- •Table of Contents
- •What’s New in EViews 5.0
- •What’s New in 5.0
- •Compatibility Notes
- •EViews 5.1 Update Overview
- •Overview of EViews 5.1 New Features
- •Preface
- •Part I. EViews Fundamentals
- •Chapter 1. Introduction
- •What is EViews?
- •Installing and Running EViews
- •Windows Basics
- •The EViews Window
- •Closing EViews
- •Where to Go For Help
- •Chapter 2. A Demonstration
- •Getting Data into EViews
- •Examining the Data
- •Estimating a Regression Model
- •Specification and Hypothesis Tests
- •Modifying the Equation
- •Forecasting from an Estimated Equation
- •Additional Testing
- •Chapter 3. Workfile Basics
- •What is a Workfile?
- •Creating a Workfile
- •The Workfile Window
- •Saving a Workfile
- •Loading a Workfile
- •Multi-page Workfiles
- •Addendum: File Dialog Features
- •Chapter 4. Object Basics
- •What is an Object?
- •Basic Object Operations
- •The Object Window
- •Working with Objects
- •Chapter 5. Basic Data Handling
- •Data Objects
- •Samples
- •Sample Objects
- •Importing Data
- •Exporting Data
- •Frequency Conversion
- •Importing ASCII Text Files
- •Chapter 6. Working with Data
- •Numeric Expressions
- •Series
- •Auto-series
- •Groups
- •Scalars
- •Chapter 7. Working with Data (Advanced)
- •Auto-Updating Series
- •Alpha Series
- •Date Series
- •Value Maps
- •Chapter 8. Series Links
- •Basic Link Concepts
- •Creating a Link
- •Working with Links
- •Chapter 9. Advanced Workfiles
- •Structuring a Workfile
- •Resizing a Workfile
- •Appending to a Workfile
- •Contracting a Workfile
- •Copying from a Workfile
- •Reshaping a Workfile
- •Sorting a Workfile
- •Exporting from a Workfile
- •Chapter 10. EViews Databases
- •Database Overview
- •Database Basics
- •Working with Objects in Databases
- •Database Auto-Series
- •The Database Registry
- •Querying the Database
- •Object Aliases and Illegal Names
- •Maintaining the Database
- •Foreign Format Databases
- •Working with DRIPro Links
- •Part II. Basic Data Analysis
- •Chapter 11. Series
- •Series Views Overview
- •Spreadsheet and Graph Views
- •Descriptive Statistics
- •Tests for Descriptive Stats
- •Distribution Graphs
- •One-Way Tabulation
- •Correlogram
- •Unit Root Test
- •BDS Test
- •Properties
- •Label
- •Series Procs Overview
- •Generate by Equation
- •Resample
- •Seasonal Adjustment
- •Exponential Smoothing
- •Hodrick-Prescott Filter
- •Frequency (Band-Pass) Filter
- •Chapter 12. Groups
- •Group Views Overview
- •Group Members
- •Spreadsheet
- •Dated Data Table
- •Graphs
- •Multiple Graphs
- •Descriptive Statistics
- •Tests of Equality
- •N-Way Tabulation
- •Principal Components
- •Correlations, Covariances, and Correlograms
- •Cross Correlations and Correlograms
- •Cointegration Test
- •Unit Root Test
- •Granger Causality
- •Label
- •Group Procedures Overview
- •Chapter 13. Statistical Graphs from Series and Groups
- •Distribution Graphs of Series
- •Scatter Diagrams with Fit Lines
- •Boxplots
- •Chapter 14. Graphs, Tables, and Text Objects
- •Creating Graphs
- •Modifying Graphs
- •Multiple Graphs
- •Printing Graphs
- •Copying Graphs to the Clipboard
- •Saving Graphs to a File
- •Graph Commands
- •Creating Tables
- •Table Basics
- •Basic Table Customization
- •Customizing Table Cells
- •Copying Tables to the Clipboard
- •Saving Tables to a File
- •Table Commands
- •Text Objects
- •Part III. Basic Single Equation Analysis
- •Chapter 15. Basic Regression
- •Equation Objects
- •Specifying an Equation in EViews
- •Estimating an Equation in EViews
- •Equation Output
- •Working with Equations
- •Estimation Problems
- •Chapter 16. Additional Regression Methods
- •Special Equation Terms
- •Weighted Least Squares
- •Heteroskedasticity and Autocorrelation Consistent Covariances
- •Two-stage Least Squares
- •Nonlinear Least Squares
- •Generalized Method of Moments (GMM)
- •Chapter 17. Time Series Regression
- •Serial Correlation Theory
- •Testing for Serial Correlation
- •Estimating AR Models
- •ARIMA Theory
- •Estimating ARIMA Models
- •ARMA Equation Diagnostics
- •Nonstationary Time Series
- •Unit Root Tests
- •Panel Unit Root Tests
- •Chapter 18. Forecasting from an Equation
- •Forecasting from Equations in EViews
- •An Illustration
- •Forecast Basics
- •Forecasting with ARMA Errors
- •Forecasting from Equations with Expressions
- •Forecasting with Expression and PDL Specifications
- •Chapter 19. Specification and Diagnostic Tests
- •Background
- •Coefficient Tests
- •Residual Tests
- •Specification and Stability Tests
- •Applications
- •Part IV. Advanced Single Equation Analysis
- •Chapter 20. ARCH and GARCH Estimation
- •Basic ARCH Specifications
- •Estimating ARCH Models in EViews
- •Working with ARCH Models
- •Additional ARCH Models
- •Examples
- •Binary Dependent Variable Models
- •Estimating Binary Models in EViews
- •Procedures for Binary Equations
- •Ordered Dependent Variable Models
- •Estimating Ordered Models in EViews
- •Views of Ordered Equations
- •Procedures for Ordered Equations
- •Censored Regression Models
- •Estimating Censored Models in EViews
- •Procedures for Censored Equations
- •Truncated Regression Models
- •Procedures for Truncated Equations
- •Count Models
- •Views of Count Models
- •Procedures for Count Models
- •Demonstrations
- •Technical Notes
- •Chapter 22. The Log Likelihood (LogL) Object
- •Overview
- •Specification
- •Estimation
- •LogL Views
- •LogL Procs
- •Troubleshooting
- •Limitations
- •Examples
- •Part V. Multiple Equation Analysis
- •Chapter 23. System Estimation
- •Background
- •System Estimation Methods
- •How to Create and Specify a System
- •Working With Systems
- •Technical Discussion
- •Vector Autoregressions (VARs)
- •Estimating a VAR in EViews
- •VAR Estimation Output
- •Views and Procs of a VAR
- •Structural (Identified) VARs
- •Cointegration Test
- •Vector Error Correction (VEC) Models
- •A Note on Version Compatibility
- •Chapter 25. State Space Models and the Kalman Filter
- •Background
- •Specifying a State Space Model in EViews
- •Working with the State Space
- •Converting from Version 3 Sspace
- •Technical Discussion
- •Chapter 26. Models
- •Overview
- •An Example Model
- •Building a Model
- •Working with the Model Structure
- •Specifying Scenarios
- •Using Add Factors
- •Solving the Model
- •Working with the Model Data
- •Part VI. Panel and Pooled Data
- •Chapter 27. Pooled Time Series, Cross-Section Data
- •The Pool Workfile
- •The Pool Object
- •Pooled Data
- •Setting up a Pool Workfile
- •Working with Pooled Data
- •Pooled Estimation
- •Chapter 28. Working with Panel Data
- •Structuring a Panel Workfile
- •Panel Workfile Display
- •Panel Workfile Information
- •Working with Panel Data
- •Basic Panel Analysis
- •Chapter 29. Panel Estimation
- •Estimating a Panel Equation
- •Panel Estimation Examples
- •Panel Equation Testing
- •Estimation Background
- •Appendix A. Global Options
- •The Options Menu
- •Print Setup
- •Appendix B. Wildcards
- •Wildcard Expressions
- •Using Wildcard Expressions
- •Source and Destination Patterns
- •Resolving Ambiguities
- •Wildcard versus Pool Identifier
- •Appendix C. Estimation and Solution Options
- •Setting Estimation Options
- •Optimization Algorithms
- •Nonlinear Equation Solution Methods
- •Appendix D. Gradients and Derivatives
- •Gradients
- •Derivatives
- •Appendix E. Information Criteria
- •Definitions
- •Using Information Criteria as a Guide to Model Selection
- •References
- •Index
- •Symbols
- •.DB? files 266
- •.EDB file 262
- •.RTF file 437
- •.WF1 file 62
- •@obsnum
- •Panel
- •@unmaptxt 174
- •~, in backup file name 62, 939
- •Numerics
- •3sls (three-stage least squares) 697, 716
- •Abort key 21
- •ARIMA models 501
- •ASCII
- •file export 115
- •ASCII file
- •See also Unit root tests.
- •Auto-search
- •Auto-series
- •in groups 144
- •Auto-updating series
- •and databases 152
- •Backcast
- •Berndt-Hall-Hall-Hausman (BHHH). See Optimization algorithms.
- •Bias proportion 554
- •fitted index 634
- •Binning option
- •classifications 313, 382
- •Boxplots 409
- •By-group statistics 312, 886, 893
- •coef vector 444
- •Causality
- •Granger's test 389
- •scale factor 649
- •Census X11
- •Census X12 337
- •Chi-square
- •Cholesky factor
- •Classification table
- •Close
- •Coef (coefficient vector)
- •default 444
- •Coefficient
- •Comparison operators
- •Conditional standard deviation
- •graph 610
- •Confidence interval
- •Constant
- •Copy
- •data cut-and-paste 107
- •table to clipboard 437
- •Covariance matrix
- •HAC (Newey-West) 473
- •heteroskedasticity consistent of estimated coefficients 472
- •Create
- •Cross-equation
- •Tukey option 393
- •CUSUM
- •sum of recursive residuals test 589
- •sum of recursive squared residuals test 590
- •Data
- •Database
- •link options 303
- •using auto-updating series with 152
- •Dates
- •Default
- •database 24, 266
- •set directory 71
- •Dependent variable
- •Description
- •Descriptive statistics
- •by group 312
- •group 379
- •individual samples (group) 379
- •Display format
- •Display name
- •Distribution
- •Dummy variables
- •for regression 452
- •lagged dependent variable 495
- •Dynamic forecasting 556
- •Edit
- •See also Unit root tests.
- •Equation
- •create 443
- •store 458
- •Estimation
- •EViews
- •Excel file
- •Excel files
- •Expectation-prediction table
- •Expected dependent variable
- •double 352
- •Export data 114
- •Extreme value
- •binary model 624
- •Fetch
- •File
- •save table to 438
- •Files
- •Fitted index
- •Fitted values
- •Font options
- •Fonts
- •Forecast
- •evaluation 553
- •Foreign data
- •Formula
- •forecast 561
- •Freq
- •DRI database 303
- •F-test
- •for variance equality 321
- •Full information maximum likelihood 698
- •GARCH 601
- •ARCH-M model 603
- •variance factor 668
- •system 716
- •Goodness-of-fit
- •Gradients 963
- •Graph
- •remove elements 423
- •Groups
- •display format 94
- •Groupwise heteroskedasticity 380
- •Help
- •Heteroskedasticity and autocorrelation consistent covariance (HAC) 473
- •History
- •Holt-Winters
- •Hypothesis tests
- •F-test 321
- •Identification
- •Identity
- •Import
- •Import data
- •See also VAR.
- •Index
- •Insert
- •Instruments 474
- •Iteration
- •Iteration option 953
- •in nonlinear least squares 483
- •J-statistic 491
- •J-test 596
- •Kernel
- •bivariate fit 405
- •choice in HAC weighting 704, 718
- •Kernel function
- •Keyboard
- •Kwiatkowski, Phillips, Schmidt, and Shin test 525
- •Label 82
- •Last_update
- •Last_write
- •Latent variable
- •Lead
- •make covariance matrix 643
- •List
- •LM test
- •ARCH 582
- •for binary models 622
- •LOWESS. See also LOESS
- •in ARIMA models 501
- •Mean absolute error 553
- •Metafile
- •Micro TSP
- •recoding 137
- •Models
- •add factors 777, 802
- •solving 804
- •Mouse 18
- •Multicollinearity 460
- •Name
- •Newey-West
- •Nonlinear coefficient restriction
- •Wald test 575
- •weighted two stage 486
- •Normal distribution
- •Numbers
- •chi-square tests 383
- •Object 73
- •Open
- •Option setting
- •Option settings
- •Or operator 98, 133
- •Ordinary residual
- •Panel
- •irregular 214
- •unit root tests 530
- •Paste 83
- •PcGive data 293
- •Polynomial distributed lag
- •Pool
- •Pool (object)
- •PostScript
- •Prediction table
- •Principal components 385
- •Program
- •p-value 569
- •for coefficient t-statistic 450
- •Quiet mode 939
- •RATS data
- •Read 832
- •CUSUM 589
- •Regression
- •Relational operators
- •Remarks
- •database 287
- •Residuals
- •Resize
- •Results
- •RichText Format
- •Robust standard errors
- •Robustness iterations
- •for regression 451
- •with AR specification 500
- •workfile 95
- •Save
- •Seasonal
- •Seasonal graphs 310
- •Select
- •single item 20
- •Serial correlation
- •theory 493
- •Series
- •Smoothing
- •Solve
- •Source
- •Specification test
- •Spreadsheet
- •Standard error
- •Standard error
- •binary models 634
- •Start
- •Starting values
- •Summary statistics
- •for regression variables 451
- •System
- •Table 429
- •font 434
- •Tabulation
- •Template 424
- •Tests. See also Hypothesis tests, Specification test and Goodness of fit.
- •Text file
- •open as workfile 54
- •Type
- •field in database query 282
- •Units
- •Update
- •Valmap
- •find label for value 173
- •find numeric value for label 174
- •Value maps 163
- •estimating 749
- •View
- •Wald test 572
- •nonlinear restriction 575
- •Watson test 323
- •Weighting matrix
- •heteroskedasticity and autocorrelation consistent (HAC) 718
- •kernel options 718
- •White
- •Window
- •Workfile
- •storage defaults 940
- •Write 844
- •XY line
- •Yates' continuity correction 321
Working with the Model Data—817
To make this process easier, EViews provides a special procedure for solving a model which automatically searches for the unknown values. Simply create a series in the workfile which contains the values you would like the endogenous variable to achieve, then select
Proc/Solve Control for Target… from the menus. Enter the name of the exogenous variable you would like to modify in the Control Variable box, the name of the endogenous
variable which you are targeting in the Target Variable box, and the name of the workfile series which contains the target values in the Trajectory Variable box. Set the sample to the range for you would like to solve, then click on OK.
The procedure may take some time to complete, since it involves repeatedly solving the model to search for the desired solution. It is also possible for the procedure to fail if it cannot find a value of the exogenous variable for which the endogenous variable solves to the target value. If the procedure fails, you may like to try moving the trajectory series closer to values that you are sure the model can achieve.
Working with the Model Data
When working with a model, much of your time will be spent viewing and modifying the data associated with the model. Before solving the model, you will edit the paths of your exogenous variables or add factors during the forecast period. After solving the model, you will use graphs or tables of the endogenous variables to evaluate the results. Because there is a large amount of data associated with a model, you will also spend time simply managing the data.
Since all the data associated with a model is stored inside standard series in the workfile, you can use all of the usual tools in EViews to work with the data of your model. However, it is often more convenient to work directly from the model window.
Although there are some differences in details, working with the model data generally involves following the same basic steps. You will typically first use the variable view to select the set of variables you would like to work with, then use either the right mouse button menu or the model procedure menu to select the operation to perform.
Because there may be several series in the workfile associated with each variable in the model, you will then need to select the types of series with which you wish to work. The following types will generally be available:
818—Chapter 26. Models
•Actuals: the workfile series with the same name as the variable name. This will typically hold the historical data for the endogenous variables, and the historical data and baseline forecast for the exogenous variables.
•Active: the workfile series that is used when solving the active scenario. For endogenous variables, this will be the series with a name consisting of the variable name followed by the scenario extension. For exogenous variables, the actual series will be used unless it has been overridden. In this case, the exogenous variable will also be the workfile series formed by appending the scenario extension to the variable name.
•Alternate: the workfile series that is used when solving the alternate scenario. The rules are the same as for active.
In the following sections, we discuss how different operations can be performed on the model data from within the variable view.
Editing Data
The easiest way to make simple changes to the data associated with a model is to open a series or group spreadsheet window containing the data, then edit the data by hand.
To open a series window from within the model, simply select the variable using the mouse in the variable view, then use the right mouse button menu to choose Open selected series…, followed by Actuals, Active Scenario or Alternate Scenario. If you select several series before using the option, an unnamed group object will be created to hold all the series.
To edit the data, click the Edit+/- button to make sure the spreadsheet is in edit mode. You can either edit the data directly in levels or use the Units button to work with a transformed form of the data, such as the differences or percentage changes.
To create a group which allows you to edit more than one of the series associated with a variable at the same time, you can use the Make Group/Table procedure discussed below to create a dated data table, then switch the group to spreadsheet view to edit the data.
More complicated changes to the data may require using a genr command to calculate the series by specifying an expression. Click the Genr button from the series window toolbar to call up the dialog, then type in the expression to generate values for the series and set the workfile sample to the range of values you would like to modify.
Displaying Data
The EViews model object provides two main forms in which to display data: as a graph or as a table. Both of these can be generated easily from the model window.
Working with the Model Data—819
From the variable view, select the variables you wish to display, then use the right mouse button menu or the main menu to select Proc and then Make Group/Table or Make Graph.
The dialogs for the two procs are almost identical. Here we see the Make Graph dialog. We saw this dialog earlier in our macro model example.
The majority of fields in the dialog control which series
you would like the table or graph to contain. At the top left of the graph is the Model Variables box, which is used to select the set of variables to place in the graph. By default, the table or graph will contain the variables that are currently selected in the variable view.
You can expand this to include all model variables, or add or remove particular variables from the list of selected variables using the radio buttons and text box labeled From. You can also restrict the set of variables chosen according to variable type using the list box next to Select. By combining these fields, it is easy to select sets of variables such as all of the endogenous variables of the model, or all of the overridden variables.
Once the set of variables has been determined, it is necessary to map the variable names into the names of series in the workfile. This typically involves adding an extension to each name according to which scenario the data is from and the type of data contained in the series. The options affecting this are contained in the Graph series (if you are making a graph) or Series types (if you are making a group/table) box at the right of the dialog.
The Solution series box lets you choose which solution results you would like to examine when working with endogenous variables. You can choose from a variety of series generated during deterministic or stochastic simulations.
The series of checkboxes below determine which scenarios you would like to display in the graphs, as well as whether you would like to calculate deviations between various scenarios. You can choose to display the actual series, the series from the active scenario, or the series from an alternate scenario (labeled “Compare”). You can also display either the difference between the active and alternate scenario (labeled “Deviations: Active from Compare”), or the ratio between the active and alternate scenario in percentage terms (labeled “% Deviation: Active from Compare”).
820—Chapter 26. Models
The final field in the Graph series or Series types box is the Transform listbox. This lets you apply a transformation to the data similar to the Transform button in the series spreadsheet.
While the deviations and units options allow you to present a variety of transformations of your data, in some cases you may be interested in other transformations that are not directly available. Similarly, in a stochastic simulation, you may be interested in examining standard errors or confidence bounds on the transformed series, which will not be available when you apply transformations to the data after the simulation is complete. In either of these cases, it may be worth adding an identity to the model that generates the series you are interested in examining as part of the model solution.
For example, if your model contains a variable GDP, you may like to add a new equation to the model to calculate the percentage change of GDP:
pgdp = @pch(gdp)
After you have solved the model, you can use the variable PGDP to examine the percentage change in GDP, including examining the error bounds from a stochastic simulation. Note that the cost of adding such identities is relatively low, since EViews will place all such identities in a final recursive block which is evaluated only once after the main endogenous variables have already been solved.
The remaining option, at the bottom left of the dialog, lets you determine how the series will be grouped in the output. The options are slightly different for tables and graphs. For tables, you can choose to either place all series associated with the same model variable together, or to place each series of the same series type together. For graphs, you have the same two choices, and one additional choice, which is to place every series in its own graph.
In the graph dialog, you also have the option of setting a sample for the graph. This is often useful when you are plotting forecast results since it allows you to choose the amount of historical data to display in the graph prior to the forecast results. By default, the sample is set to the workfile sample.
When you have finished setting the options, simply click on OK to create the new table or graph. All of EViews usual editing features are available to modify the table or graph for final presentation.
Managing Data
When working with a model, you will often create many series in the workfile for each variable, each containing different types of results or the data from different scenarios. The model object provides a number of tools to help you manage these series, allowing you to perform copy, fetch, store and delete operations directly from within the model.
Working with the Model Data—821
Because the series names are related to the variable names in a consistent way, management tasks can often also be performed from outside the model by using the pattern matching features available in EViews commands (see Appendix B, “Wildcards”, on page 945).
The data management operations from within the model window proceed very similarly to the data display operations. First, select the variables you would like to work with from the variable view, then choose Copy, Store series…, Fetch series… or Delete series… from the right mouse button menu or the object procedures menu. A dialog will appear, similar to
the one used when making a table or graph.
In the same way as for the table and graph dialogs, the left side of the dialog is used to choose which of the model variables to work with, while the right side of the dialog is used to select one or more series associated with each variable. Most of the choices are exactly the same as for graphs and tables. One significant difference is that the checkboxes for active and comparison scenarios include exogenous variables only if they have been overridden in the scenario. Unlike when displaying or editing the data, if an exogenous variable has not been overridden, the actual series will not be included in its place. The only way to store, fetch or delete any actual series is to use the Actuals checkbox.
After clicking on OK, you will receive the usual prompts for the store, fetch and delete operations. You can proceed as usual.
822—Chapter 26. Models