Quantitative Methods in Macroeconomics and Finance (ECO-AD-QNTMTHS)
ECO-AD-QNTMTHS
| Department |
ECO |
| Course category |
ECO Advanced courses |
| Course type |
Course |
| Academic year |
2023-2024 |
| Term |
BLOCK 2 |
| Credits |
.5 (EUI Economics Department) |
| Professors |
- Prof. Pablo Guerron (Boston College)
|
| Contact |
Simonsen, Sarah
|
| Sessions |
13/11/2023 13:30-15:30 @ Conference Room, Villa la Fonte
16/11/2023 9:00-11:00 @ Conference Room, Villa la Fonte
17/11/2023 13:30-15:30 @ Conference Room, Villa la Fonte
20/11/2023 13:30-15:30 @ Conference Room, Villa la Fonte
21/11/2023 11:00-12:30 @ Conference Room, Villa la Fonte
22/11/2023 11:00-13:00 @ Conference Room, Villa la Fonte
|
Purpose
In this course, we will learn numerical methods to solve and estimate nonlinear dynamic general equilibrium models (DSGE). Although the target audience is macro/international economics students, the class will be taught in a very general format so students from other fields may find beneficial to take the course. The material is mostly based on lecture notes jointly developed with Jesus Fernandez-Villaverde (U. Penn). They will be available before each class.
In the first part of the course, the student will be introduced to tools in software engineering and numerical analysis. The second part of the course is devoted to global methods to solve DSGE models. Some of these methods may be familiar to you because of economic examples in other Ph.D. courses. We will go into the details of why these methods work and how to apply them to a variety of situations.
In the final part, you will be introduced to techniques to estimate models displaying nonlinear dynamics. Most of this part will concentrate on the particle filter and estimation using likelihood-based methods.
Description
In the first part of the course, the student will be introduced to tools in software engineering and numerical analysis. The second part of the course is devoted to global methods to solve DSGE models. Some of these methods may be familiar to you because of economic examples in other Ph.D. courses. We will go into the details of why these methods work and how to apply them to a variety of situations.
In the final part, you will be introduced to techniques to estimate models displaying nonlinear dynamics. Most of this part will concentrate on the particle filter and estimation using likelihood-based methods.
Register for this course
Page last updated on 05 September 2023