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Python is a long-standing and popular high-level programming language for general-purpose programming. Being an extensible language, it features thousands of add-ons for scientific and statistical computing and for data science. For example, the extension packages NumPy, pandas, matplotlib, and statsmodels implement versatile matrix programming features, and provide powerful tools for time series data handling, model estimation, and data visualization. Python is managed by the Python Software Foundation.

Econometrics Training:

Applied Time Series in Python

May 4 - May 6, 2020 from 8:30 AM - 5:00 PM

60 East 42nd Street, New York, NY 10165

  • $850
    1 day of training

  • $1600
    2 days of training

  • $2200
    3 days of training

Register for Training

Course size is limited to encourage interaction and close learning with instructor.

Breakfast, lunch, and coffee will be provided throughout.


For more information please contact:
Pete Ungberg
Product Manager
Phone: +1 212 986 9300
E-mail: [email protected]

Python instructor

Abdel M. Zellou, Ph.D.
Abdel Zellou holds a Ph.D. in mineral and energy economics from the Colorado School of Mines and co-founded Clear Future Consulting.

Course Descriptions

Day 1

Python Bootcamp

Monday, May 4th
8:30 AM – 5:00 PM

"Python Bootcamp" introduction to the power of Python language for data handling, manipulation, analysis and presentation. "Python Bootcamp" is highly recommended even for the most experienced user, as Haver database integration and an introduction to Python visualization will be covered in detail.

Topics will include:
  • Intro to Python environment via Anaconda, and Jupyter notebooks
  • Loading data into Python and spreadsheet import
  • Data visualization and graphing featuring Matplotlib and Seaborn
  • Haver database link with Python - database queries, arguments, and search functionality
  • Time-series data analysis - summary stats, correlograms, tests for auto-correlation
  • Data transformations - natural logs, working with differences
  • Regressions in Python - OLS model and applied examples

Days 2 & 3

Applied Time Series Pt. 1

Tuesday, May 5th to Wednesday, May 6th
8:30 AM – 5:00 PM

"Applied Time Series with Python" is the follow up to "Python Bootcamp". The objective is to introduce fundamental time-series concepts and teach their implementation with a wide selection of packages available in Python. Real-world economic and financial data is used throughout the two days giving participants the opportunity to explore data and view results directly related to their line of work.

This two-day course is designed to be applied with a hands-on approach taken. Applicable time-series theory is introduced, but formal proofs are not explored in depth. The objective of the course is to leave participants with a catalog of time-series models and the knowledge to know when and how to apply each. Throughout the course, participants will be given additional resources and references for further exploration.

Day 2 topics will include:
  • Univariate models in Python
  • Model selection: R-squared, Mean Squared Error, Akaike and Schwarz information criteria
  • Serial correlation
  • Unit root tests
  • Forecast evaluation
  • Introduction to multivariate models
Day 3 topics will include:
  • Vector auto-regressions (VAR) - multivariate mode
  • Co-integration and Engle-Granger test
  • Vector Error Correction Models (VECM)
  • Principal component analysis and application to investments and forecasting

Required Equipment and Software

Participants are expected to bring their own laptops equipped with Python, Anaconda, Jupyter, Excel, and Adobe Acrobat Reader.* Other course materials will be provided.

* A limited number of laptops may be loaned on-site in case of technical issues

Cancellation Policy

Cancellations received prior to two full business days before the date of the seminar will be honored. For cancellations received after the two day deadline but before the date of the seminar, the full fee less $150 will be converted to a transferable, nonrefundable credit to be applied toward a future seminar. Any credits issued must be used within one year. If notification of cancellation is not received prior to the first day of the seminar the full fee is payable.

Note: Colleague substitution permitted with no penalty.

Register or call +1 212 986 9300 or email Pete Ungberg