- •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
114—Chapter 5. Basic Data Handling
Exporting Data
EViews provides you with a number of methods for getting data from EViews into other applications.
Copying and Pasting
You can click and drag in a spreadsheet view or table of statistical results to highlight the cells you want to copy. Then click Edit/Copy… in the main menu to put the data into the clipboard. You will see a dialog box asking whether to copy the numbers with the precision showing on your screen (formatted copy) or to copy the numbers at full precision (unformatted copy).
As a shortcut, you can highlight entire rows or columns of cells by clicking on the gray border that surrounds the spreadsheet. Dragging across the border selects multiple rows or columns. To copy several adjacent series from the spreadsheet, drag across their names in the top border. All of their data will be highlighted. Then click Edit/Copy… to put the data into the clipboard.
Once the data are on the clipboard, switch to the target application, and select Edit/Paste.
Exporting to a Spreadsheet or Text File
First, click on Proc/Export/Write Text-Lotus-Excel… from the workfile toolbar or main menu, then enter the name and type of the output file in the SaveAs dialog. As you fill out the SaveAs dialog, keep in mind the following behavior:
•If you enter a file name with an extension, EViews will use the file extension to identify the file type. Files with common spreadsheet extensions (“.XLS”, “.WK3”, “.WK1”, and “.WKS”) will be saved to the appropriate spreadsheet type. All others will be saved as ASCII files.
•If you do not enter an extension, EViews will use the file type selected in the combobox to determine the output type. Spreadsheet files will have the appropriate extensions appended to the name. ASCII files will be saved using the name provided in the dialog, without an extension. EViews will not append extensions to ASCII files unless you explicitly include one in the file name.
•Note that EViews cannot, at present, write into an existing file. The file that you select will, if necessary, be replaced.
Once you have specified the output file, click OK to open the export dialog.
Tip: if you highlight the series you wish to export before beginning the export procedure, the series names will be used to fill out the export dialog.
Frequency Conversion—115
Spreadsheet Export
The dialogs for spreadsheet export are virtually identical to the dialogs for spreadsheet import. You should determine the orientation of your data, the series to export, and the sample of observations to be written.
Additionally, EViews provides you with checkboxes for determining whether to include the series names and/or the series dates in the spreadsheet. If you choose to write one or both to the spreadsheet, make certain that the starting cell for your data leaves the necessary room along the borders for the information. If the necessary room is not available, EViews will ignore the option—for example, if you choose to write your data beginning in cell A1, EViews will not write the names or dates.
ASCII Export
The ASCII export dialog is quite similar to the spreadsheet export dialog, but it contains a few additional options:
•You can change the text string to be used for writing missing values. Simply enter the text string in the edit field.
•EViews provides you with the option of separating data values with a tab, a space, or a comma. Click on the desired radio button.
We caution that if you attempt to write your data by series, EViews will write all of the observations for a series on a single line. If you have a reasonably long series of observations, these data may overflow the line-length of other programs.
Matrix Object Export
Exporting data from a matrix object simply reverses the matrix import (“Matrix Object Import” on page 112). To write the contents of the matrix to a file, select Proc/Export Data (ASCII, .XLS, .WK?)… from the matrix toolbar and fill in the dialog as appropriate.
Frequency Conversion
Every series in EViews has an associated frequency. When a series is in a workfile, the series is stored at the frequency of the workfile. When a series is held in a database (Chapter 10, “EViews Databases”), it is stored at its own frequency. Since all series in the same workfile page must share a common frequency, moving a series from one workfile to another or from a database to a workfile page will cause the series being moved to be converted to the frequency of the workfile page into which it is being placed.
Performing Frequency Conversion
Frequency conversion is performed in EViews simply by copying or fetching a series with one frequency into a workfile of another frequency.
116—Chapter 5. Basic Data Handling
Copy-and-Paste
Suppose that you have two workfile page (or a source database and a destination workfile page), where the source contains quarterly data on the series YQ, and the destination workfile contains annual data. Note that you may copy between pages in the same workfile or between separate workfiles.
To convert YQ from a quarterly to annual frequency, you may copy-and-paste the series from the source quarterly workfile to the annual workfile. Click on the YQ series in the quarterly workfile, press the right-mouse button and select Copy, navigate to the annual workfile, then right mouse button and select Paste or Paste Special....
If you select Paste, EViews will copy YQ to the annual page, using the default frequency conversion settings present in YQ to perform the conversion.
If you select Paste Special..., EViews will display a dialog offering you the opportunity to override the default frequency conversion settings. Before describing this dialog (“Overriding Default Conversion Methods” on page 120), we provide a background on frequency conversion methods, and describe how default conversion methods are specified in EViews.
Using Commands
You may use either the copy or fetch command to move series between workfiles or between a database and a workfile. EViews will perform frequency conversion if the frequencies of the source and destination do not match.
See copy (p. 249) and fetch (p. 291) in the Command and Programming Reference for details.
Frequency Conversion Methods
There are three types of frequency conversion: high frequency to low frequency conversion, low frequency to high frequency conversion, and frequency conversion between a dated and undated workfile.
EViews provides you with the ability to specify methods for all types of conversion. In addition, there are settings that control the handling of missing values when performing the conversion.
High Frequency to Low Frequency
If a numeric series being imported has a higher frequency than the workfile, you may choose between a number of different conversion methods:
•Average observations
•Sum observations
Frequency Conversion—117
•First observation
•Last observation
•Maximum observation
•Minimum observation
•No down conversions
with the latter setting permitting you to disallow high to low conversions. In this case, EViews will generate an error if you attempt to convert from high to low frequency.
In addition, you may specify how EViews handles missing data when carrying out the calculations. You may elect to propagate NAs so that whenever a missing value appears in a calculation, the result for the corresponding period will be an NA. Alternatively, you may elect not to propagate NAs so that calculations will be performed ignoring the missing values (though if all values for a period are missing, the corresponding result will still be an NA).
Low Frequency to High Frequency
EViews also provides a number of different interpolation methods for dealing with the case where the series being brought into the workfile has a lower frequency than the workfile. Since observing a series at a lower frequency provides fundamentally less information than observing the same series at a higher frequency, it is generally not possible to recover the high frequency series from the low frequency data. Consequently, the results from EViews’ interpolation methods should be considered to be suggestive rather than providing the true values of the underlying series.
EViews supports the following interpolation methods:
•Constant: Constant with sum or average matched to the source data.
•Quadratic: Local quadratic with sum or average matched to the source data.
•Linear: Linear with last observation matched to the source data.
•Cubic: Cubic spline with last observation matched to the source data.
•No conversion: Do not allow up conversion.
Using an interpolation method which matches the average means that the average of the interpolated points for each period is equal to the source data point for that period. Similarly if the sum is matched, the interpolated points will sum to the source data point for the period, and if the last observation is matched, the last interpolated point will equal the source data point for the period.
118—Chapter 5. Basic Data Handling
For all methods, all relevant data from the low frequency series is used when forming the high frequency series, even if the destination observations are a subset of the observations available in the source.
The following describes the different methods in greater detail:
•Constant: match average, Constant: match sum—These two methods assign the same value to all observations in the high frequency series associated with a particular low frequency period. In one case, the value is chosen so that the average of the high frequency observation matches the low frequency observation (the value is simply repeated). In the other case, the value is chosen so that the sum of the high frequency observations matches the low frequency observation (the value is divided by the number of observations).
•Quadratic: match average, Quadratic: match sum—These two methods fit a local quadratic polynomial for each observation of the low frequency series, then use this polynomial to fill in all observations of the high frequency series associated with the period. The quadratic polynomial is formed by taking sets of three adjacent points from the source series and fitting a quadratic so that either the average or the sum of the high frequency points matches the low frequency data actually observed. For most points, one point before and one point after the period currently being interpolated are used to provide the three points. For end points, the two periods are both taken from the one side where data is available.
This method is a purely local method. The resulting interpolation curves are not constrained to be continuous at the boundaries between adjacent periods. Because of this, the method is better suited to situations where relatively few data points are being interpolated and the source data is fairly smooth.
•Linear: match last—This method assigns each value in the low frequency series to the last high frequency observation associated with the low frequency period, then places all intermediate points on straight lines connecting these points.
•Cubic: match last—This method assigns each value in the low frequency series to the last high frequency observation associated with the low frequency period, then places all intermediate points on a natural cubic spline connecting all the points.
A natural cubic spline is defined by the following properties:
1.Each segment of the curve is represented by a cubic polynomial.
2.Adjacent segments of the curve have the same level, first derivative and second derivative at the point where they meet.
3.The second derivative of the curve at the two global end points is equal to zero (this is the “natural” spline condition).
Frequency Conversion—119
Cubic spline interpolation is a global interpolation method so that changing any one point (or adding an additional point) to the source series will affect all points in the interpolated series.
Undated Conversion
If you fetch or copy a series to or from an undated or unstructured workfile into or from a dated workfile, the data will be copied sequentially, beginning at the starting observation number of the undated or unstructured series (generally the first observation).
Specifying Default Conversion Methods
When performing frequency conversion of one or more series, EViews uses the default settings in each series to perform the conversion. These settings may be specified in each series using the Freq Convert tab of the Properties dialog. To access the dialog, select View/Properties... from the series main menu, or click on the Properties button on the series toolbar.
If the series default setting is set to EViews default, the series will take its frequency conversion setting from the EViews global options (see “Dates & Frequency Conversion” on page 939 in Appendix A, “Global Options”). Here, the high to low conversion is set to Sum observations, overriding the global setting, while the low to high uses the EViews default global setting.
This two level default system allows you to set global default settings for frequency
conversion that apply to all newly created series, while allowing you to override the default settings for specific series.
As an example of controlling frequency conversion using default settings, suppose you have daily data consisting of HIGH, LOW, and CLOSE series for a particular stock, from which you would like to construct a monthly workfile. If you use the default frequency conversion methods, the monthly workfile will contain series which use the series defaults, which is not likely to be what you want. By setting the frequency conversion method of the HIGH series to Max observation, of the LOW series to Min observation, and of the CLOSE series to Last observation, you may use conversion to populate a monthly workfile with converted daily data that follow the desired behavior.