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Nowcasting with High-Frequency Information

Nowcasting with High-Frequency Information

Learn with our expert faculty

facultyLuca Gambetti
PhD, Universitat Pompeu Fabra
UAB and BSE
Director
facultyGabriel Pérez-Quirós
PhD, University of California San Diego
Unit Head of Macroeconomic Research, Bank of Spain
Instructor

Course overview

The Covid-19 crisis and the Ukrainian war revealed the need to address current conditions of the economy with higher frequency than the traditional methods allowed. Weekly and daily variables shed light on the timing and the magnitude of the actual developments of the economy while traditional monthly and quarterly variables were silent about it.

Besides this, the lack of representativeness of these new high-frequency information variables, together with  noise, non-linearities, and seasonal effects, also mislead our assessment of the dynamics of the economy. New methods are being developed to combine traditional variables with new information sets, to create timely and robust indicators to address the current conditions of the economy, in real-time, daily or weekly.

This brand-new course teaches you the most recent techniques in macroeconomic nowcasting and forecasting, with particular attention to “special situations” that most courses do not cover, those in which traditional econometric models either fail or are silent about informing the economic developments. For example, after the covid crisis, the magnitude of the shock was unprecedented, the uncertainty in those months was so high that either nothing could be said or whatever we could say had such an uncertainty that it was not informative. This course will provide participants with tools to address those special situations.

You will be able to produce forecast and nowcast even when standard techniques fail due to exceptional economic conditions

If you are a macroeconomist and need to understand how economic analysis in real-time can be provided, you will find this course extremely useful as it is one of the very few courses on this topic.

Having successfully completed this course, you will be also able to create your own tools, based on your specific problem, to inform decision-makers even when uncertainty is high and standard techniques are useless. You will learn to do so through the presentations in class, the reading of the material, and especially, through the detailed analysis of computer codes. If you have a specific problem to solve, you will be able to apply directly the techniques covered in class to your case.

The course consists of theory sessions (10 hours) and practical sessions (10 hours). During the practical sessions, we will use MATLAB to replicate the methodology as well as the empirical findings documented in the lectures.

You will participate in an active and fruitful environment with international colleagues without incurring high costs, thanks to online live delivery.

Sessions will be recorded and videos will be available for a month once the course has finished.

Learn to analyze current economic conditions and produce forecast using high-frequency data

This course will be taught online but it will be live and interactive.

INTENSIVE COURSE

Nowcasting with High-Frequency Information

Applications will open soon!
  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 19, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Early-bird payment deadline: January 19, 2024

  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 19, 2024 at 23:59 (CET)

See below for reduced fee eligibility

Apply to join this course

Last day to apply: February 20, 2024

  ONLINE
Regular Fee 1300 €
Reduced Fee 775 €

10% early-bird discount applies to payments made on or before January 19, 2024 at 23:59 (CET)

See below for reduced fee eligibility

This edition is closed. Next edition TBA.

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Schedule

Times listed are Central European Time (CET). Compare with your time zone on time.is

Schedule for Week 1
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Lecture Practical Lecture Practical Lecture
Schedule for Week 2
Day / Time Mon Tue Wed Thu Fri
14:30 - 16:30 Practical Lecture Practical Lecture Practical

Course content

Read an outline of topics and bibliography for this course

Course outline

1.Nowcasting with standard economic data

  1. Linear framework: Kalman filter. Mixed frequency factor models and VARs. References: (6), (7)
  2. Non-linear framework: Markov-Switching and Threshold Models. Multivariate time varying specifications. References: (8), (9)
  3. MIDAS Models. References: (10), (11)

2.   Challenges of high frequency data

  1. Modelling and forecasting with high frequency data (12) (13) (14) (15)
  2. Mixing high frequency data with standard information sets (16) (17)
  3. Non-linear high frequency data models (18)
  4. Special topics on high frequency data (19) (20) (21)
    1. Data Selection
    2. Seasonality
    3. Signal to noise in high frequency data

3.   Special topic. Machine learning and high frequency data. Model comparison. (22)

 

References

In addition to the general references from advanced time series books that are always helpful to deal with some of the details,

 1. James Hamilton. “Time Series Analysis” Princeton. (1994).

2. Chang-Jin Kim, Charles R. Nelson. “State-Space Models With Regime Switching: Classical and Gibbs-Sampling Approaches With Applications”. MIT Press (1999).

3. Andrew Blake and Haroon Mumtaz. “Applied Bayesian Econometrics for Central Bankers” (2007). Bank of England.

4. Helmut Lütkepohl “New Introduction to Multivariate Time Series Analysis”. (2005).

5. Fabio Canova.” Methods for Applied Macroeconomic Research” (2007)

Specific references for our course will be:

6. Camacho, M. and Perez Quiros G. (2010) “Introducing the euro-sting: Short-term indicator of euro area growth” Journal of Applied Econometrics, Volume25, Issue 4

7. Schorfheide, F and Song, D. (2021) Real-Time Forecasting with a (Standard) MixedFrequency VAR During a Pandemic “Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic”. NBER Working paper 29535

8. Leiva Leon, D, Perez Quiros, G and Rots, E. Real-time weakness of the global economy a first assessment of the coronavirus crisis. ECB Working Paper 2381. March 2020

9. Eo, Y. and C.-J. Kim (2016). Markov-switching models with evolving regime-specific parameters: Are postwar booms or recessions all alike? Review of Economics and Statistics 98 (5), 940–949.

10. Bai, Jennie, Eric Ghysels and Jonathan Wright (2010), “State Space Models and MIDAS Regressions.” Econometric Reviews Volume 32, 2013 - Issue 7

11. Eric Ghysels & Pedro Santa-Clara & Rossen Valkanov, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," CIRANO Working Papers 2004s-20, CIRANO.

12. Hale, T, A Petherick, T Phillips, S Webster, B Kira, N Angrist and L Dixon (2020), “Oxford COVID-19 Government Response Tracker”, Blavatnik School of Government, Oxford University

13. Lewis, D., K. Mertens, and J. Stock (2020): “U.S. Economic Activity During the Early Weeks of the SARS-Cov-2 Outbreak,” FRB Dallas Working Paper, 20-11.

14. Lewis, D., K. Mertens, and J. Stock. 2020. “Monitoring Real Activity in Real Time: The Weekly Economic Index,” Federal Reserve Bank of New York Liberty Street Economics, March 30.

15. Chetty, Raj, John N. Friedman, Nathaniel Hendren, Michael Stepner, and the Opportunity Insights Team (2020). "How Did COVID-19 and Stabilization Policies A§ect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data," mimeo, Harvard University.

16. Perez Quiros, G. and Saiz, L. “Introducing the Eurotracker” ECB Mimeo 17) Gonzales, F, A Jaax and A Mourougane (2020), “Nowcasting aggregate services trade. A pilot approach to providing insights into monthly balance of payments data”, Paris: OECD Publishing.

17. Christiane Baumeister, Danilo Leiva-León & Eric R. Sims. “Tracking Weekly State-Level Economic Conditions” NBER Working Paper 29003. July 2021 19) Cuevas, Á., Ledo, R. & Quilis, E.M. Seasonal adjustment of the Spanish sales daily data. SERIEs 12, 687–708 (2021).

18. Manuel Medina Magro (2022) Nowcasting The USA Real Quarterly GDP Growth With a Seasonally Adjusted Weekly Economic Index. U. of Alicante (mimeo)

19. Sercan Eraslan & Thomas G ö tz. (2021). “An unconventional weekly economic activityindex for Germany”. Economics Letters, Volume 204, 109881, ISSN 0165-1765.

20. Woloszko, N (2020), “A Weekly Tracker of activity based on machine learning and Google Trends”, OECD Economics Department Working Papers 1634, Paris: OECD Publishing.

21. Maccarrone G, Morelli G, Spadaccini S. GDP Forecasting: Machine Learning, Linear or Autoregression? Front Artif Intell. 2021 Oct 15;4:757864.

 

 

Prerequisites

The course is self-contained, but although not required, knowledge in mathematics and/or statistics at graduate level could help to take further advantage of the course.

Course materials

Every participant will receive a time-limited personal free license of MATLAB several days before the start of the course. You will need to install the MATLAB software on your own computer for use during the practical sessions.

Additional class materials will be provided before and during the sessions. The instructors will also be available throughout the course to discuss your individual research ideas and projects.

Participant Profile

This course is specifically aimed at: 

  • Practitioners at central banks as well as other private and public institutions which need to provide economic analysis in real-time.
  • Masters and Ph.D. students in economics or related fields that want to incorporate in their work real-time tools or want to develop new tools to analyze economic activity
  • Researchers in general that want to learn about uncertainty and decision-making in real-time
  • Students in any quantitative field that want an overview of the techniques used in real economic analysis and incorporate these techniques to their own field.

Fees

The fees for this course are listed below and all available discounts can be found here. 

* Reduced Fee applies for PhD or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.

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