This course is about some fundamental and important ideas in econometrics.

First, the course starts with a review of the basic ideas and history of econometrics.

Second, we discuss the meanings of identification in econometrics. We start with the classical example of identifying simultaneous equations (demand and supply curves). The various identification meanings are closely related to the estimation strategies. We discuss two important types of estimation methods: moment-based and extremum-based methods.

Third, we discuss the endogeneity problems in econometrics, which are common reasons for the failure of identification.

Fourth, we discuss the ideas and differences in the meanings of causality and prediction.

Finally, we discuss frequentist, Bayesian, and Fisherian inferences. This part emphasizes the connection between econometrics and statistics.

No econometrics knowledge is assumed. Each topic will be developed at the beginner level so that the course is self-contained. But a certain level of mathematical maturity is expected (see Wikipedia for interesting definitions of mathematical maturity). Precisely, the prerequisites are
(1) introductory microeconomics;
(2) basic calculus, linear algebra, and statistics.

Essentially, students are expected to know what are market (competitive and non-competitive), demand, supply, differentiation, integration, matrix, probability, distribution, density, expectation (conditional and unconditional), mean, variance, and covariance.

This course is suitable for those who are interested in econometrics and statistics for social sciences. Students who have no training in economics but have solid background in mathematics and statistics are welcome.