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Remote Training:

Applied Time Series in Python

Bootcamp and Econometric Tools 1 & 2

6 days of training

Haver partners with Clear Future Consultants to offer a two week immersive online course introducing participants to the power of the Python programming language. This appiled hands-on training develops statistical economics skills with a concentration on economics and finance.

Gain proficiency in building modern time-series models in Python through real-world applications. Designed for professionals in both the public and private sector, the course does not require prior experience, assuming only that participants have a basic understanding of regression and statistical concepts.

Leveraging virtual training software and small class sizes, instructors work closely with all attendees, encouraging group discussion and improvement through practice examples. Participants will also receive custom training manuals, annotated Python notebooks, video tutorials, and certificates of completion.

Register for Training


For more information please contact:
Pete Ungberg
Product Manager
E-mail: [email protected]

Python instructor

Daniel L. Jerrett, Ph.D.
teaches econometrics and forecasting at the University of Colorado Denver. He has over 15 years of econometric experience in both the private and public sector


Session 1: Bootcamp Part 1

  • Introduction to Python
  • Working with dataframes

Session 2: Bootcamp Part 2

  • Introduction to NumPy
  • Introduction to pandas
  • OLS regressions

Session 3: Univariate Models Part 1

  • Data transformations
  • Introduction to time series data
  • Overview of univariate modeling
  • Estimating ARIMA models

Session 4: Univariate Models Part 2

  • Forecasting with ARIMA models
  • Non-stationarity and trending data
  • Unit root tests

Session 5: Multivariate Models Part 1

  • Overview of vector autoregressions
  • Estimating VAR models
  • Grander causality
  • Impulse response functions
  • Forecasting with VAR models

Session 6: Multivariate Models Part 2

  • Overview of cointegration
  • Engle-Granger test
  • Estimating VECM models

Course Goals

  • Proficiency working in Jupyter notebooks and the Python environment
  • Manipulating data using numpy and pandas
  • Leveraging the Haver Python library
  • Working with time-series data
  • Estimating, evaluating, and forecasting ARIMA models
  • Understanding the concept of non-stationarity and testing for unit roots
  • Estimating autoregressions, granger causality tests, and impulse response functions
  • Understanding the concept of cointegration and testing using the Enger-Granger test
  • Estimating and forecasting with vector error correction models

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