
- •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

Creating a Link—191
2 |
2002 |
335 |
|
|
|
2 |
2003 |
365 |
|
|
|
Bear in mind that the first two steps, the averaging across firms to obtain a quarterly frequency series, and the frequency conversion to obtain an annual frequency series, are all performed automatically by the link, and are invisible to the user.
The results of frequency conversion linking from the quarterly panel to the annual panel differ significantly from the results obtained by general panel match merging using dates processing of matches. If we had performed the latter by creating a standard link by match merge with sum, we would have obtained:
Firm |
Year |
Revenue3 |
|
|
|
1 |
2002 |
670 |
|
|
|
1 |
2003 |
730 |
|
|
|
2 |
2002 |
670 |
|
|
|
2 |
2003 |
730 |
|
|
|
In creating a link that matches dates between the two panel workfile pages, we have a many-to-many match merge. In this case, the initial contraction involves summing over both quarters and firms to obtain annual values for 2002 (670) and 2003 (730). The second step, match merges these contracted values into the annual panel using a one-to-many match merge.
See “Panel links with date matching” on page 185 for related discussion.
Creating a Link
Links may be created interactively either by copying-and-pasting a series from the source to the destination page, or by issuing a link declaration in the destination page.
Creating a link using copy-and-paste
To define a link using copy-and-paste, first select one or more source series in the source workfile page, and either click on the right mouse button and select Copy, or select Edit/ Copy from the main EViews menu. Next, switch to the destination page by clicking on the appropriate tab, and either click on the right mouse button and select Paste Special..., or select Edit/Paste Special... from the main menu.
General match merge links
If neither the source nor the destination series are dated pages, EViews will display a dialog prompting you to fill out the general match merge options. Here we have used Paste

192—Chapter 8. Series Links
Special... to copy-and-paste the series TAXRATE from the source page into a destination page.
Destination name
The field in the upper left-hand portion of the dialog should be used for specifying the name of the destination object. Here, we have the default wildcard value of “*” indicating that the series named TAXRATE in the source page will be used in the destination page. We may modify the name by typing an explicit name such as “NEWTAX”, or by entering an expression containing the wildcard character. For
example, if we wish to use the name “NEWTAXRATE” in the destination page, we may enter “NEW*” in the edit field.
The wildcard processing is particularly useful if you are copying multiple series into a new page since it facilitates batch renaming of series.
Destination type
Next, you will choose between pasting the series by value, or pasting the series as a link. If you paste by value, EViews will create an ordinary series in the destination page, and will fill it with the values from the link evaluation. If you paste your series as a link, EViews will create an actual link object containing the desired specification. As you might expect, there are significant differences between the two methods of copying your series.
In the first method, the link computations are performed immediately and the destination series values are assigned at the time the series is created. This behavior follows the traditional model of match merging and frequency conversion in which the operation is performed once to compute static values.
When you paste your series as a link, EViews defines a link object containing a specification of the match merge or frequency conversion. At creation, the link object is not evaluated and uses no memory. Then, whenever you access the values in the link series, EViews will determine whether the object needs evaluation and if so, will allocate memory and perform the link calculations.
With links, you gain the benefits of efficient memory use and dynamic updating of the values in the destination, at the cost of some speed since the link calculations may be performed more than once. Along these lines, it is worth pointing out that links may be

Creating a Link—193
converted into ordinary series at any time. Once a series is created, however, it may not be converted back into a link.
Match merge options
Whether you elect to create a new series with fixed values or to create a new link series, you must specify link options.
Match ID information
First, you must specify the information that EViews will use to identify matches between observations in the two pages.
In the Source ID and Destination ID edit fields, you will enter the names of one or more source ID series and one or more destination ID series. The number and order of the names in the two fields should match. Thus, if you wish to match both CXID1 and PERIOD1 in the source page to CXID2 and PERIOD2 in the second page, you should enter the sets of names in parallel. Here, we choose to match observations using the values of the STATE1 series in the source page and the values of the STATE2 series in the destination page.
Next, there is a checkbox labeled Treat NA as ID category for whether to use observations which have NA values in the source and destination ID values. By default, observations are ignored if there are NAs in the ID series; by selecting this option, you instruct EViews to match observations with NA ID values from the source page to observations with NA ID values in the destination page.
Link calculation settings
The remaining options are used when computing the link values.
First, you should specify a source series contraction method. As described in “Linking by general match merging” on page 178, the first step in every match merge is to perform a contraction to ensure uniqueness of the source values. Since contraction is always performed, you should pay attention to your contraction method even when the source IDs are unique, since some settings will not yield the original source data.
There is an extensive list of contractions from which you may choose. For links involving numeric series you may choose to employ obvious methods such as the Mean (default) or the Median of the observations, or less obvious summary statistics such as the Variance, Kurtosis,
Quantile, Number of obs, or Number of NAs.
For links involving alpha series, you must select from a subset of the numeric contractions: Unique values (default) , No contractions allowed, First, Last, Maximum, Minimum, Number of obs, Number of NAs.

194—Chapter 8. Series Links
Most of these options are self-explanatory, though a few comments about the choice of method may prove useful.
First, there are two options at the bottom of the list which deserve additional explanation. The last choice, No contractions allowed, may be used to ensure that contractions are never performed prior in the first step of a link match merge. The option is designed for cases where you believe that your source ID values are unique, and wish the link to generate an error if they are not.
The Unique values option provides a less strict version of the No contractions allowed setting, allowing for non-unique source ID values so long as any observations with matching IDs share the same source series value. In this case, the contraction will simply identify the unique source value associated with each unique source ID value. If there are observations with a single ID that have more than one source series value, the link will generate an error.
To see the difference between the two settings, note that contracting the following SOURCE and ID series
ID |
Source |
|
|
1 |
80 |
|
|
1 |
80 |
|
|
1 |
80 |
|
|
2 |
100 |
|
|
2 |
100 |
|
|
generates an error with the Unique values setting, but not with the No contractions allowed setting. Alternatively, the SOURCE and ID series
ID |
Source |
|
|
1 |
80 |
|
|
1 |
80 |
|
|
1 |
50 |
|
|
2 |
100 |
|
|
2 |
100 |
|
|
generate errors with both contractions.
Second, you should note that if you select First or Last, EViews will contract the source series by selecting the first or last observation in each set of observations with repeated source IDs. First or Last is defined here as depending on the order in which the observa-

Creating a Link—195
tions appear in the original source workfile. Thus, selecting First means that the contracted value for each source ID value will be taken from the first observation in the workfile with that ID value.
Lastly, you should bear in mind that unless you select No contractions allowed or Unique values, EViews will perform a first stage contraction of the data using the specified settings. In cases where the source ID values are not unique, this contraction is a necessary step; in cases where the source ID values are unique, the contraction is not necessary for the resulting one-to-one or one-to-many match merge, but is performed so that EViews can support more complicated many-to-many merge operations.
For most of the choices, performing a contraction on the unique source data has no practical effect on the outcome of a one-to-one or one-to-many match merge. For example, a choice of any of the data preserving options: Mean, Median, Maximum, Minimum, Sum,
First, Last, Unique values, or No contractions allowed will create a link that performs the standard one-to-one or one-to-many match merge of the values of the original source series into the destination page.
On the other hand, selecting a contraction method that alters the source values will create a link that performs a match merge of the summary values into the destination page. Thus, selecting Sum of Squares, Variance, Standard Deviation, Skewness, Kurtosis, Quantile,
Number of obs, or Number of NAs, will generate link values that differ from those obtained in a traditional one-to-one or one-to-many match merge.
It is worth emphasizing that the default contraction setting, Mean, preserves values for data with unique source IDs. Thus, unless you specifically set the contraction method to a non-preserving method, a one-to-one or one-to-many match merge will link the original values into the destination page. You may also ensure that EViews performs the traditional one-to-one or one-to-many match merge by selecting any of the other value preserving transformation methods, or even better by selecting No contractions allowed or Unique values to validate the IDs.
Finally, in the Source sample edit field, you should enter a description of the source sample to be used when constructing link values. By default, the full sample keyword “@ALL” is entered in the field so that EViews will use all of the observations in the source page.

196—Chapter 8. Series Links
One important application involving sample settings is to restrict the observations over which the contraction is performed prior to performing the match merge. Suppose, for example, that we have a workfile with observations on individuals with state of residence. Then we could construct two links from the individual page to a state page, one of which computes the mean INCOME for males in each state, and another which computes the mean INCOME for females.
Date match merge links
Dates may be used in matching in two ways: exact matching or date matching (see “Linking by date match merging” on page 183 for details).
Suppose we have a workfile containing the quarterly data on PROFITS described earlier. The quarterly PROFITS data is contained in a regular frequency quarterly workfile page. Also contained in the page is a date series DT generated by taking the first instance in each quarter (“series dt=@date”). We show here DT formatted to show the day-month-year, alongside the PROFIT series.
Contained in a separate, unstructured page are advertising data ADVERT, and another series DT showing the corresponding irregular dates.
If we attempt to match merge these data using the DT date series as identifiers, EViews will use the first method, exact matching, to identify common observations. Thus, if we try to link the PROFIT data into the advertising page using the

Creating a Link—197
DT series as the identifiers, we will find that there are no observations in the quarterly source page that match observations in the irregular daily destination page. The resulting link values will all be NAs.
When one or both of the pages follow a regular frequency, we may instruct EViews to employ date matching. We may do so by using the special ID keyword “@DATE” as an ID in the regular frequency page ID to indicate that we wish to use date matching with the built-in date identifiers given by the structure of the page. In this case, we will use “@DATE” as the ID for the regular frequency quarterly page, and match it against the values in the DT series in the destination page.
In this example, we use the Paste Special dialog to instruct EViews to copy the quarterly PROFIT series to a link named PROFIT1 in the destination page. We employ date matching to match the quarters in the source page to the values in the DT series in the destination page, rounding to the lowest common frequency.
We first compute a Mean contraction of the source data for all observations, then match merge the
contracted results into the destination. Note that since the match merge in this example is one-to-many, the Mean contraction method is irrelevant since it leaves the source data unchanged. If we wish to guarantee that the source IDs are unique, we may change the
Contraction method to No contractions allowed.
In the special case where you have two dated structured pages, you may construct the link using the “@DATE” keyword for both page identifiers. Here, where the advertising page is structured as an (irregular) daily dated page, we could replace DT in the destination index field with the keyword “@DATE”.
If “@DATE” is used as an ID in both pages, EViews will use the observation

198—Chapter 8. Series Links
date identifiers associated with the structure of each page, round them to the lowest common frequency, and then find matching observations.
Frequency conversion links
In the special case where we link numeric series between two regular frequency pages, we may copy-and-paste to define a link (or a by value copy of the source series) that employs frequency conversion (“Linking by date with frequency conversion” on page 187). In this setting, the Paste Special dialog offers you an additional choice between linking by general match merge, or linking by date using frequency conversion.
If you select General match merge criteria in the Merge by section of the dialog, the right side of the dialog with change to show the standard match merge version described in “General match merge links” on page 191.
Alternately, to define a frequency conversion link, click on the Date
(with frequency conversion) selection. The dialog will change to display the frequency conversion options for converting data both
from high to low, and low to high frequency.
By default, EViews will use the high to low and the low to high conversion methods specified in the original source series.
If you wish to change the high to low conversion methods, simply select the desired setting from the drop-down menu. In addition, if you select one of the non-default methods, choose whether to select the No conversion of partial periods checkbox. If this setting is selected, EViews will propagate NAs when performing the
frequency conversion so that the average of observations with an NA value will not drop the observation, and will instead generate an NA.
Note that the last conversion method, No down conversions, may be used to disallow down frequency conversion of the data. This setting allows you to ensure that when evaluated, the link involves same frequency (one-to-one) or low to high (one-to-many) frequency conversion, otherwise the link evaluation will generate an error.

Creating a Link—199
To set the low to high conversion method, select the desired method from the drop-down menu. Once again, the last frequency conversion method, No up conversions, allows you to inform EViews that you expect the link to work only for same frequency, or high-to-low frequency linking, and that the link evaluation
should generate an error if it encounters data requiring up conversion.
Creating a link by command
While the copy-and-paste interface is the easiest approach to specifying a link, we note that you may also create links using the LINK declaration statement and the LINKTO procedure.
You may, at the command line, enter the keyword “LINK” followed by the name of a new link object. EViews will create a new, incompletely specified, link object in the current (destination) workfile page. The destination page should be active when you enter the command.
You may modify a link specification, defining link IDs, as well as contraction and in some cases, expansion methods using the LINKTO proc.
Consider our earlier example where we link the TAXRATE data from the state page to the individual page. The following command creates a link object in the current workfile page:
link taxrate2
You may modify the TAXRATE2 link by providing a link definition using the LINKTO procedure. The “LINKTO” keyword should be followed by the name of the source series and the source and destination IDs, with the latter separated by “@SRC” and “@DEST” keywords. For example, if the link object TAXRATE2 exists in our individual page, the link proc:
taxrate2.linkto state::taxrate @src state1 @dest state2
instructs EViews to define the link TAXRATE2 so that it uses the TAXRATE series in the source page named “STATE” as the source series, and matches the source page STATE1 values to the current page STATE2 values.
In the special case where there is only one ID series in each page, we may, without introducing ambiguity, omit the “@SRC” and “@DEST” keywords. Here, we may shorten our link definition statement to:
taxrate2.linkto state::taxrate state1 state2
Lastly, we may combine these declaration and definition statements into one. The command

200—Chapter 8. Series Links
link taxrate2.linkto state::taxrate state1 state2
both creates a link object in the active workfile page and defines the source and link ID series.
In this one-to-many example where we link state data to individuals, we need not consider contraction methods as the default (mean) contraction method preserves the original data. If you wish to disallow contractions, or to limit them to cases where the values of the source data are unique, you may use contraction options as in:
link taxrate2.linkto(c=none) state::taxrate state1 state2
or
link taxrate2.linkto(c=unique) state::taxrate state1 state2
Conversely, linking the SALES data from the individual page to the state page yields a many-to-one conversion in which the contraction method is important. In this setting, we may optionally specify a contraction method so that when the state page is active, the statement
link sales2.linkto(c=sum) indiv::sales state2 state1
links the SALES data from the “INDIV” source page, matching the source page STATE2 values to the current page STATE1 values, and contracting observations using the sum transformation. If the contraction option is not provided, EViews will use the mean contraction default.
In the special case where you wish to link your data using date matching, you must use the special keyword “@DATE” as an ID series for the regular frequency page. For example, when linking from our quarterly to our advertising page, we may specify:
link profit1.linkto quarterly::profit @date dt
to tell EViews to link the quarterly page PROFIT data, matching the built-in identifier for the quarter with the date series DT in the destination advertising page.
As in the copy-and-paste interface, the presence of the special “@DATE” keyword tells EViews that you wish to perform date matching using the date structure of the corresponding regular frequency page. If “@DATE” is not specified as an ID, EViews will employ a general match merge using the specified identifiers.
When linking data between dated regular frequency workfile pages, the LINKTO proc will perform a frequency conversion link between the two pages unless ID series are explicitly provided, or a general match merge specific conversion method (such as variance or kurtosis) is specified. Thus, issuing the command
link profit2.linkto quarterly::profit