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Software

R

R is an open source software environment for statistical computing and graphics which can be used for data manipulation, calculation, and display. It can also function as a data handling and storage facility and has multiple tools for performing techniques such as linear and nonlinear modeling, time series analysis, classification, and clustering. R is designed as a true computer language and allows users to add capabilities by writing and defining new functions.

R training

Dates TBA

CCT Venues-Barbican Aldersgate House, 135-137 Aldersgate Street

  • $950
    1 day of training

  • $1800
    2 days of training

  • $2500
    3 days of training

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.

Register

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

R instructors

Daniel L. Jerrett, Ph.D.

Daniel teachers econometrics and forecasting at the University of Colorado Denver.  He has over 15 years of experience in both the private and public sector and co-founded Clear Future Consulting.

Course Descriptions

R Bootcamp

Dates TBA
8:30 AM – 5:00 PM

Bootcamp is a 'hands on' introduction to the power of R econometric software for data handling, manipulation, analysis and presentation. Bootcamp is highly recommended even for the most experienced user, as Haver database integration and writing R programs will be covered.

Topics will include:
  • R language and environment for statistical computing and graphics
  • R navigation: RStudio, data (time series, cross-section and panel), transforming series, graphing, exporting output to Word and Excel, and displaying descriptive statistics, links to useful sites to retrieve programs and packages, R community
  • Powerful data import features and integration with Haver databases
  • Data analysis: correlograms, white noise
  • Calculating means, variance, covariances, and moving averages
  • Basic regressions and exporting estimation results to Excel-based models
  • P-values and their extensive usage
  • Introduction to robust standard errors and hypothesis testing

Econometric Tools

Dates TBA
8:30 AM – 5:00 PM

Econometric Tools is an intermediate course. The objective is to review fundamental econometric concepts and teach their implementation with a wide selection of the advanced tools available in R.

Tuesday's topics will include:
  • Regression with robust standard errors
  • Hypothesis testing
  • Model selection: nesting and testing, Akaike and Schwarz information criteria
  • Univariate time series models, estimation and forecasting
  • Serial correlation
  • Multivariate Models
  • TS-DS/ARMA models
Wednesday's topics will include:
  • Simulating stochastic processes; Monte Carlo
  • Stationarity issues
  • Simple programs for carrying out repetitive tasks
  • Simultaneous equations
  • Vector auto-regressions: estimation and forecasting
  • Granger causality
  • Impulse response analysis
  • Cointegration
  • Vector Error Correction Models

Required Equipment and Software

Participants will be provided with laptops equipped with R, R Studio, Excel, and Adobe Acrobat Reader.  Other course materials will be provided.

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