This course is not for everybody. It is important that you have a solid knowledge of macroeconomic models taught at the graduate level. If you don't have the right background yet, then it is better to wait a year. Required knowledge about dynamic models:
Know what an Euler and a Bellman equation is and how to derive them.
Know what state variables are.
Understand key economic mechanisms in representative-agent models such as consumption smoothing and precautionary savings.
Some Matlab programming.
You can find some preparation tips here.
This course runs online only. Therefore, if you are accepted and enroll into the course, you will need:
access to Zoom to participate in lectures and computer sessions.
access to Matlab for computer sessions (with the optimization toolbox).
some elements will require Dynare. If you're new to Dynare, make sure to go through the tutorial and example code here (please make sure you are able to run Dynare code before the course starts).
This graduate-level course provides an introduction into the macroeconomics of business dynamism. Students will learn how to build and solve general equilibrium models with heterogeneous firms and how to use micro-data to discipline them. Participants will walk away with a portfolio of Matlab codes implementing a range of such models. Importantly, the course focuses on developing practical knowledge and pointing out typical problems that researchers may run into when building, parameterizing and solving their own models and when analyzing firm-level micro-datasets.
The first lecture introduces the topic of business dynamism and presents the workhorse macroeconomic model (Hopenhayn and Rogerson, 1993). In doing so, we will pay particular attention to key building blocks: decisions of incumbent businesses, firm entry and exit and the distribution of firms. We will discuss how to parameterize the model and how to solve for the general equilibrium using a simple algorithm.
Topics
Motivation and empirical facts
A baseline model ala Hopenhayn (1992)
Hopenhayn and Rogerson (1993)
Solving for the general equilibrium with heterogeneous firms
Exercise
In the first coding session, you will be asked to solve a version of the Hopenhayn and Rogerson (1993) model.
The second lecture moves on to models of (static) factor misallocation. We will begin by discussing the model of Restuccia and Rogerson (2008) and the importance of firm heterogeneity and transitory shocks (ex-ante vs ex-post differences). Next, we will show how the nature of firm heterogeneity matters for aggregate outcomes through the lens of the model in Sterk, Sedlacek and Pugsley (2021). In doing so, we will discuss the choices researchers must make when building their models and what data they may use to discipline their frameworks.
Topics
Misallocation in the data
Modelling firm heterogeneity
Restuccia and Rogerson (2008), Sterk (r) Sedlacek (r) Pugsley (2021)
Exercise
In the second exercise, you are asked to solve a general equilibrium model with firm heterogeneity, ala Sterk (r) al (2021).
This lecture introduces aggregate uncertainty in order to investigate how firm dynamics evolve over the business cycle. You will learn how to solve heterogeneous firm models with aggregate shocks and we will discuss how the distribution of firms (and its changes over the business cycles) helps shape aggregate fluctuations.
Topics
Business dynamism over the business cycle in the data
Clementi and Palazzo (2016), Sedlacek and Sterk (2017)
Solving models with heterogeneous firms and aggregate uncertainty
Exercise
In the coding session, you are asked to solve a firm dynamics model with business cycles, ala Sedlacek, Sterk (2017).
The fourth lecture turns towards long-run dynamics and considers models of business dynamism and innovation. We will discuss in detail how to model firms' individual innovation decisions and how these map into aggregate growth. You will learn how to solve and simulate models with endogenous productivity growth and which modeling choices may help you gain (analytical) insights.
Topics
Firms, innovation and growth in the data
Acemoglu et al. (2018), Mukoyama and Osotimehin (2019)
Special cases and analytical results
Exercise
In this coding session, you are asked to solve a "generic" firm dynamics model with endogenous growth.
The Friday lecture examines the data and methodologies used to estimate macroeconomic models of business dynamism. We will review available data sources—ranging from aggregate, economy-wide datasets to firm-level microdata—with a focus on key variables such as markups, firm lifecycle dynamics, and capital investment. You will learn the theoretical foundations of indirect inference and the generalized method of moments, alongside practical applications for estimating the parameters of structural models. Finally, the session will provide an overview of recent advances in causal inference within panel data settings.
Topics
Data sources: macro and micro
Data hygiene: dealing with outliers, industries, winsorizing
Data theory: indirect inference, panel data econometrics, casual inference
Data practice: markup estimation, firm dynamics, capital investment, tips and tricks
Research workshop
The last day of the course does not include a coding session. Instead, the time is devoted to the research workshop.