
- •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

Chapter 15. Super Models
Most of EViews centers on using data to estimate something we’d like to know, often the parameters of an equation. The model object turns the process around, taking a model made up of linear or nonlinear, (possibly) simultaneous equations and finding their solution. We begin the chapter with the solution of a simple, familiar model. Next, we discuss some of the ways that models can be used to explore different scenarios. Of course, we’ll link the models to the equations you’ve already learned to estimate.
Your First Homework—Bam, Taken Up A Notch!
Odds are that your very first homework assignment in your very first introductory macroeconomics class presented a model something like this:
Y ≡ C + I + G
C = C + mpc × Y
Your assignment was to solve for the variables Y (GDP), and C (consumption), given information about I (investment) and G (government spending).
Cultural Imperialism Apologia: If you took the course outside the United States, the national income identity probably included net exports as well.
But that consumption function is econometrically pretty unsophisticated. (We’ll carefully avoid any questions about the sophistication of a model consisting solely of a national income identity and a consumption function.) A more modern consumption function might look like this:
lnCt = a + l lnCt – 1 + b lnYt

366—Chapter 15. Super Models
The page Real in the workfile “Keynes.wf1” contains annual national income accounting data for the U.S. from 1959 through 2000. It also includes an estimated equation, , for this more modern consumption function.
Creating A Model
This model isn’t so easy to solve as is the Keynesian cross. The consumption function introduces both nonlinear and dynamic
elements. Fortunately, this sort of number-crunching is a breeze with EViews’ modeling facility.
To get started with making an EViews model, use Object/New Object… to generate a model named KEYNESCROSS.
The new model object opens to an empty window, as shown. We’re going to type the first equation in manually, so hit the button.
Type in the national income accounting identity. When you’re done, the window should look something like the picture shown to the right.
Hint: Since C is a reserved name in EViews, we’ve substituted CONS for C.
Hint: In this example, we typed in one equation and copied another from an estimated equation in the workfile. You’re free to mix and match, although in real work most equations are estimated. In addition to linking in an equation object, you can also link in SYS and VAR objects.

Your First Homework—Bam, Taken Up A Notch!—367
Let’s find out whether we and EViews have had a meeting of the minds on how to interpret the model. Click to switch to the equations view, which tells us what EViews is thinking. (You may get a warning mes-
sage about recompiling the model. It can be ignored.) One line appears for each equation. (So far, there is only one equation.)
Double-click on an equation for more information; for example, the Properties of the first equation are shown to the right. Since this is the national income accounting identity, it should really be marked as an identity. Click the Identity radio button on the lower right of the dialog, and then to return to the equations view.
To complete the model, we
need to bring in the consumption function. The estimated consumption function is stored in the workfile as . Select this equation in the workfile window, and then copy-and-paste or drag-and-drop it into the model window. EViews
checks to be sure that you really want to link the estimated consumption function into the model. Since you do, choose .
The model is now complete.

368—Chapter 15. Super Models
Solving the Model
So what’s the homework answer? Click . The Model Solution dialog appears with lots and lots of options. We’re not doing anything fancy, so just hit
. EViews will find a numerical solution for the simultaneous equation model we’ve specified.
Two windows display new information. The model solution messages window (below right) provides information about the solution. The workfile window (below left) has
acquired two series ending with the suffix “__0.”
The model window gives details of the solution technique used. Complicated models can be hard for even a computer to solve, but this model is not complex, so the details aren’t very interesting. Note at the bottom of the window that EViews solved the model essentially instantly.
Wow hint: The model is nonlinear. There is no closed form solution. This doesn’t bother EViews in the slightest! (Admittedly, some models are harder for a computer to solve.)
The model solver created new series containing the solution values. To distinguish these series from our original data, EViews adds “_0” to the end of the name for each solved

Your First Homework—Bam, Taken Up A Notch!—369
series. That’s the source of the series CONS_0 and Y_0 that now appears in the workfile window.
Hint: EViews calls a series with the added suffix an alias.
Open a group in the usual way to look at the solutions. The solution for GDP is close to the real data, although it isn’t perfect.
Making A Better Model
If we’d like the solution to be closer to the real data, we need a better model. In the example at hand, we know that we’ve left exports and imports out of the model. Switch to the text view and
edit the identity so it looks as shown here.
Hint: Discrepancy? What discrepancy? Well for our data, consumption, investment, government spending, and net exports don’t quite add up to GDP.
Welcome to the real world.
Click again.