Ebook Computational Laboratory for Economics with R

These notes refer mainly to the examples and illustrations proposed in the book A Guide to
Modern Econometrics by Marno Verbeek (4th and 3rd editions).
The source codes here described are written in the R language (R Development Core Team
2016) (R version 3.2.4 was used).
Subjects were initially presented in the course Computational Laboratory for Economics
held at Universit`a Cattolica del Sacro Cuore, Graduate Program Economics. The course ran
in parallel with the course Empirical Economics where the methodological background were
assessed.
Attention is paid in order to obtain results first according to their mathematical structure,
and then by using appropriate built-in R functions, always aiming at achieving an efficient
and elegant programming style.
The reader is assumed to possess the basic knowledge of R.
In Chapter 1 some simulation results on the distribution properties of the sample mean are
shown. Chapters from 2 to 10 reproduce results of examples and illustrations of Verbeek’s
Guide. Appendix A1 describes how to read data from text, Stata, EViews and Microsoft
Excel files. Appendix B contains results for examples which were present on the 3rd edition
of Verbeek’s Guide.
Detailed methodological presentations are given on the geometric interpretation of the
linear model and OLS, on multicollinearity problems and their detection, on the assessment
of distributional hypotheses (Chapter 2), on the interpretation of coefficients in linear
regression model with logarithmic transformation of the outcome or of explanatory variables
(Chapter 3), on the application of the GMM (Chapter 5), on the interpretation of marginal
effects for logit and probit models and of odds ratio for logit models (Chapter 7) and on the
classical presentation of stochastic processes (Chapter 8).
Some companion material to these Lecture Notes can be downloaded from the booksite
www.educatt.it/libri/materiali.
I warmly thank Diego Zappa and Giuseppe Boari for having read parts of the manuscript.
I wish to thank Stefano Iacus for his short course on an efficient and advanced use of R,
and Achim Zeileis, Giovanni Millo and Yves Croissant for having improved their packages
lmtest and plm in order to properly fit some problems here presented.