Economics

The course discusses probability, distribution theory, and statistical inference. It covers mathematical statistics as important discrete and continuous probability distributions (such as the Binomial, Poisson, Uniform, Exponential, and Normal distributions) and investigates properties of these distributions, including use of the moment generating function. The course discusses point estimation techniques including method of moments, maximum likelihood, and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction are also covered.

This course explores the basic theories regarding the economics of money, banking, and financial markets. Topics include why money is needed; the monetary system and financial system; how money be should managed; and what kind of financial system is best.

This course discusses transformations in the governance of global trade. It examines tensions between multilateralism and bilateralism in global trade, reconfiguration of the WTO, and the role and protagonism of the EU and other key actors.

This course provides an introduction to models of international trade and their predictions of trade patterns, with some consideration of empirical studies and policy issues. The course introduces students to classical and new theories of international trade; uses examples and empirical evidence to introduce students to the methods most commonly used in the economic analysis of international trade; and enables students to engage with trade theory in a critical manner, understanding the arguments used both in favor and against trade liberalization. 

The goal of this course is in introducing popular skills for analyzing economic data. We attempt to achieve this goal by getting familiar with the well-known econometric analyses and linking this to the knowledge on the numerical outputs generated by standard statistical packages. In attaining this goal, our interests will be focused more on cross-sectional data and their slight extensions. There are two reasons for this focus. First, analysis of cross-sectional data is a building bloc for the analysis of many other data sets. Second, the analysis of cross-sectional data is easier than analyzing other data sets as they do not involve too much complication that comes from the variation assumptions. Eventually, by these, studying cross-sectional data becomes a good starting point for achieving the specified objectives, even though their applicability is not so limited. After completing this course, students are expected to be able to conduct the following: Understanding the implicit assumptions behind economic data analysis; Interpreting the numerical outputs generated by standard statistical packages.

Prerequisite: Mathematics for economics and statistics; Recommended: Mathematical statistics.