EABCN Training School - What's New in Mixed Frequency Data (MIDAS), with Applications to Machine Learning and Big Data
with Applications to Machine Learning and Big Data
Eric Ghysels (University of North Carolina – Chapel Hill)
Massimiliano Marcellino (Università Bocconi)
Jonas Striaukas (Copenhagen Business School)
Online via Zoom
26-28 September 2022
2 PM / 7:30 PM (est.) (GMT)
Deadline: 6pm (GMT), Wednesday 24 August 2022
We are pleased to announce details of the latest EABCN Training School; a three-day course entitled “What's New in Mixed Frequency Data (MIDAS), with Applications to Machine Learning and Big Data”. Professors Eric Ghysels and Massimiliano Marcellino will teach the course. It is primarily aimed at participants in the Euro Area Business Cycle Network but applications will also be considered from doctoral students, post-doctoral researchers and economists working in central banks and government institutions outside of the network, as well as commercial organisations (fees are applicable for non-network non-academic organisations).
The focus of the course is the use of mixed frequency data in economics and finance. A variety of single and multiple equation models will be considered, for both small and large datasets, combined with alternative estimation and inference techniques. Theory and practical implementations will be covered.
The course is divided into three lecture sessions, each followed by a practice session.
Day 1 (Ghysels)
Introduction to MIDAS regression and related econometric methods
Regularized machine learning MIDAS regression models
Day 2 (Ghysels)
Regularized machine learning MIDAS regression models
High dimensional mixed frequency panel data regression models
High dimensional mixed frequency factor models
Day 3 (Marcellino)
Mixed frequency VARs and Mixed frequency factor models
Parameter time variation in mixed frequency models
Structural mixed frequency models
Mixed frequency models for the tails
Day 1 (Striaukas)
MIDAS regressions (data construction, weight functions, estimation methods, prediction). Applications.
Regularized machine learning MIDAS regressions (data construction, group structures, tuning parameter selection, estimation, prediction). Applications.
Day 2 (Striaukas)
Implementation of Granger causality with regularized machine learning MIDAS regressions. Application.
High dimensional MIDAS panel data regressions (pooled, fixed effects, group structures, estimation, prediction and inference). Applications.
Day 3 (Striaukas)
Mixed frequency VARs (impulse response analysis) and mixed frequency factor models (estimation, prediction and inference). Applications.
Mixed frequency quantile regressions (estimation and prediction). Application.
The course will take place online via Zoom. The course will run from Monday 26th – Wednesday 28th September. Each day the session will begin at 2 PM (GMT) and with an estimated finish time of 7:30 PM (GMT) each day. More information will be circulated to successful applicants closer to the date.
We ask that you send a current version of your CV. PhD students must also specify in which way the school will be useful for their current research (max 300 words).
Participants from non-academic institutions where the employer is not a member of the EABCN network are charged a course fee of EUR1000. We reserve the right to deny access to the course if payment has not been completed in due time.
How to apply:
Candidates who are CEPR affiliated or already have a CEPR profile should apply by submitting their CV online:
1) Log in on the CEPR portal online at https://portal.cepr.org/
2) Go to https://portal.cepr.org/meetings/1559/info
3) If you are a member of the MEF programme area, click on "Change registration details", complete the requested information and click "Submit information".
4) If you have a CEPR profile, click on "Step 1: Apply" and complete the requested information and click "Register"
Candidates who are not CEPR affiliated or do not have a CEPR profile should apply by submitting their CV online:
1) Create an online profile here
2) Log in on the CEPR portal online at https://portal.cepr.org/
3) Go to https://portal.cepr.org/meetings/1559/info
4) Click on "Step 1: Apply" and complete the requested information and click "Register"
The application deadline is 6pm (GMT), Wednesday 24 August 2022.
If you have any difficulty in applying please contact, Lydia Williams, CEPR Events Officer at [email protected] for assistance, with the subject line ‘1559- EABCN Training School -Ghysels, Marcellino, and Striaukas - Online, 2022'
About the Instructors:
Massimiliano Marcellino is professor of Econometrics in the Economics Department of Bocconi University and fellow of CEPR and IGIER. Previously, he held the Pierre Werner Chair at the European University Institute, where he was also Director of the Department of Economics. He has published over one hundred academic articles in leading international journals on empirical macroeconomics, econometrics, economic statistics and forecasting, his main areas of research, teaching and consulting. He has acted as an advisor for several institutions, including the ECB, Bank of Italy, Bundesbank, Eurostat, BIS, IADB and IMF.
Jonas Striaukas is an assistant professor of Statistics in the Finance Department of the Copenhagen Business School. He obtained his Ph.D. from the Université catholique de Louvain in Belgium. His research focuses on machine learning and big data analysis in econometrics with a particular focus on mixed frequency data models.