Learning Microeconometrics with R

Christopher P. Adams

• Publisher: Chapman and Hall/CRC
• Year: 2020
• ISBN:
  9781000282467
• Edition: 1
• Topic: Economía
• Subtopic: Microeconomía

This book provides an introduction to the field of microeconometrics through the use of R. The focus is on applying current learning from the field to real world problems. It uses R to both teach the concepts of the field and show the reader how the techniques can be used. It is aimed at the general reader with the equivalent of a bachelor's degree in economics, statistics or some more technical field. It covers the standard tools of microeconometrics, OLS, instrumental variables, Heckman selection and difference in difference. In addition, it introduces bounds, factor models, mixture models and empirical Bayesian analysis.

Key Features:

• Focuses on the assumptions underlying the algorithms rather than their statistical properties.
• Presents cutting-edge analysis of factor models and finite mixture models.
• Uses a hands-on approach to examine the assumptions made by the models and when the models fail to estimate accurately.
• Utilizes interesting real-world data sets that can be used to analyze important microeconomic problems.
• Introduces R programming concepts throughout the book.
• Includes appendices that discuss some of the standard statistical concepts and R programming used in the book.

Part II

Structural Estimation

5

Estimating Demand

5.1 Introduction

In the early 1970s, San Francisco was completing a huge new infrastructure project, the Bay Area Rapid Transit (BART) system. The project initially cost $1.6 billion and included tunneling under the San Francisco Bay. Policy makers were obviously interested in determining how many people would use the new system once it was built. But that is a problem. How do you predict the demand for a product that does not exist?

One solution is to ask people. A survey was conducted of people who were likely to use the new transport system. The survey asked detailed questions about their current mode of transport and asked them whether they would use the new system. The concern is that it is hard for people to predict how they would use something that does not exist. Berkeley econometrician, Dan McFadden, suggested an alternative approach. Instead of asking people to predict what they would do, McFadden suggested using information on what people actually do do, then use economic theory to predict what they would do.

McFadden argued that combining survey data with economic theory would produce more accurate estimates than the survey data alone (McFadden, 1974). In the case of the BART survey, McFadden was correct. According to the survey data, 15% of respondents said that they would use BART. McFadden estimated that 6% of respondents would use BART. In fact, 6% of respondents actually did use BART.¹ Survey data is valuable, but people give more accurate answers to some questions than others.

The first part of the book discussed how exogenous variation is needed to use observed data to predict policy outcomes. Chapters 1 and 2 assume that observed variation in exposure to a policy is determined independently of unobserved characteristics. Chapters 3 and 4 relaxed this assumption but allowed economic theory to be used in estimating the impact of the policy. This part of the book extends the idea of using economic theory. This chapter introduces the idea of using revealed preference.

Today, the ideas that McFadden developed for analyzing the value of BART are used across economics, antitrust, marketing, statistics and machine learning. At the Federal Trade Commission and the Department of Justice, economists use these techniques to determine whether a merger between ice cream manufacturers, or cigarette manufacturers, or supermarkets, or hospitals, will lead to higher prices.

When Google changed the way it displayed search results, user traffic moved away from Google’s competitors. Such actions by a dominant firm like Google could lead to antitrust actions unless the changes also made users better off. By combining economic theory and data on the behavior of Google’s users, we can determine wheth...