In recent years the structural approach to econometrics compared to reduced form econometrics has become more popular. This involves tight combination of theoretical economic models and statistics in order to estimate parameters of interest. Imposing more theoretical structure in the way that we use data and statistical methods is meant to provide guidance and sometimes can even uncover parameters that are not easily estimable with reduced form methods. Even for non-econometricians this can potentially be interesting because simulation and sampling can be an important part in structural estimation and the techniques are well applicable in other social sciences.

This branch of econometrics, as a branch of statistics, does not seem to have any introductory textbooks so far. I have only found more advanced material like Structural Econometric Models by Choo and Shum (2013) or the survey chapter by Reiss and Wolak.

Could someone point me towards a set of lectures or perhaps even a book (that I just haven’t found yet) which would provide an introduction to structural econometrics? Ideally this would be based on examples with different approaches including code or a guide on how to replicate these examples for better understanding.

I am aware of several research papers especially in industrial organization

- modeling of state dependence (Rust, 1987)
- demand estimation (Berry, 1994; Berry, Levinson, and Pakes, 1995)
- estimation of productivity (Olley and Pakes, 1996)
- estimation of market power (Nevo, 2005; Sovinsky, 2008)
but most of them are difficult to follow. So if someone knows about a more gentle introduction this would be of great help.

**Answer**

I am not aware of anything like this. Paarsch and Hong’s An Introduction to the Structural Econometrics of Auction Data and Ada and Cooper’s Dynamic Economics come closest.

The usual classroom approach is to read classic papers and perhaps replicate one along the way. Here’s one example (Jean-Marc Robin). Here’s are more labor oriented lecture notes (Chris Taber).

**Attribution***Source : Link , Question Author : Andy , Answer Author : dimitriy*