- •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
956—Appendix C. Estimation and Solution Options
Optimization Algorithms
Given the importance of the proper setting of EViews estimation options, it may prove useful to review briefly various basic optimization algorithms used in nonlinear estimation. Recall that the problem faced in non-linear estimation is to find the values of parameters θ that optimize (maximize or minimize) an objective function F(θ) .
Iterative optimization algorithms work by taking an initial set of values for the parameters, say θ(0) , then performing calculations based on these values to obtain a better set of parameter values, θ(1) . This process is repeated for θ(2) , θ(3) and so on until the objective function F no longer improves between iterations.
There are three main parts to the optimization process: (1) obtaining the initial parameter values, (2) updating the candidate parameter vector θ at each iteration, and (3) determining when we have reached the optimum.
If the objective function is globally concave so that there is a single maximum, any algorithm which improves the parameter vector at each iteration will eventually find this maximum (assuming that the size of the steps taken does not become negligible). If the objective function is not globally concave, different algorithms may find different local maxima, but all iterative algorithms will suffer from the same problem of being unable to tell apart a local and a global maximum.
The main thing that distinguishes different algorithms is how quickly they find the maximum. Unfortunately, there are no hard and fast rules. For some problems, one method may be faster, for other problems it may not. EViews provides different algorithms, and will often let you choose which method you would like to use.
The following sections outline these methods. The algorithms used in EViews may be broadly classified into three types: second derivative methods, first derivative methods, and derivative free methods. EViews’ second derivative methods evaluate current parameter values and the first and second derivatives of the objective function for every observation. First derivative methods use only the first derivatives of the objective function during the iteration process. As the name suggests, derivative free methods do not compute derivatives.
Second Derivative Methods
For binary, ordered, censored, and count models, EViews can estimate the model using Newton-Raphson or quadratic hill-climbing.
Optimization Algorithms—957
Newton-Raphson
Candidate values for the parameters θ(1) may be obtained using the method of NewtonRaphson by linearizing the first order conditions ∂F ⁄ ∂θ at the current parameter values,
θ(i) :
g(i) + H(i)( θ(i + 1) − θ(i)) = 0
− (C.2)
θ(i + 1) = θ(i) − H(i1)g(i)
where g is the gradient vector ∂F ⁄ ∂θ , and H is the Hessian matrix ∂2F ⁄ ∂θ2 .
If the function is quadratic, Newton-Raphson will find the maximum in a single iteration. If the function is not quadratic, the success of the algorithm will depend on how well a local quadratic approximation captures the shape of the function.
Quadratic hill-climbing (Goldfeld-Quandt)
This method, which is a straightforward variation on Newton-Raphson, is sometimes attributed to Goldfeld and Quandt. Quadratic hill-climbing modifies the Newton-Raphson algorithm by adding a correction matrix (or ridge factor) to the Hessian. The quadratic hillclimbing updating algorithm is given by:
˜ −1 |
˜ |
(C.3) |
θ(i + 1) = θ(i) − H(i)g(i) |
where −H(i) = − H(i) + αI |
where I is the identity matrix and α is a positive number that is chosen by the algorithm.
The effect of this modification is to push the parameter estimates in the direction of the gradient vector. The idea is that when we are far from the maximum, the local quadratic approximation to the function may be a poor guide to its overall shape, so we may be better off simply following the gradient. The correction may provide better performance at locations far from the optimum, and allows for computation of the direction vector in cases where the Hessian is near singular.
For models which may be estimated using second derivative methods, EViews uses quadratic hill-climbing as its default method. You may elect to use traditional Newton-Raph- son, or the first derivative methods described below, by selecting the desired algorithm in the Options menu.
Note that asymptotic standard errors are always computed from the unmodified Hessian once convergence is achieved.
First Derivative Methods
Second derivative methods may be computationally costly since we need to evaluate the k( k + 1) ⁄ 2 elements of the second derivative matrix at every iteration. Moreover, second derivatives calculated may be difficult to compute accurately. An alternative is to employ
958—Appendix C. Estimation and Solution Options
methods which require only the first derivatives of the objective function at the parameter values.
For general nonlinear models (nonlinear least squares, ARCH and GARCH, nonlinear system estimators, GMM, State Space), EViews provides two first derivative methods: GaussNewton/BHHH or Marquardt.
Gauss-Newton/BHHH
This algorithm follows Newton-Raphson, but replaces the negative of the Hessian by an approximation formed from the sum of the outer product of the gradient vectors for each observation’s contribution to the objective function. For least squares and log likelihood functions, this approximation is asymptotically equivalent to the actual Hessian when evaluated at the parameter values which maximize the function. When evaluated away from the maximum, this approximation may be quite poor.
The algorithm is referred to as Gauss-Newton for general nonlinear least squares problems, and often attributed to Berndt, Hall, Hall, and Hausman (BHHH) for maximum likelihood problems.
The advantages of approximating the negative Hessian by the outer product of the gradient are that (1) we need to evaluate only the first derivatives, and (2) the outer product is necessarily positive semi-definite. The disadvantage is that, away from the maximum, this approximation may provide a poor guide to the overall shape of the function, so that more iterations may be needed for convergence.
Marquardt
The Marquardt algorithm modifies the Gauss-Newton algorithm in exactly the same manner as quadratic hill climbing modifies the Newton-Raphson method (by adding a correction matrix (or ridge factor) to the Hessian approximation).
The ridge correction handles numerical problems when the outer product is near singular and may improve the convergence rate. As above, the algorithm pushes the updated parameter values in the direction of the gradient.
For models which may be estimated using first derivative methods, EViews uses Marquardt as its default method. You may elect to use traditional Gauss-Newton via the Options menu.
Note that asymptotic standard errors are always computed from the unmodified (GaussNewton) Hessian approximation once convergence is achieved.
Nonlinear Equation Solution Methods—959
Choosing the step size
At each iteration, we can search along the given direction for the optimal step size. EViews performs a simple trial-and-error search at each iteration to determine a step size λ that improves the objective function. This procedure is sometimes referred to as squeezing or stretching.
Note that while EViews will make a crude attempt to find a good step, λ is not actually optimized at each iteration since the computation of the direction vector is often more important than the choice of the step size. It is possible, however, that EViews will be unable to find a step size that improves the objective function. In this case, EViews will issue an error message.
EViews also performs a crude trial-and-error search to determine the scale factor α for Marquardt and quadratic hill-climbing methods.
Derivative free methods
Other optimization routines do not require the computation of derivatives. The grid search is a leading example. Grid search simply computes the objective function on a grid of parameter values and chooses the parameters with the highest values. Grid search is computationally costly, especially for multi-parameter models.
EViews uses (a version of) grid search for the exponential smoothing routine.
Nonlinear Equation Solution Methods
When solving a nonlinear equation system, EViews first analyzes the system to determine if the system can be separated into two or more blocks of equations which can be solved sequentially rather than simultaneously. Technically, this is done by using a graph representation of the equation system where each variable is a vertex and each equation provides a set of edges. A well known algorithm from graph theory is then used to find the strongly connected components of the directed graph.
Once the blocks have been determined, each block is solved for in turn. If the block contains no simultaneity, each equation in the block is simply evaluated once to obtain values for each of the variables.
If a block contains simultaneity, the equations in that block are solved by either a GaussSeidel or Newton method, depending on how the solver options have been set.
Gauss-Seidel
By default, EViews uses the Gauss-Seidel method when solving systems of nonlinear equations. Suppose the system of equations is given by:
960—Appendix C. Estimation and Solution Options
x1 = f1(x1, x2, …, xN, z)
x2 = f2(x1, x2, …, xN, z)
(C.4)
xN = fN(x1, x2, …, xN, z )
where x are the endogenous variables and z are the exogenous variables.
The problem is to find a fixed point such that x = f(x, z) . Gauss-Seidel employs an iterative updating rule of the form:
x(i + 1) = f(x(i), z) . |
(C.5) |
to find the solution. At each iteration, EViews solves the equations in the order that they appear in the model. If an endogenous variable that has already been solved for in that iteration appears later in some other equation, EViews uses the value as solved in that iteration. For example, the k-th variable in the i-th iteration is solved by :
x |
(i) |
= |
f |
k |
(x(i), x(i), …, x(i) , x(i − 1), x(i − 1), …, x(i − 1), z) . |
(C.6) |
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k |
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1 2 |
k − 1 k |
k + 1 |
N |
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The performance of the Gauss-Seidel method can be affected be reordering of the equations. If the Gauss-Seidel method converges slowly or fails to converge, you should try moving the equations with relatively few and unimportant right-hand side endogenous variables so that they appear early in the model.
Newton's Method
Newton’s method for solving a system of nonlinear equations consists of repeatedly solving a local linear approximation to the system.
Consider the system of equations written in implicit form: |
|
F(x, z) = 0 |
(C.7) |
where F is the set of equations, x is the vector of endogenous variables and z is the vector of exogenous variables.
In Newton’s method, we take a linear approximation to the system around some values x and z :
F(x, z) = F(x , z ) + |
∂ |
F(x , z )∆x = 0 |
(C.8) |
|
∂x |
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and then use this approximation to construct an iterative procedure for updating our current guess for x :
Nonlinear Equation Solution Methods—961
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F( x , z |
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F( x , z ) |
(C.9) |
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where raising to the power of -1 denotes matrix inversion.
The procedure is repeated until the changes in xt between periods are smaller than a specified tolerance.
Note that in contrast to Gauss-Seidel, the ordering of equations under Newton does not affect the rate of convergence of the algorithm.
962—Appendix C. Estimation and Solution Options