Macroeconomics, Part II: Quantitative Growth Modeling
IWH-DPE / CGDE, Summer 2025, time and location
Syllabus
--  under construction  --

Description

This course equips students with advanced knowledge in modern growth theory. We start, focusing on capital accumulation as major growth engine,  with a discussion of the two basic workhorse growth models: Ramsey Model and OLG Model (Part II.1). (Some overlap with Master classes is okay.😊) We then review dynamic optimization methods and basic numerical solution techniques (Part II.2). Subsequently, we then focus on the role of education for economic growth (Part II.3). Next, we discuss how private and deliberate innovation can be modeled in general equilibrium to understand how endogenous technological change drives economic growth (Part II.4). Finally, there will be a brief review of stochastic growth (Brock-Mirman Model & Bewley Model) as well as an introduction to modeling climate change & growth (Part II.5). Most sections follow the textbook of Acemoglu, D. (2009): Introduction to Modern Economic Growth.

What is growth theory good for? Growth theory is obviously important when thinking about real-world growth processes as well as many growth-related phenomena, such as i) changes in asset prices (e.g., house prices) that trigger a change in the wealth distribution or ii) CO2 emissions (a by-product of economic activity and attenuated by directed technological change) that trigger climate change. These examples indicate growth theory is instrumental when reflecting on many big and fundamental questions. Growth theory, or more general macro theory, is fun because it enables us

1. to think, in a structured manner, about empirical data (macro & time series) by developing consistent narratives on historical episodes; 

2. to identify the impact of impulses (shocks) on endogenous variables over time and provide a decomposition of GE effects into different channels;

3. to identify the differential effects of policy changes by constructing counterfactuals (theory-based policy evaluation); and 

4. to provide theoretical interpretations of empirical estimation results and come up with educated priors in Bayesian estimations.