- •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
374—Chapter 12. Groups
Here is an example of a table after freezing and editing:
|
|
|
1995 |
|
1995 |
Gross Domestic Product |
1792.3 |
1802.4 |
1825.3 |
1845.5 |
1816.4 |
One-period % change |
1.03 |
0.56 |
1.27 |
1.11 |
4.58 |
Price Level |
1.07 |
1.07 |
1.08 |
1.09 |
1.08 |
One-period % change |
0.80 |
0.49 |
0.52 |
0.55 |
2.55 |
|
|
|
1996 |
|
1996 |
Gross Domestic Product |
1866.9 |
1902.0 |
1919.1 |
1948.2 |
1909.0 |
One-period % change |
1.16 |
1.88 |
0.90 |
1.52 |
5.10 |
Price Level |
1.09 |
1.10 |
1.11 |
1.11 |
1.10 |
One-period % change |
0.72 |
0.41 |
0.64 |
0.46 |
2.27 |
Graphs
These views display the series in the group in various graphical forms. You can create graph objects by freezing these views. Chapter 14, “Graphs, Tables, and Text Objects”, on page 415 explains how to edit and modify graph objects in EViews.
The Graph views display all series in a single graph. To display each series in a separate graph, see “Multiple Graphs” on page 376.
Line, Area, Bar and Spike Graphs
Displays a line, area, bar or spike graph of the series in the group. Click anywhere in the background of the graph to modify the scaling options or line patterns.
Scatter
There are five variations on the scatter diagram view of a series.
Simple Scatter plots a scatter diagram with the first series on the horizontal axis and the remaining series on the vertical axis.
The remaining three options, Scatter with Regression, Scatter
with Nearest Neighbor Fit, and Scatter with Kernel Fit, plot fitted lines of the first series against the second on top of the scatter diagram. They differ in how the fitted lines are calculated. All three graph views are described in detail in “Scatter Diagrams with Fit Lines” beginning on page 400.
XY Pairs produces scatterplots of the first series in the group against the second, the third series against the fourth, and so forth.
Graphs—375
XY Line
These views plot XY line graphs of the series in the group. They are similar to the scatterplot graphs, but with successive observations connected by lines. One X against all Y’s will plot the first series in the group against all other series in the group. XY pairs will produce XY plots for successive pairs of series in the group.
Once you have constructed the XY plot, you may elect to display symbols only (similar to a scatterplot), or lines and symbols for each XY graph. Click anywhere in the background of the view and change
the line attributes for the selected line from Lines only to Symbol only or Line & Symbol.
See Chapter 14, “Graphs, Tables, and Text Objects”, on page 415 for additional details on graph customization.
Error Bar
The Error Bar view plots error bars using the first two or three series in the group. The first series is used for the “high” value and the second series is the “low” value. The high and low values are connected with a vertical line. The (optional) third series is plotted as a small circle.
Note that EViews does not check the values of your high and low data for consistency. If the high value is below the low value, EViews will draw “outside halflines” that do not connect.
This view is commonly used to display confidence intervals for a statistic.
High-Low (Open-Close)
This view plots the first two to four series in the group as a high-low (open-close) chart. As the name
suggests, this chart is commonly used by financial analysts to plot the daily high, low, opening and closing values of stock prices.
The first series in your group should be used to represent the “high” value and the second series should be the “low” value. The high and low values are connected with a vertical line. EViews will not check the values of your high and low data for consistency. If the
376—Chapter 12. Groups
high value is below the low value, EViews will draw “outside half-lines” that do not connect.
The third and fourth series are optional. If you provide only three series, the third series will be used as the “close” value in a high-low- close graph. The third series will be plotted as a right-facing horizontal line representing the “close” value.
If you provide four series,
the third series will represent the “open” value and will be plotted as a left-facing horizontal line. The fourth series will be used to represent the “close” value. The close value will be plotted as a right-facing horizontal line.
Pie
This view displays each observation as a pie chart, where the percentage of each series in the group is shown as a wedge in the pie.
If a series has a negative or missing value, the series will simply be dropped from the pie for that observation. You can label the observation number to each pie; double click in the background of the pie chart and mark the Label Pie option in the Graph Options dialog.
Multiple Graphs
While Graph views display all series in a single graph, Multiple Graphs views display a separate graph for each series in the group.
Line, Area, Bar and Spike Graphs
These views display a separate line, area, or bar graph for each series in the group.
Scatter
First series against all
This view displays scatter plots with the first series in the group on the horizontal axis and the remaining series on the vertical axis, each in a separate graph. If there are G series in the group, G − 1 scatter plots will be displayed.
Multiple Graphs—377
Matrix of all pairs (SCATMAT)
This view displays the scatterplot matrix, where scatter plots for all possible pairs of series in the group are displayed as a matrix. The important feature of the scatterplot matrix is that the scatter plots are arranged in such a way that plots in the same column share a common horizontal scale, while plots in the same row share a common vertical scale.
If there are G series in the group, G2 scatter plots will be displayed. The G plots on the main diagonal all lie on a 45 degree line, showing the distribution of the corresponding series on the 45 degree line. The G( G − 1 ) ⁄ 2 scatters below and above the main diagonal are the same; they are repeated so that we can scan the plots both across the rows and across the columns.
10
5
0
-5
-10
25
20
15
10
5
0
100
80
60
40
20
0
800
600
400
200
0
-10
log(Body Weight)
Total Sleeping Time
Maximum Life Span
Gestation Time
-5 |
0 |
5 |
10 |
0 |
5 |
10 |
15 |
20 |
25 |
0 |
20 |
40 |
60 |
80 |
100 |
0 |
200 |
400 |
600 |
800 |
Here is a scatter plot matrix that we copy-and-pasted directly into our document. Note that the resolution of the scatter plot matrix deteriorates quickly as the number of series in the group increases. You may want to freeze the view and modify the graph by moving the axis labels into the scatters on the main diagonal. You can also save more space by moving
378—Chapter 12. Groups
each scatter close to each other. Set the vertical and horizontal spacing by right-clicking and choosing the Position and align graphs... option.
XY line
This view plots the XY line graph of the first series on the horizontal X-axis and each of the remaining series on the vertical Y-axis in separate graphs. See the XY line view for Graph for more information on XY line graphs. If there are G series in the group, G − 1 XY line graphs will be displayed.
Distribution Graphs
CDF-Survivor-Quantile
This view displays the empirical cumulative distribution functions (CDF), survivor functions, and quantiles of each series in the group. These are identical to the series CDF-Sur- vivor-Quantile view; see “CDF-Survivor-Quantile” on page 391 for a detailed description of how these graphs are computed and the available options.
Quantile-Quantile
This view plots the quantiles of each series against the quantiles of a specified distribution or the empirical quantiles of another series. QQ-plots are explained in detail in “QuantileQuantile” on page 393.
One useful application of group QQ-plots is to form a group of simulated series from different distributions and plot them against the quantiles of the series of interest. This way you can view, at a glance, the QQ-plots against various distributions. Suppose you want to know the distribution of the series SLEEP2. First, create a group containing random draws from the candidate distributions. For example,
group dist @rnorm @rtdist(5) @rextreme @rlogit @rnd
creates a group named DIST that contains simulated random draws from the standard normal, a t-distribution with 5 degrees of freedom, extreme value, logistic, and uniform distributions. Open the group DIST, choose View/Multiple Graphs/Distribution Graphs/ Quantile-Quantile, select the Series or Group option and type in the name of the series SLEEP2 in the field of the QQ Plot dialog box.
