
- •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

370—Chapter 15. Super Models
Looking At Model Solutions
Since EViews placed the results CONS_0 and Y_0 in the workfile, we can examine our solution using any of the usual tools for looking at series. In addition, the model object has tools convenient for this task in the Proc menu. Choose Proc/Make Graph… to bring up the Make Graph dialog and click .
The graph window that opens shows the time path for all the variables in our homework model.

Looking At Model Solutions—371
If you prefer to see all the series on a single graph, choose the radio button
Group by Scenario/Actuals/Deviations/etc. in the
Make Graph dialog. To make the graph prettier, we’ve reassigned Y and CONS to the right axis. (See Left and Right Axes in Group Line Graphs in Chapter 5, “Picture This!”)
Comparing Actual Data to the Model Solution
Return again to the Make Graph dialog. This time choose Listed variables, enter Y in the text field, and check the checkbox Compare and choose Actuals on the dropdown menu. Click
Group by Model Variable so that all the GDP data will appear on the same graph. If you’d like, also check
% Deviation: Active from Compare.

372—Chapter 15. Super Models
You can see that we now do a much better job of matching the real data.
More Model Information
Before we see what else we can do with a model, let’s explore a bit to see what else is stored inside.
Model Variables
Click the button to switch the model to the variables view. We see that CONS and Y are marked with an
icon, while the remaining variables are marked with an
icon to distinguish the former as endogenous from the latter, which are exogenous.
Teminology hint: Exogenous variables are determined outside the model and their values are not affected by the model’s solution. Endogenous variables are determined by the solution of the model. Think of exogenous variables as model inputs and endogenous variables as model outputs.
Model Equations
Equations View
Return to the equations view. The left column shows the beginning of the equation. The column on the right shows how EViews is planning on solving the model: income is a function of consumption, investment,
and government spending; consumption is a function of income and consumption (well,

More Model Information—373
lagged consumption actually).
Hint: How come the equation shows only CONS+I+G? What happened to exports and imports—not to mention that discrepancy thing? In the equation view, EViews displays only enough of each equation so that you can remember which equation’s which. To see the full equation, switch to the text view or look at the equation’s Properties.
Equation Properties
Double-click in the view on to bring up the Properties dialog. As you can see, the model has pulled in the estimated coefficients as well as the estimated standard error of the regression. We’re looking at a live link. If we re-estimated the consumption equation, the new estimates would automatically replace the current estimates in the model.
Hint: The estimated standard error is used when you ask EViews to execute a stochastic simulation, a feature we won’t explore, referring you instead to the User’s Guide.