- •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
Appendix B. Wildcards
EViews supports the use of wildcard characters in a variety of situations where you need to enter a list of objects or a list of series. For example, you can use wildcards to:
•fetch, store, copy, rename or delete a list of objects
•specify a group object
•query a database by name or filter the workfile display
The following discussion describes some of the issues involved in the use of wildcard characters and expressions.
Wildcard Expressions
There are two wildcard characters: “*” and “?”. The wildcard character “*” matches zero or more characters in a name, and the wildcard “?” matches any single character in a name.
For example, you can use the wildcard expression “GD*” to refer to all objects whose names begin with the characters “GD”. The series GD, GDP, GD_F will be included in this list GGD, GPD will not. If you use the expression GD?, EViews will interpret this as a list of all objects with three character names beginning with the string “GD”: GDP and GD2 will be included, but GD, GD_2 will not.
You can instruct EViews to match a fixed number of characters by using as many “?” wildcard characters as necessary. For example, EViews will interpret “??GDP” as matching all objects with names that begin with any two characters followed by the string “GDP”. USGDP and F_GDP will be included but GDP, GGDP, GDPUS will not.
You can also mix the different wildcard characters in an expression. For example, you can use the expression “*GDP?” to refer to any object that ends with the string “GDP” and an arbitrary character. Both GDP_1, USGDP_F will be included.
Using Wildcard Expressions
Wildcard expressions may be used in filtering the workfile display (see “Filtering the Workfile Directory Display” on page 59), in selected EViews commands, and in creating a group object.
The following commands support the use of wildcards: show, store, fetch, copy, rename and delete.
946—Appendix B. Wildcards
To create a group using wildcards, simply select Object/New Object.../Group, and enter the expression, EViews will first expand the expression, and then attempt to create a group using the corresponding list of series. For example, entering the list,
y x*
will create a group comprised of Y and all series beginning with the letter X. Alternatively, you can enter the command:
group g1 x* y?? c
defines a group G1, consisting of all of the series matching “X*”, and all series beginning with the letter “Y” followed by two arbitrary characters.
When making a group, EViews will only select series objects which match the given name pattern and will place these objects in the group.
Once created, these groups may be used anywhere that EViews takes a group as input. For example, if you have a series of dummy variables, DUM1, DUM2, DUM3, …, DUM9, that you wish to enter in a regression, you can create a group containing the dummy variables, and then enter the group in the regression:
group gdum dum?
equation eq1.ls y x z gdum
will run the appropriate regression. Note that we are assuming that the dummy variables are the only series objects which match the wildcard expression DUM?.
Source and Destination Patterns
When wildcards are used during copy and rename operations, a pattern must be provided for both the source and the destination. The destination pattern must always conform to the source pattern in that the number and order of wildcard characters must be exactly the same between the two. For example, the patterns,
Source Pattern |
Destination Pattern |
|
|
x* |
y* |
|
|
*c |
b* |
|
|
x*12? |
yz*f?abc |
|
|
which conform to each other, while these patterns do not:
Source Pattern |
Destination Pattern |
|
|
a* |
b |
|
|
Resolving Ambiguities—947
*x |
?y |
|
|
x*y* |
*x*y* |
|
|
When using wildcards, the new destination name is formed by replacing each wildcard in the destination pattern by the characters from the source name that matched the corresponding wildcard in the source pattern. This allows you to both add and remove characters from the source name during the copy or rename process. Some examples should make this clear:
Source Pattern |
Destination Pattern |
Source Name |
Destination Name |
|
|
|
|
*_base |
*_jan |
x_base |
x_jan |
|
|
|
|
us_* |
* |
us_gdp |
gdp |
|
|
|
|
x? |
x?f |
x1 |
x1f |
|
|
|
|
*_* |
**f |
us_gdp |
usgdpf |
|
|
|
|
??*f |
??_* |
usgdpf |
us_gdp |
|
|
|
|
Note, as shown in the second example, that a simple asterisk for the destination pattern will result in characters being removed from the source name when forming the destination name. To copy objects between containers preserving the existing name, either repeat the source pattern as the destination pattern,
copy x* db1::x*
or omit the destination pattern entirely,
copy x* db1::
Resolving Ambiguities
Note that an ambiguity can arise with wildcard characters since both “*” and “?” have multiple uses. The “*” character may be interpreted as either a multiplication operator or a wildcard character. The “?” character serves as both the single character wildcard and the pool cross section identifier.
Wildcard versus Multiplication
There is a potential for ambiguity in the use of the wildcard character “*”.
Suppose you have a workfile with the series X, X2, Y, XYA, XY2. There are then two interpretations of the wildcard expression “X*2”. The expression may be interpreted as an autoseries representing X multiplied by 2. Alternatively, the expression may be used as a wildcard expression, referring to the series X2 and XY2.
948—Appendix B. Wildcards
Note that there is only an ambiguity when the character is used in the middle of an expression, not when the wildcard character “*” is used at the beginning or end of an expression. EViews uses the following rules to determine the interpretation of ambiguous expressions:
•EViews first tests to see whether the expression represents a valid series expression. If so, the expression is treated as an auto-series. If it is not a valid series expression, then EViews will treat the “*” as a wildcard character. For example,
y*x
2*x
are interpreted as auto-series, while,
*x
x*a
are interpreted as wildcard expressions.
•You can force EViews to treat “*” as a wildcard by preceding the character with another “*”. Thus, expressions containing “**” will always be treated as wildcard expressions. For example, the expression:
x**2
unambiguously refers to all objects with names beginning with “X” and ending with “2”. Note that the use of “**” does not conflict with the EViews exponentiation operator “^”.
•You can instruct EViews to treat “*” as a series expression operator by enclosing the expression (or any subexpression) in parentheses. For example:
(y*x)
always refers to X times Y.
We strongly encourage you to resolve the ambiguity by using parentheses to denote series expressions, and double asterisks to denote wildcards (in the middle of expressions), whenever you create a group. This is especially true when group creation occurs in a program; otherwise the behavior of the program will be difficult to predict since it will change as the names of other objects in the workfile change.
Wildcard versus Pool Identifier
The “?” wildcard character is used both to match any single character in a pattern and as a place-holder for the cross-section identifier in pool objects.
EViews resolves this ambiguity by not allowing the wildcard interpretation of “?” in any expression involving a pool object or entered into a pool dialog. “?” is used exclusively as
Wildcard versus Pool Identifier—949
a cross-section identifier. For example, suppose that you have the pool object POOL1. Then, the expression,
pool1.est y? x? c
is a regression of the pool variable Y? on the pool variable X?, and,
pool1.delete x?
deletes all of the series in the pool series X?. There is no ambiguity in the interpretation of these expressions since they both involve POOL1.
Similarly, when used apart from a pool object, the “?” is interpreted as a wildcard character. Thus,
delete x?
unambiguously deletes all of the series matching “X?”.
950—Appendix B. Wildcards