, but include detailed comments which help the reader better understand the software steps and use them as templates for possible extension and modification.

Contents: Production Function and Regression Methods Using R; Univariate Time Series Analysis with R; Bivariate Time Series Analysis Including Stochastic Diffusion and Cointegration; Utility Theory and Empirical Implications; Vector Models for Multivariate Problems; Simultaneous Equation Models; Limited Dependent Variable (GLM) Models; Dynamic Optimization and Empirical Analysis of Consumer Behavior; Single, Double and Maximum Entropy Bootstrap and Inference; Generalized Least Squares, VARMA, and Estimating Functions; Box-Cox, Loess and Projection Pursuit Regression.