This chapter provides a brief and intuitive summary of the econometric theory that underlies OLS estimation, including:

• The data generating process and sample regression functions

• Types of data

• Ordinary Least Squares (OLS) estimation

• Measuring correlation and goodness of fit

• The Gauss–Markov theorem

• Properties of estimators

• Hypothesis testing

Introduction

Running Regressions is about the quantitative analysis of observed behaviours and phenomena using economic theory, probability and statistics. By bringing these different elements together we can improve our understanding of people, firms and countries – in the past, the present and the future. For those readers who approach the subject with trepidation, it may be worth remembering this practical goal.

The skills that are required are not intrinsically difficult if a systematic approach is followed. Not least, our intention in Running Regressions is to illustrate commonly used techniques in an imaginative and interesting way. This chapter provides a brief introduction to concepts and techniques that are then worked through as practical examples in the following chapters.

Finally, whilst we focus on economics, finance and development studies in our selection of topics, the approach can be used in analysing a wide range of all real-world situations. The techniques can be, and are, used in all the social and natural sciences.

Models and data

The aim of econometric analysis is to understand the data generating processes (DGPs) that underlie economic systems and human behaviour. These DGPs are like the engines in a car and they propel socio-economic actions and events.