
- •Table of Contents
- •Foreword
- •Chapter 1. A Quick Walk Through
- •Workfile: The Basic EViews Document
- •Viewing an individual series
- •Looking at different samples
- •Generating a new series
- •Looking at a pair of series together
- •Estimating your first regression in EViews
- •Saving your work
- •Forecasting
- •What’s Ahead
- •Chapter 2. EViews—Meet Data
- •The Structure of Data and the Structure of a Workfile
- •Creating a New Workfile
- •Deconstructing the Workfile
- •Time to Type
- •Identity Noncrisis
- •Dated Series
- •The Import Business
- •Adding Data To An Existing Workfile—Or, Being Rectangular Doesn’t Mean Being Inflexible
- •Among the Missing
- •Quick Review
- •Appendix: Having A Good Time With Your Date
- •Chapter 3. Getting the Most from Least Squares
- •A First Regression
- •The Really Important Regression Results
- •The Pretty Important (But Not So Important As the Last Section’s) Regression Results
- •A Multiple Regression Is Simple Too
- •Hypothesis Testing
- •Representing
- •What’s Left After You’ve Gotten the Most Out of Least Squares
- •Quick Review
- •Chapter 4. Data—The Transformational Experience
- •Your Basic Elementary Algebra
- •Simple Sample Says
- •Data Types Plain and Fancy
- •Numbers and Letters
- •Can We Have A Date?
- •What Are Your Values?
- •Relative Exotica
- •Quick Review
- •Chapter 5. Picture This!
- •A Simple Soup-To-Nuts Graphing Example
- •A Graphic Description of the Creative Process
- •Picture One Series
- •Group Graphics
- •Let’s Look At This From Another Angle
- •To Summarize
- •Categorical Graphs
- •Togetherness of the Second Sort
- •Quick Review and Look Ahead
- •Chapter 6. Intimacy With Graphic Objects
- •To Freeze Or Not To Freeze Redux
- •A Touch of Text
- •Shady Areas and No-Worry Lines
- •Templates for Success
- •Point Me The Way
- •Your Data Another Sorta Way
- •Give A Graph A Fair Break
- •Options, Options, Options
- •Quick Review?
- •Chapter 7. Look At Your Data
- •Sorting Things Out
- •Describing Series—Just The Facts Please
- •Describing Series—Picturing the Distribution
- •Tests On Series
- •Describing Groups—Just the Facts—Putting It Together
- •Chapter 8. Forecasting
- •Just Push the Forecast Button
- •Theory of Forecasting
- •Dynamic Versus Static Forecasting
- •Sample Forecast Samples
- •Facing the Unknown
- •Forecast Evaluation
- •Forecasting Beneath the Surface
- •Quick Review—Forecasting
- •Chapter 9. Page After Page After Page
- •Pages Are Easy To Reach
- •Creating New Pages
- •Renaming, Deleting, and Saving Pages
- •Multi-Page Workfiles—The Most Basic Motivation
- •Multiple Frequencies—Multiple Pages
- •Links—The Live Connection
- •Unlinking
- •Have A Match?
- •Matching When The Identifiers Are Really Different
- •Contracted Data
- •Expanded Data
- •Having Contractions
- •Two Hints and A GotchYa
- •Quick Review
- •Chapter 10. Prelude to Panel and Pool
- •Pooled or Paneled Population
- •Nuances
- •So What Are the Benefits of Using Pools and Panels?
- •Quick (P)review
- •Chapter 11. Panel—What’s My Line?
- •What’s So Nifty About Panel Data?
- •Setting Up Panel Data
- •Panel Estimation
- •Pretty Panel Pictures
- •More Panel Estimation Techniques
- •One Dimensional Two-Dimensional Panels
- •Fixed Effects With and Without the Social Contrivance of Panel Structure
- •Quick Review—Panel
- •Chapter 12. Everyone Into the Pool
- •Getting Your Feet Wet
- •Playing in the Pool—Data
- •Getting Out of the Pool
- •More Pool Estimation
- •Getting Data In and Out of the Pool
- •Quick Review—Pools
- •Chapter 13. Serial Correlation—Friend or Foe?
- •Visual Checks
- •Testing for Serial Correlation
- •More General Patterns of Serial Correlation
- •Correcting for Serial Correlation
- •Forecasting
- •ARMA and ARIMA Models
- •Quick Review
- •Chapter 14. A Taste of Advanced Estimation
- •Weighted Least Squares
- •Heteroskedasticity
- •Nonlinear Least Squares
- •Generalized Method of Moments
- •Limited Dependent Variables
- •ARCH, etc.
- •Maximum Likelihood—Rolling Your Own
- •System Estimation
- •Vector Autoregressions—VAR
- •Quick Review?
- •Chapter 15. Super Models
- •Your First Homework—Bam, Taken Up A Notch!
- •Looking At Model Solutions
- •More Model Information
- •Your Second Homework
- •Simulating VARs
- •Rich Super Models
- •Quick Review
- •Chapter 16. Get With the Program
- •I Want To Do It Over and Over Again
- •You Want To Have An Argument
- •Program Variables
- •Loopy
- •Other Program Controls
- •A Rolling Example
- •Quick Review
- •Appendix: Sample Programs
- •Chapter 17. Odds and Ends
- •How Much Data Can EViews Handle?
- •How Long Does It Take To Compute An Estimate?
- •Freeze!
- •A Comment On Tables
- •Saving Tables and Almost Tables
- •Saving Graphs and Almost Graphs
- •Unsubtle Redirection
- •Objects and Commands
- •Workfile Backups
- •Updates—A Small Thing
- •Updates—A Big Thing
- •Ready To Take A Break?
- •Help!
- •Odd Ending
- •Chapter 18. Optional Ending
- •Required Options
- •Option-al Recommendations
- •More Detailed Options
- •Window Behavior
- •Font Options
- •Frequency Conversion
- •Alpha Truncation
- •Spreadsheet Defaults
- •Workfile Storage Defaults
- •Estimation Defaults
- •File Locations
- •Graphics Defaults
- •Quick Review
- •Index
- •Symbols

164—Chapter 5. Picture This!
The second item worth knowing about this dialog is that it uses @series as a special keyword. You can see an example in the dialog on page 162. When a graph has multiple series, EViews will treat data coming from one series versus another analogously with data within a series coming from one category versus another. In other words, the list of series can be treated like an artificial categorical variable for arranging graph layouts. (Just as we saw in Multiple Series as Factors.) The keyword @series is used to identify the list of series in the graph.
Togetherness of the Second Sort
At this point you know lots of ways to create graphs. Graphs, however created, are easily combined into a new, single graph.
Hint: Remember that a graph view of a series or group isn’t a graph object. A graph object, distinguished by the icon, is most commonly created by freezing a graph view.

Togetherness of the Second Sort—165
Select the graph objects you want to combine just as you would select series for a group, or use the show command, as in:
show tm3_ty01 tm3_ty20
where tm3_ty01 tm3_ty20 are names of graphs stored in the workfile, to open a new graph object.
Using the mouse, you can re-arrange the position of the subgraphs within the overall graph window. This allows you to produce some very interesting effects. For example, if you have two graphs with identical axes, you can superimpose one on the other.

166—Chapter 5. Picture This!
You’ll note that this last picture has been messed with some: colors, titles, and axes have changed. Such messing around techniques are covered in Chapter 6, “Intimacy With Graphic Objects.”
Quick Review and Look Ahead
EViews makes visually pleasing graphs quite easily. All you need do is open a series or a group and choose from the wide variety of graph types available. A variety of summary statistics can be graphed as easily as raw data. You can also have EViews make individual plots for data falling into different categories. Customizing graphs by adding text or changing colors is similarly easy.
If your visual needs have been satisfied, then this chapter is all you need to know about graphics. If more control is your style, then the next chapter will make you very happy.