ECO550B - Microeconomics and Statistics for Public Policy (2023-2024)

PART 1: Microeconomics

This course provides an introduction to the main public economic issues: social welfare, public choice theory, externalities, public goods and taxation. It recalls the classical theory of market failures due to externalities and public goods and derives the main results from the public choice theory. A large part is devoted to the theoretical models. The course contains rigorous mathematical tools and handles the microeconomic tools used in the public economic theory.

List of tems (for 12h)

1. Introduction to public economics (basics on standard microeconomics + market failure and public intervention) : 3h
2. Social choice and introduction to vote : how to aggregate preferences ? 3h
3. Externality (taxes, pollution permits, Coase Theorem) : 3h
4. Public goods and taxation (incidence + deadweight loss): 3h

Part 2 Statistics 

This course introduces the notion of statistical models. It develops the basic principles and concepts of estimation and testing within an asymptotic framework. These principles will be developed in particular on the maximum likelihood estimator and in the context of the multiple linear model, whose use is central in econometrics.

At the end of this course, students should be able to

• Define and use statistical vocabulary (population, observation, sample, etc.)
• Model a statistical problem
• Calculate estimators of moments, maximum likelihood and ordinary least squares
• Describe the asymptotic behavior of these estimators
• Construct tests and interpret their decisions
• Give the level and power of a test.

Outline

1. General principles - The aims of statistics, the various approaches, the types of statistical models (parametric, semi- and non-parametric). Descriptive Statistics for univariate and bivariate data.
2. Estimation - Estimation problem. Asymptotic estimation: maximum likelihood, moments method, ordinary least squares.
3. Hypothesis testing - Neyman-Pearson approach (confidence region, power, level, risks). Simple tests, Neyman-Pearson lemma. Student's t-test. Asymptotic tests (Wald, Score, likelihood ratio). Goodness-of-fit tests (chi-squared, K-S).

References :

Wasserman, L. (2004). All of statistics: a concise course in statistical inference (Vol. 26).

New York: Springer.

Casella, G., & Berger, R. L. (2021). Statistical inference. Cengage Learning.