Optimization is at the core of human behavior and decision making, even in the presence of significant uncertainty, imperfect information or limited rationality. Modern research in all areas of business and economics therefore relies heavily on using such optimization models as coherent frameworks to interpret data and perform policy analysis by incorporating all types of frictions described above.
There are two distinct analytical approaches that serve these purposes. The first obtains qualitative economic insights from simple structures that can be solved by hand. The second typically specifies more complicated models whose aim is to provide a more accurate description of the reality and whose solutions must be approximated numerically by computer. This course focuses on the second approach. Its main purpose is to familiarize students with a basic set of techniques needed to start quantitative research.
At the same time, it provides an opportunity for students whose main research interests lie in the areas of finance and financial economics to broaden their understanding on how asset markets interact with the rest of the economy, for example, how incomplete markets (borrowing constraints) affect individual welfare in the presence of individual shocks (such the unemployment spells).
As a by-product, students will acquire hands-on experience with computational methods with a wider applicability in quantitative research and data analysis.
The course has two modules. First, it introduces a simple dynamic stochastic general equilibrium model with representative agents and explains the analytical and numerical (computer based) solution methods. Second, it expands the environment to include heterogeneous agents and analyzes some computational methods used to track their decisions. Numerical methods are implemented in Matlab.
Following this course, the students should be able to:
- Understand the economic foundations of dynamic models
- Implement appropriate solution and simulation techniques to analyze individual and aggregate outcomes in such models
- Confront model outcomes with data and implement counterfactual exercises or policy experiments
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1. Inter-temporal decisions of households and firms (2 session)
a. A work-horse model
b. Adding uncertainty
2. Dynamic systems (2 sessions)
a. A primer on difference equations
b. Steady state analysis (linearization, stability, saddle path property)
3. Simple stochastic models (3 sessions)
a. Solution techniques
b. Implementing solution techniques on computer using Matlab: calibration, simulation, recovering stylized facts from the model
c. Applications
4. Heterogeneous agents models (3 sessions)
a. Solution techniques
b. Implementation and applications using Matlab
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