1. Introduction 2 -- Start using Gretl and R 3 -- Basic Material 4 -- Hypothesis testing 5 -- Simple linear regression 6 -- Multiple regression 7 -- Regression using dummy variables 8 -- Non linear models 9 -- Time series analysis 10 -- Other statistical tools.

Mathematical models are instruments of investigation, epistemological equivalent to the microscope and the telescope. In comparing the epistemological difference between models and experiments, Morgan (2005) argues that experiments offer greater epistemic power than models as a means to...

While historians of economics have noted the transition toward empirical work in economics since the 1970s, less understood is the shift toward "quasi-experimental" methods in applied microeconomics. Angrist and Pischke (2010) trumpet the wide application of these methods as a "credibility...

This chapter sets out the extension of the scope of the classical IV model to cases in which unobserved variables are set-valued functions of observed variables. The resulting Generalized IV (GIV) models can be used when outcomes are discrete while unobserved variables are continuous, when there...

Inverse problems can be described as functional equations where the value of the function is known or easily estimable but the argument is unknown. Many problems in econometrics can be stated in the form of inverse problems where the argument itself is a function. For example, consider a...

I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with...

We present a new class of methods for identification and inference in dynamic models with serially correlated unobservables, which typically imply that state variables are econometrically endogenous. In the context of Industrial Organization, these state variables often reflect econometrically...