EViews Tutorial

1. EViews Tutorial

2. EViews Introduction • Eviews will be your partner for this course. • Eviews is a computer program. • Depending on your method of working your assignments can take 2-20 hours. • Prepare yourself to work with Eviews ahead of time. Work smart, not hard.

3. EViews This is your Command Window This is your Work Area This is your desktop

4. Starting a Workfile Match your workbook construction to your needs. Use unstructured/undated when in doubt. If dated match your dates to those of observations. This is very important:Match your frequency to observations. If you know in advance that you will be out of sample forecasting you may need to include room in your workfile for these forecasts. If you do not need this room then…

5. Starting a Workfile (TheEasy Way) Drag your Data into your Work Space

6. A Blank Workfile • Workfiles come with two things • c which represents a constant term • resid which represents your residuals, A.K.A. εt • If you learn one thing from this tutorial, resid = εt

7. Importing Data • Eviews is capable of parsing data from many formats and sources. • If your data is given in many forms, you will need to import the individual pieces. • GUI: File -> Import -> … • Choose the appropriate import technique • If your data has no file extension, or is an unknown filetype try opening it with Notepad. If it opens and is legible either copy the data out, or save it as an acceptable format. • Use google to identify file extensions such as .csv

8. Integrity and Safety • Not all data makes it into Eviews without error. You should visually inspect your data every time. • Not all data provided to you is correct. People make types. File formats are incorrectly changed. • Save your workfile after verifying. • Save often. Eviews will crash. Sometimes as often as 5 times over a 3 hour period.

9. Example of Problematic Data • What is this? A table? • Can you read it? • Can Eviews? • Yes, but are there problems? • This icon means there are strings. • This icon means you have numeric data. • Be mindful of: Commas, Strings, Tables

10. Properly Formatted Data • No indication of strings. • All numerical data. • Note that Excel will put commas in. These are okay.

11. Final Data Check & Interacting With Groups • Doubleclick a group to open it in spreadsheet format. • You can visually inspect each cell or you can work smart. • You can hand-edit entries in spreadsheet format. You can also generate new series and hand edit their entries. • Note: Eviews tracks sources, dates, times. Don’t try to cheat.

12. Final Data Check & Descriptive Statistics • Let Eviews check the data for you. • Generate a histogram. You will often include these in your homework. • Histogram reveals data has no extreme outlying observations, and has sensible values. • To include this histogram in your homework click in the pale yellow area, and simply push CTRL+C (copy), click ok, then paste it into a word document (CTRL+V.) • Save! (Both your workfile and your homework document.)

13. Moving Forward • So far we’ve only created the workfile and verified the data. • Consider the objectives of your assignment. • You may not have been provided with all of the data necessary to complete your assignment.

14. Generating Series (genr) • GUI: Object -> New. • Best used as command line operation. • Creates a new series that performs the mathematical operation to every entry in the original series. • genr ser_name = expression • ser_name = name of your generated series • expression = the mathematical expression which creates the series you wish to create • Type in the command, and then simply hit enter • genr temperaturef = ((temperature___c_*1.8)+32)

15. genr continued • Series will be added to your workfile. (Check it, save.) • Do this until you have all the series your assignment will require. This will often include squares, or the logs of series: • genr seriessq = series^2 • genr logseries = log(series)

16. Seasonal Dummy Variables • An extension of genr • genr <dummyname> = @seas(#) • Example: • genr d1 = @seas(1) • genr d2 = @seas(2) • Be careful that you match your seasons to your data. Do not create 4 seasonal dummies on monthly data.

17. Sampling Series (smpl) • GUI: Quick -> Sample • Command: smpl <begin> <end> • Top Window for editing observations, IE “1 100” is first 100, can also limit by date • Bottom window for conditions, IE “station = 444943” would only include observations from station 444943 • Can use inequality symbols and other logical expressions • Check your data within your sample.

18. Doing Some Actual WorkEquations • All we’ve done so far is get everything ready to complete the assignment. • GUI: Object -> New -> Equation • Equations are stored as both the equation you specify, the sample specified, and also have influence on the rest of your workfile, specifically the last equation to have operated will change c and resid

19. The “c Question” • Whether or not to include c, the constant term, in your equations was a problem that troubled some students all the way until the final assignment. • If you are using dummy variables for every season, do not include c. Why? • Does your data have a constant? Does temperature data? National defense spending?

20. Equations Command: ls temperature latitude Estimation Output view shows there may be some effect on temperature

21. Estimation Output View • You will use this view very often in this course. In fact, very often you will be asked to include the values represented in this view, or the actual window itself. • You could simply click print from Eviews, however that will give you the output on a piece of blank paper. • E.O. view is able to be copy/pasted into Word documents, simply select all (CTRL+A), copy (CTRL+C), and choose Formatted, then paste (CTRL+V.) • Don’t make a new table and spend time filling in the entries. Work smart.

22. Viewing Graphs • View -> Graph -> … • Graphs will also be asked for in your assignments. • To create and view a vs graph, click the value acting as the x-axis, then hold ctrl and click the value to act as the y axis. Right click, open as group, and then view the graph (most often a scatterplot.) • Like the histogram earlier, this can be copied as a metafile, and pasted. (Do so, this helps both the TA and yourself!)

23. Technique: Smoothing • Command: Smooth • GUI: Open Series then Proc -> Exponential Smoothing

24. Smoothing: @movav • Command: @movav(<ser_name>,<interval>) • Must be used with genr. • Generates a moving average on the series name using the interval. • A 2 point moving average on temperature • Genr tempma2 = @movav(temperature,2) • To generate a 2x2 or 2x4 moving average, generate your results first (like above) then take another moving average • Genr tempma24 = @movav(tempma2,4)

25. Technique: Forecasting • Identify the type of forecasting necessary. • In sample forecasting should limit the sample to earlier entries. • Out of sample forecasting requires space in the workfile. • Command: forecast(option) yhat [y_series] • Example (plain): forecast yhat se • You can also sample ahead of a series that has been exponentially smoothed in some cases by using smpl series(#)

26. Lags and Leads • Lag operators <Series>(#) • Example: sales(-3), sales with a lag of 3, sales(3), sales with a lead of +3 • This technique will become important for you to use in later assignments. • genr lag3sales = sales(-3) • smpl sales(5) – will attempt to forecast and generate the next 5 value of sales given workfile room (Remember making space for out of sample forecasts?)

27. Correlograms • Best to access these via GUI. • View -> Residual Tests -> Correlogram Q Statistics • Include a sensible # of lags. 36 for monthly data is good. 12 for annual data. • You can’t copy/paste correlograms normally. You must use print screen, then paste into paint (or other image editor) then hand cut your desired image from there.

28. Comparison • Date: 01/25/08 Time: 15:07 • Sample: 1968M01 1993M12 • Included observations: 312 • Autocorrelation Partial Correlation AC   PAC  Q-Stat  Prob •        .|* |        .|* | 1 0.117 0.117 4.3158 0.038 •        *|. |        *|. | 2 -0.149 -0.165 11.365 0.003 •        *|. |        *|. | 3 -0.106 -0.069 14.943 0.002 •        .|. |        .|. | 4 -0.014 -0.017 15.007 0.005 •        .|* |        .|* | 5 0.142 0.125 21.449 0.001 •        .|. |        .|. | 6 0.041 -0.004 21.979 0.001 •        .|* |        .|* | 7 0.134 0.175 27.708 0.000

29. ARMA Terms • AR and MA terms may be included in equations just like any other series. • Example: • ls sales gdp AR(1) AR(2) MA(1) MA(2) • While seemingly complex, ARMA terms are easy to work with in Eviews. • AR(2) would be both AR(1) and AR(2) • ARMA(2,2) would be both AR(1) and AR(2), and also MA(1) and MA(2)

30. Common Errors • Near Singular Matrix – Most commonly occurs because you used a constant and a full compliment of dummy variables. • Errors that force you to crash – Most commonly occur because Eviews was asked to perform a demanding operation and ran out of memory. • Save!

31. Using Eviews Help • Whenever you’re confused, try using help. • Even if you don’t know the command, entering the process you’re looking for may be found in help, for example least squares

32. Good Ideas • While doing your homework keep a log of any commands or operations you perform alongside your homework. In fact, before each figure or result it doesn’t hurt to list the steps you made. This is a good habit to form for three reasons. First the TA knows that you understand what you’re doing. Second you can help your fellow students. Third, when your homework is returned you have a study guide for quizzes and tests. You will be tested on Eviews material. • If you’re not going to write your explanations in the lab make sure you have all of the figures and information needed for the homework. • Start early. You will make mistakes. Sometimes twice. • Save often.