This course is the second part of the macroeconomics sequence of the master’s program at TeIAS. While classical macroeconomics is concerned about the aggregate economy of a huge and well-diversified, market-oriented economy, such as the US, much of the world economies are relatively small and open economies in which trade shocks, as well as the relative price of non-tradable goods (inverse of the real exchange rate), plays a crucial role in the consumption, saving and labor supply decision of workers. In short, the macroeconomic policy depends on trade and exchange rate policy as well as monetary and fiscal policy. 

The purpose of this course is twofold. On the theory side, we first review basic open macro models well suited for developing countries. Then we review important literature on consumption, investment, and frictions in the financial markets and how these frictions may be of importance for the development process of an economy. Consumption, savings, and investment decisions of economic agents are intertwined not only with interest rates in the economy (which is most of the classical macro) but also with trade and the economy’s current account balance. On the other hand, financial markets have proved to be crucial for sustainable and long-term economic growth, development, and stabilization of the economy in the short term.

Nevertheless, the malfunctioning financial markets due to their under-supervision or even wrong supervision has proved to be the primary source of economic crises. Therefore, it is essential to review how these parts of the economy work and how they are interconnected. While labor markets are a critical topic in macro, we will not have time to review the vital theories to understand unemployment in this course. These essential topics remain for future macro-labor classes.

On the methodological side, a macroeconomist needs to be able to compare alternative theories. First, we have to use models to predict how the economy may work under the model’s assumptions and for a given set of model parameters. Then we have to be able to “calibrate/parametrize” or “estimate” parameters for our models to best match the real-world data. Unfortunately, we don’t have time during this course to cover the latter, which needs another separate entire semester course. But we tangentially cover a few topics in dynamic programming and quantitative tools to simulate those problems.

Here is the syllabus for this course.