Monday, May 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.
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 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
Tuesday, May 7th - Wednesday May 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.
Tuesday's topics will include:
Univariate time series models, motivation, estimation, and forecasting
Model selection: R-squared, Mean Squared Error, Akaike and Schwarz information criteria
Unit root tests
Introduction to multivariate models
Wednesday's topics will include:
Vector auto-regressions: estimation and forecasting
Impulse response analysis
Co-integration, Engle-Granger test
Vector error correction models
Forecasting with multivariate models
1 day: $850 2 days: $1600 3 days: $2200
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.
Cancellations received prior to five full business days before the date of the seminar will be honored. For cancellations received after the five day deadline but before the date of the seminar, the full fee less $200 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. Colleague substitution permitted with no penalty.
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.