Advanced Macroeconomic Modeling Summer School in Lisbon, Portugal

July 4 - July 8
Advanced Macro Policy Modeling in Iris/Matlab

 

Application Deadline: June 13

Paper Submission Deadline (optional): May 30

No Seats Available

August 1 - August 5
Advanced Macro Policy Modeling in Iris/Matlab

 

Application Deadline: July 20

Paper Submission Deadline (optional): June 27

No Seats Available

July 11 - July 15
Advanced Macro Policy Modeling in Iris/Matlab


Application Deadline: June 20

Paper Submission Deadline (optional): June 6

No Seats Available

August 8 - August 12
Advanced Macro Policy Modeling in Iris/Matlab

 

Application Deadline: July 27

Paper Submission Deadline (optional): July 3

No Seats Available

July 18 - July 22
Introduction to Python and R for Economists

 

Application Deadline: June 27

Paper Submission Deadline (optional): June 13

No Seats Available

August 15 - August 19
Introduction to Python and R for Economists

 

Application Deadline: August 3

Paper Submission Deadline (optional): July 10

No Seats Available

July 25 - July 29
Semi-Structural and DGSE Models in Dynare–Julia

 

Application Deadline: July 3

Paper Submission Deadline (optional): June 20

No Seats Available

August 22 - August 26
Semi-Structural and DGSE Models in Dynare–Julia

 

Application Deadline: August 10

Paper Submission Deadline (optional): July 17

No Seats Available

No Seats Available

No Seats Available

No Seats Available

No Seats Available

No Seats Available

Olá! Bem Vindo a Portugal

 

The Better Policy Project has launched the 2022 Summer School. We have scheduled 8 one-week-long courses that are designed specifically for policy-making institutions that are trying to develop a new state-of-the-art Forecasting and Policy Analysis System (FPAS) called FPAS Mark II. The new framework stresses the importance of policy institutions as macroeconomic risk managers and not forecasters. At its core, the importance of a baseline forecast is reduced and replaced with more emphasis on scenario building and risk assessments. The new framework is meant to build an arsenal of analytical tools over time to be able to efficiently move from one scenario to another depending on the most up-to-date data and information. The world is unforecastable, uncertainty is very high and therefore, policymaking institutions need to be able to communicate different scenarios and to swiftly adjust policy to avoid the potentially severe negative welfare costs associated with sluggish policy reactions.

The Hybrid Summer School will take place in Carcavelos, Portugal from July 4 to August 26. This summer we offer 3 courses (details and dates below):

 

Our Summer School courses mostly use open-source software (Dynare, Julia, Python, R, IRIS etc.). Every course  includes lectures and some hands-on work on course-related topics. The lectures cover issues such as how to design analytical frameworks to support fiscal and macroeconomic policies. All participants will also be introduced to The Better Policy Project’s Scenario builder and will receive a 1-month subscription to the Global Forecasting School. And for those interested in understanding trends in the oil market there will be a lecture on nonlinear models of the oil market and how to simplify these nonlinear models for more practical scenario building.

Paper Submission: All participants are welcomed to present their research work to course lecturers. The lecturers will be happy to provide feedback and comments. The authors of the best papers will be given a chance to present at The Better Policy Project's Seminar Series.

All of our courses start with introduction to and application of  GFS (Global Forecasting School) Scenario Builder.

The foundation of FPAS Mark II puts much more emphasis on uncertainty and nonlinearities. The development of
different scenarios by the
Global Forecasting School (GFS) helps us to flesh out with different economic stories about what uncertainty might mean and how to present this analysis to policymakers. It is very important for many decisionmakers in society to have easier access to the types of forward-looking analytical frameworks that have been developed in the leading FPAS policymaking institutions. 


The development of FPAS Mark I analytical frameworks in leading central banks such as New Zealand, Czech Republic and Chile have provided the foundation for much higher levels of monetary policy transparency. The investment in FPAS frameworks has also resulted in a massive increase in central bank efficiency compared to non-FPAS central banks.


Thanks to the development of open-source software, the tools that were once limited to highly trained modelers in the most advanced FPAS Mark I central banks are now becoming much more accessible to other central banks hoping to catch up to the leading FPAS central banks. The GFS provides a very practical training opportunity for staff and policymakers to learn how to develop analytical frameworks for surveillance systems designed to make sense of a very complex world and to use much more sophisticated nonlinear models to study the policy implications of nonlinearities and uncertainty. The example that the GFS is focusing on is how to think about the risks of falling behind shifts in the Phillips Curve and allowing long-term inflation expectations to ratchet upwards.

The
GFS is creating an integrated system to support decision-making under uncertainty. The system includes multiple ingredients: monitoring, data generation, nowcasting, medium and long-term projections and reporting. The US scenario builder is another part of the GFS. It is a front-end application in R, Python, Julia and Java that can be GFS follows 2 main principles:

 

  • Human capital is the key. It is very important to invest in training and continuous education of people.

  • Never say model says. Nobody should ever expect to get answers just from models. Models can only be some of many tools used by decisionmakers, but they can never give ultimate “correct” answers.


At first, GFS mechanism will be applied to the US economy given its global importance as a financial shock emitter, but later will be extended to other countries.

 

Advanced Macro Policy Modeling in Iris/Matlab

This course is based on using the historical-narrative approach to develop plausible estimates of output gaps that are relevant for assessing conventional monetary policy trade-offs as well as other measures that are useful for measuring lower-frequency financial cycles. The course follows the work done in the Mind the Gaps paper.

 

Half the course will be hands-on using IRIS/MATLAB for modeling exercises, but at some point, there will also be versions in Dynare/JULIA, which is 100% free open-source software.

The course includes daily lectures on topics such as adding macroprudential, fiscal policy and oil market.

The course will also introduce participants to the Global Forecasting School and the modeling and analysis tools being developed under that project which includes a scenario builder, open-source surveillance reports and methods for thinking about financial stability.

What will you learn?

  • GFS Scenario Builder.

  • Small open economy models in Iris/Matlab useful for monetary policy and financial stability.

  • Foundational knowledge of FPAS Mark I central banking analytical frameworks.

  • Surveillance in Python and R.

  • Lectures on Adding Macroprudential, Fiscal Policy and Oil Market.

Requirements:

  • This course is designed for people working in central banks, ministries of finance and financial institutions.

  • Have at least intermediate knowledge in statistics and macroeconomics.

  • MATLAB 2017a or later versions. IRIS-Toolbox-Master will be provided by instructors.

Lecturers: Douglas Laxton and others

Price: 1000 Euros (1 participant, 1-week course)

Schedule: Monday – Friday (Lisbon Time (GMT+1))

  • 10:00 – 13:00 Lectures,

  • 13:00 – 14:00 Lunch,

  • 14:00 – 17:00 Assignments and Presentations.

 

Introduction to Python and R for Economists

Central banks, universities and other research institutions spend massive amounts of resources every year on software licences. With the development of open-source software (Python, R, Julia etc) there is an enormous opportunity to save and use these resources more efficiently to develop human capital and improve the
quality/quantity of central bank policy analysis, forecasting and policy-relevant research.


This course guides participants from the basics of Python and R to becoming skilled users. It is designed for economists and researchers to help them organize and report data in a fast, effective, and beautiful way. Unlike other courses which only provide general knowledge of Python or R, this course is result-oriented and focuses on using combination both for surveillance and research. By the end of the course participants will have a powerful framework that can be used for database management, visualization, reporting, estimation, near-term forecasting, etc.).

What will you learn?

  • GFS Scenario Builder​.

  • Basics and advantages of Python and R.

  • Data structures in Python and R.

  • Data manipulation and database management.

  • Data visualization.

  • Statistical analysis and filters.

  • Web scraping, data Extraction and automation.

  • R Markdown.

  • R Shiny: Creating interactive web apps.

Requirements:

  • ​Python 3.9.

  • Anaconda.

  • R (Version 4.0.3 or the latest ones up to 4.1.1).

  • RStudio.

  • MiKTeX.

 

Lecturers: Douglas Laxton and others

Price: 1000 Euros (1 participant, 1-week course)

Schedule: Monday – Friday (Lisbon Time (GMT+1))

  • 10:00 – 13:00 Lectures,

  • 13:00 – 14:00 Lunch,

  • 14:00 – 17:00 Assignments and Presentations.

 

Semi-Structural and DGSE Models in Dynare – Julia

Welcome to the big leagues! This course will provide a tour through the development of state-of-the-art semi-structural and DSGE models. These are Overlapping Generations (OLG) models with well-defined steady states, where we can study among other things the transitional dynamics from one steady state with low levels of government debt to steady states with much higher levels of government debt.  Participants will be taught how to think through the economics of large-scale DSGE models like Global Integrated Monetary and Fiscal Model (GIMF) as well as semi-structural models like MULTIMOD and Flexible System of Global Models (FSGM).  

Participants will also be given a crash course in understanding divide-and-conquer strategies to solve mixed-complementarity problems that involve nasty occasionally-binding constraints. Examples of occasionally-binding constraints include the Effective Lower Bound (ELB) on the Policy Rate as well as potentially efficient unconventional policy strategies that are designed to replace the policy space that is lost by hitting the ELB.

The course will be partly hands on showing participants how to build semi-structural closed-economy and open-economy models in a free and open-source Julia-based version of DYNARE.

Course includes elements from the Global Forecasting School, using R and Python for Surveillance, and daily lectures on examples of useful DSGE models.

What will you learn?

  • GFS Scenario Builder.

  • Small open economy models.

  • GIMF for fiscal policy (multipliers, stock-flow dynamics, distortionary taxes, government investment on infrastructure, etc.).

  • Lectures on Adding Macroprudential, Fiscal Policy and Oil Market is semi-structural and DSGE models.

Requirements:

  • Dynare 5.1.

  • Julia v1.7.2.

  • Visual Studio Code (If the participants do not have the required software, instructors will help to install those).

Lecturers: Douglas Laxton and others

Price: 1000 Euros (1 participant, 1-week course)

Schedule: Monday – Friday (Lisbon Time (GMT+1))

  • 10:00 – 13:00 Lectures,

  • 13:00 – 14:00 Lunch,

  • 14:00 – 17:00 Assignments and Presentations.