# INET 2015

## Instructor

Daniel McDonald

Department of Statistics

Indiana University, Bloomington

## Course description

Largely unnoticed by economists, over the last three decades statisticians and computer scientists have developed sophisticated prediction procedures and methods of model selection and forecast evaluation under the rubric of statistical learning theory. These methods have revolutionized pattern recognition and artificial intelligence, and the modern industry of data mining (without the pejorative connotation) would not exist without it.

In this short course, we will investigate the connections between modern statistical methodology and standard techniques which are more common in economics and social science. The goal is to develop an understanding for the circumstances under which different statistical methods are appropriate, how methods behave when the model for the data generating process is incorrect, and why some methods can adapt while others cannot. We will focus especially on selecting models with good predictive performance and on the relationships between model complexity, statistical estimation, and the amount of data used to estimate the model.

The starting point for statistical learning is the notion of predictive risk and the tradeoff between bias and variance. With this tradeoff in mind, we will investigate the importance of assessing models using training and test data, the benefits of regularization, and the necessity of selecting tuning parameters carefully. We will illustrate each of these issues with some standard econometric procedures applied to financial and economic datasets while at the same time, introducing some potentially new procedures which may be useful in your own research.

## Schedule of topics

- The predictive viewpoint
- The bias-variance tradeoff
- Evaluating predictions and estimators
- The benefits of regularization
- Model selection
- Choosing tuning parameters
- Application: BVARs and DSGEs
- Tools for classification
- Application: Predicting recessions
- Collaborative filtering and the Netflix prize
- Dimension reduction

## Suggested preparation

In order to be best prepared for this course, we suggest that you brush up on your econometrics, especially the content about probability. Most useful will be to remind your self about expected values and variance, convergence in probability and consistency, maximum likelihood, ordinary least squares and some time series topics like autoregressive models. Also, remind yourself what a dynamic stochastic general equilibrium model is (you probably know more about this than we do, but perhaps not). Less useful are GMM techniques and cointegration.

Before coming to class, we suggest that you take a look at the first three (3) chapters of Cosma Shalizi’s preprint Advanced Data Analysis from an Elementary Point of View. These chapters will serve as a nice introduction to the materials we intend to cover. The rest of the book is great too if you are feeling more ambitious/motivated.

## Preliminary exercises

We will make a handful of exercises available to try before you come to class. These are mainly for review and little else. They will certainly not be graded, but spending a few hours trying them may help you come to class more prepared. The exercises are here.

## Data analysis

All of the data analysis examples we use in class will be done in the open source programming language R. R is extremely powerful and easily extensible. We will try to make all of our code available on the website so that you may experiment with it. The software is available for free on CRAN. An official introduction is available there, and many other introductions are available on the web. See for example here. Feel free to download and play around with R, but experience and/or mastery of computer programming is not necessary.

## Lectures and code

- Introduction (slides)
- Regression (slides) (R code)
- Economic forecasting (slides) (Selected R code) (data files)
- Classification (slides) (Selected R code) (data files)
- Dimension reduction (slides) (Selected R code) (data)
- Collaborative filtering (slides) (Facebook Code)
- Conclusion (slides)