One of the main challenges for central banks is to monitor capital flows in and out of the country. Moreover, even if each central bank has high-quality information, it is difficult to make cross-country comparisons due to the lack of homogenous criteria for monthly frequency. As Portfolio investment is an important component of the Balance of Payments, the Network coordinates a database on monthly portfolio investment flows. The objective of this initiative is timely and better access to data on capital flows in the region that goes beyond private sources, thereby facilitating cross-country comparisons, monitoring, and research on the evolution of these flows.
DEEP LEARNING
Instructors: Marcelo Fernandes and Eduardo Mendes
Dates: September and October, 2022
This course covers neural networks, text as data and deep learning applications to natural language processing. Main topics include: i) Fundamental concepts in Neural Networks; ii) Network architectures; iii) Implementation in Keras; iv) Natural Language Processing; v) Topic modeling,
Materials (to be published)
Videos (to be published)
INDUSTRIAL ORGANIZATION (IO) APPROACHES IN FINANCIAL ECONOMICS APPLICATIONS
Instructor: Ali Hortacsu
Dates: 06 - 09 September, 2022
This is a short course on the use of modern IO approaches to analyzing financial markets. The course reviews essential elements of the empirical IO toolbox, including demand estimation and structural econometrics of static and time allowing dymanic games. Special emphasis is given to models with choice/information frictions and their applications on choice and regulation in financial product markets. Finally, empirical literature on auctions and market design is discussed.
Materials (to be published)
Videos: (to be published)
QUANTITATIVE METHODS IN MACROECONOMICS WITH JULIA
Instructor: Pablo Guerrón
Dates: 22 - 26 Agust and 1 - 7 September, 2022
This course is focused on teaching quantitative methods used in macroeconomics, such as tools in software engineering, numerical analysis, and global methods to solve DSGE models. Because of its nature, this course is highly applied with the programming language Julia.
Materials (to be published)
Videos: The course videos have been shared with participants through Google Drive. If you would like to regain access, please send an email to anace@iadb.org
SUPERVISED MACHINE LEARNING
Instructors: Marcelo Fernandes and Eduardo Mendes
Dates: Agust and September, 2022
This course covers both linear and nonlinear predictive models in statistical learning. Main topics include: i) Introduction to statistical learning; ii) Linear models for prediction; iii) Tree-based models; iv) Models for classification; v) Model combination
Materials (to be published)
MODEL BASED MACROECONOMIC PROJECTIONS
Instructor: Jaromir Benes
Dates: May 9-13 and May 16,20, 2022
This course provided theoretical and practical sessions on macroeconomic modelling, and provided attendees with elements on linear and non-linear modelling training, covering the following aspects: simulation and forecasting techniques in linear models; theoretical introduction to simulation techniques in nonlinear models; practical simulation techniques in nonlinear models; Kalman filtering in nonlinear models; Bayesian estimation with system priors; and practical use of system priors.
Materials
Videos: Part I, Part II
