
- •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

Weighted Least Squares—469
Dependent Variable: LPRICE
Method: Least Squares
Date: 12/30/03 Time: 16:57
Sample: 1 506
Included observations: 506
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
|
|
|
|
|
C |
8.811812 |
0.217787 |
40.46069 |
0.0000 |
LNOX |
-0.487579 |
0.084998 |
-5.736396 |
0.0000 |
ROOMS |
0.284844 |
0.018790 |
15.15945 |
0.0000 |
RADIAL=1 |
0.118444 |
0.072129 |
1.642117 |
0.1012 |
RADIAL=2 |
0.219063 |
0.066055 |
3.316398 |
0.0010 |
RADIAL=3 |
0.274176 |
0.059458 |
4.611253 |
0.0000 |
RADIAL=4 |
0.149156 |
0.042649 |
3.497285 |
0.0005 |
RADIAL=5 |
0.298730 |
0.037827 |
7.897337 |
0.0000 |
RADIAL=6 |
0.189901 |
0.062190 |
3.053568 |
0.0024 |
RADIAL=7 |
0.201679 |
0.077635 |
2.597794 |
0.0097 |
RADIAL=8 |
0.258814 |
0.066166 |
3.911591 |
0.0001 |
|
|
|
|
|
|
|
|
|
|
R-squared |
0.573871 |
Mean dependent var |
9.941057 |
Adjusted R-squared |
0.565262 |
S.D. dependent var |
0.409255 |
S.E. of regression |
0.269841 |
Akaike info criterion |
0.239530 |
Sum squared resid |
36.04295 |
Schwarz criterion |
0.331411 |
Log likelihood |
-49.60111 |
F-statistic |
66.66195 |
Durbin-Watson stat |
0.671010 |
Prob(F-statistic) |
0.000000 |
|
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|
Weighted Least Squares
Suppose that you have heteroskedasticity of known form, and that there is a series w , whose values are proportional to the reciprocals of the error standard deviations. You can use weighted least squares, with weight series w , to correct for the heteroskedasticity.
EViews performs weighted least squares by first dividing the weight series by its mean, then multiplying all of the data for each observation by the scaled weight series. The scaling of the weight series is a normalization that has no effect on the parameter results, but makes the weighted residuals more comparable to the unweighted residuals. The normalization does imply, however, that EViews weighted least squares is not appropriate in situations where the scale of the weight series is relevant, as in frequency weighting.
Estimation is then completed by running a regression using the weighted dependent and independent variables to minimize the sum-of-squared residuals:
S( β) = Σwt2( yt − xt′β)2 |
(16.8) |
t

470—Chapter 16. Additional Regression Methods
with respect to the k -dimensional vector of parameters β . In matrix notation, let W be a diagonal matrix containing the scaled w along the diagonal and zeroes elsewhere, and let y and X be the usual matrices associated with the left and right-hand side variables. The weighted least squares estimator is,
bWLS = ( X′W′WX)−1X′W′Wy , |
(16.9) |
||||
and the estimated covariance matrix is: |
|
|
|
|
|
ˆ |
2 |
( X′W′WX) |
−1 |
. |
(16.10) |
ΣWLS = s |
|
To estimate an equation using weighted least squares, first go to the main menu and select
Quick/Estimate Equation…, then choose LS—Least Squares (NLS and ARMA) from the combo box. Enter your equation specification and sample in the Specification tab, then select the Options tab and click on the Weighted LS/TSLS option.
Fill in the blank after Weight with the name of the series containing your weights, and click on OK. Click on OK again to accept the dialog and estimate the equation.

Heteroskedasticity and Autocorrelation Consistent Covariances—471
Dependent Variable: LOG(X)
Method: Least Squares
Date: 10/15/97 Time: 11:10
Sample(adjusted): 1891 1983
Included observations: 93 after adjusting endpoints
Weighting series: POP
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
C |
0.004233 |
0.012745 |
0.332092 |
0.7406 |
LOG(X(-1)) |
0.099840 |
0.112539 |
0.887163 |
0.3774 |
LOG(W(-1)) |
0.194219 |
0.421005 |
0.461322 |
0.6457 |
|
|
|
|
|
Weighted Statistics
R-squared |
0.016252 |
Mean dependent var |
0.009762 |
Adjusted R-squared |
-0.005609 |
S.D. dependent var |
0.106487 |
S.E. of regression |
0.106785 |
Akaike info criterion |
-1.604274 |
Sum squared resid |
1.026272 |
Schwarz criterion |
-1.522577 |
Log likelihood |
77.59873 |
F-statistic |
0.743433 |
Durbin-Watson stat |
1.948087 |
Prob(F-statistic) |
0.478376 |
|
|
|
|
Unweighted Statistics
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
-0.002922 |
Mean dependent var |
0.011093 |
-0.025209 |
S.D. dependent var |
0.121357 |
0.122877 |
Sum squared resid |
1.358893 |
2.086669 |
|
|
EViews will open an output window displaying the standard coefficient results, and both weighted and unweighted summary statistics. The weighted summary statistics are based on the fitted residuals, computed using the weighted data:
˜ |
= wt( yt − xt′bWLS) . |
(16.11) |
ut |
The unweighted summary results are based on the residuals computed from the original (unweighted) data:
ut = yt − xt′bWLS . |
(16.12) |
Following estimation, the unweighted residuals are placed in the RESID series.
If the residual variance assumptions are correct, the weighted residuals should show no evidence of heteroskedasticity. If the variance assumptions are correct, the unweighted residuals should be heteroskedastic, with the reciprocal of the standard deviation of the residual at each period t being proportional to wt .
The weighting option will be ignored in equations containing ARMA specifications. Note also that the weighting option is not available for binary, count, censored and truncated, or ordered discrete choice models.
Heteroskedasticity and Autocorrelation Consistent Covariances
When the form of heteroskedasticity is not known, it may not be possible to obtain efficient estimates of the parameters using weighted least squares. OLS provides consistent

472—Chapter 16. Additional Regression Methods
parameter estimates in the presence of heteroskedasticity, but the usual OLS standard errors will be incorrect and should not be used for inference.
Before we describe the techniques for HAC covariance estimation, note that:
•Using the White heteroskedasticity consistent or the Newey-West HAC consistent covariance estimates does not change the point estimates of the parameters, only the estimated standard errors.
•There is nothing to keep you from combining various methods of accounting for heteroskedasticity and serial correlation. For example, weighted least squares estimation might be accompanied by White or Newey-West covariance matrix estimates.
Heteroskedasticity Consistent Covariances (White)
White (1980) has derived a heteroskedasticity consistent covariance matrix estimator which provides correct estimates of the coefficient covariances in the presence of heteroskedasticity of unknown form. The White covariance matrix is given by:
ˆ |
= |
T |
( X′X) |
−1 T |
u |
2 |
x |
|
−1 |
, |
(16.13) |
ΣW |
------------ |
Σ |
t |
x ′ ( X′X) |
|
||||||
|
|
T − k |
|
|
t |
t |
|
|
|
||
|
|
|
|
t = 1 |
|
|
|
|
|
|
|
where is T the number of observations, k is the number of regressors, and ut |
is the least |
||||||||||
squares residual. |
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EViews provides you the option to use the White covariance estimator in place of the standard OLS formula. Open the equation dialog and specify the equation as before, then push the Options button. Next, click on the check box labeled Heteroskedasticity Consistent Covariance and click on the White radio button. Accept the options and click OK to estimate the equation.
EViews will estimate your equation and compute the variances using White’s covariance estimator. You can always tell when EViews is using White covariances, since the output display will include a line to document this fact:
Dependent Variable: LOG(X)
Method: Least Squares
Date: 10/15/97 Time: 11:11
Sample(adjusted): 1891 1983
Included observations: 93 after adjusting endpoints
Weighting series: POP
White Heteroskedasticity-Consistent Standard Errors & Covariance
Variable |
Coefficient |
Std. Error |
t-Statistic |
Prob. |
|
|
|
|
|
C |
0.004233 |
0.012519 |
0.338088 |
0.7361 |
LOG(X(-1)) |
0.099840 |
0.137262 |
0.727369 |
0.4689 |
LOG(W(-1)) |
0.194219 |
0.436644 |
0.444800 |
0.6575 |
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