Register for Training

Class size is small and content tailored to fit participants’ interests. The teaching approach is interactive, with discussion of practical concepts coupled with working through examples of model selection, estimation, and forecasting with illustrative macroeconomic and financial datasets.

Breakfast, lunch, and coffee will be provided throughout.

For more information please contact:
Pete Ungberg
Product Manager
Phone: +1 212 986 9300
E-mail: pete@haver.com

Course Descriptions

Day 1

R Bootcamp

Wednesday, June 6th
8:30 AM – 5:00 PM

"R Bootcamp" is a 'hands on' introduction to the power of R language for data handling, manipulation, analysis and presentation. "R Bootcamp" is highly recommended even for the most experienced user, as Haver database integration and an introduction to the ggplot2 graphing package will be covered in detail.

Topics will include:

• R language and environment for statistical computing and graphics
• An introduction to RStudio, working with time series data, transforming series, graphing, exporting output to Word and Excel, and links to useful sites to retrieve programs and packages, R community
• Powerful data import features and integration with Haver databases
• Graphing time series data using ggplot2 package
• Calculating means, variance, covariance, and hypothesis tests
• Data analysis: time series theory, correlograms, serial correlation tests
• Regression analysis, specification, interpreting results, and functional form

Days 2 & 3

Applied Time Series with R

Thursday, June 7th to Friday, June 8th
8:30 AM – 5:00 PM

"Applied Time Series with R" is the follow up to "R Bootcamp". The objective is to introduce fundamental time-series concepts and teach their implementation with a wide selection of packages available in R. 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.

Thursday's topics will include:

• Univariate time series models, motivation, estimation, and forecasting
• Model selection: R-squared, Mean Squared Error, Akaike and Schwarz information criteria
• Serial correlation
• Unit root tests
• Forecast evaluation
• Introduction to multivariate models

Friday's topics will include:

• Vector auto-regressions: estimation and forecasting
• Granger causality
• Impulse response analysis
• Co-integration, Engle-Granger test
• Vector error correction models
• Forecasting with multivariate models
• Principal component analysis and application to investments and forecasting

Required Equipment and Software

Participants are expected to bring their own laptops equipped with R, loaded with R Studio, 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.

or call +1 212 986 9300 or email Pete Ungberg