EViews is a great statistical package that is used by many people for both research and teaching purposes. From EViews drop down menu one can easily run simple OLS regressions, conduct hypothesis testing, obtain misspecification tests, estimate non-linear least squares, estimate simultaneous equation models, test for unit roots and cointegration, estimate long-memory models and time varying conditional variance specifications, run VARs, VECMs and Bayesian VARS, obtain impulse responses from structural VARs and estimate mixed frequency models (MIDAS). There are efficient modules that provide estimates of a bunch of models and report the best one in terms of goodness of fit and/or information criteria. It is also possible to estimate models with hundreds of equations and to implement policy simulations and scenarios. EViews community is very large and researchers have written small packages, called Add-ins that can be directly downloaded in EViews to perform additional operations.
This being said, sometimes one would like to go further, to do something that is not implemented (yet?) in the existing EViews routines. In addition, one may wish to make life easier and to ask EViews to perform some repetitive tasks. This is where EViews Programming starts. The goal of this training course is to make life easier, namely to do things with only a small investment instead of learning a completely new language. The course shows how it is possible to remain in the user-friendly EViews environment without using external resources (e.g. using R, GAUSS), with the need to save data, to read them, to reimport output in EViews.
This two-day course provides a complete introduction to EViews programming and it is suitable to intermediate level users of EViews with some interest in applied econometrics. The course covers three main topics.
- (i) Writing a simple program that can be run, stored in an existing EViews file, and used by other commands. EViews is a programming language that has its own specific commands. As an example @trend generates a linear trend, @first denotes the first observation in the sample, @expand(@quarter, @drop(1)) generates seasonal dummies, @sum computes the sum of a series.
- (ii) Simulating data. What is the difference between difference stationary and trend stationary time series for instance? Those processes can be generated, then saved in a EViews file before being plotted or used in a regression to illustrate the spurious correlation issue. Conducting Monte Carlo simulations, namely the investigation of the small sample properties of a test procedure. We could evaluate the behaviour of the Dickey-Fuller test in the presence of ARCH effects, we may want to generate critical values for a test that does not exist and for which those values have not been tabulated yet. Thanks to the random generators we can loop and replicate processes 1,000 times to find the results.
- (iii) Doing repetitive tasks. Loops are very useful when performing similar tasks such as a regression with different explanatory variables or when doing forecasts for different subsamples (i.e. rolling windows). A repetitive task is also updating the forecasts of the growth rate of the real GDP every time new data is released. Similarly, a large macro model with hundreds of equations can be reestimated when new data is released and resimulated directly by running the model in a program.
This course has a learning ratio of approximately 30% Theory, 70% Practical
- Session 1: Introduction to EViews, reading files, transforming data, ARMA automatic modelling, do and don't (because it exists in the drop down menu and/or Add-ins already);
- Session 2: The basic structure of a program (reading files, transforming data, saving; elements, performing tasks). Comparison with OLS (ls command) and the help of the capture mode;
- Session 3: Generating set of commands, important @ functions;
- Session 4: Matrix and vectors, graphs, tables.
- Session 1: Pseudo random number generators (normal and Student's t).
- Session 2: For-next loops and if-then-else;
- Session 3: Monte Carlo simulations;
- Session 4: Repetitive automatic tasks: rolling window forecasts, simulating macro models every quarter
Not necessary for this course.
- Econometric knowledge: knowledge of basic econometrics (at a minimum: time series regression, least squares estimation, mis-specification testing, univariate cointegration)
- Software knowledge: intermediate knowledge of EViews (any version, although version 10 is preferable)
- Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
- Additional discounts are available for multiple registrations.
- Cost includes course materials, lunch and refreshments.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, laptops can be hired for a fee of £10.00 (ex. VAT) per day).
- If you need assistance in locating hotel accommodation in the region, please notify us at the time of booking.
- Payment of course fees required prior to the course start date.
- Registration closes 5-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.