Veloce, potente e flessibile più che mai!
è il software leader di mercato per la stima e la simulazione
di modelli econometrici.
Grazie all'interfaccia object-oriented, EViews offre
una vasta scelta di analisi statistiche e visualizzazioni
grafiche senza bisogno di memorizzare complicati comandi,
linguaggi particolari o scorrere infiniti menu di comandi.
Molte procedure statistiche sono semplici alternative tra
oggetti di EViews: un semplice menu consente di scegliere
tra differenti visualizzazioni degli stessi dati, che possono
essere mostrati come foglio elettronico, grafici.
La versione 8 è stata potenziata con numerosi miglioramenti sia alle funzionalità generali di base (64-bit, interfaccia utente, ecc.) sia a quelle delle aree di gestione dati, analisi econometriche e statistiche, produzione di grafici e tabelle, costruzione di modelli e supporto alla programmazione.
EViews viene fornito nella versione Standard o nella versione
versione Enterprise che aggiunge alle caratteristiche della
versione standard l'accesso diretto a database ODBC e la connessione
trasparente ai più importanti database di dati economici forniti
da terze parti: Global Insight's databanks2, Haver Analytics
DLX3, Datastream, FactSet, Moody's Economy.com e il formato
A combination of power and ease-of-use make EViews 8 the ideal package for anyone working with time series, cross-section, or longitudinal data. With EViews, you can quickly and efficiently manage your data, perform econometric and statistical analysis, generate forecasts or model simulations, and produce high quality graphs and tables for publication or inclusion in other applications.
Featuring an innovative graphical object-oriented user-interface and a sophisticated analysis engine, EViews blends the best of modern software technology with the features you've always wanted. The result is a state-of-the art program that offers unprecedented power within a flexible, easy-to-use interface.
Find out for yourself why EViews is the worldwide leader in Windows-based econometric software and the choice of those who demand the very best.
Part 1: An Intuitive, Easy-to-Use Interface
EViews sets the standard for
what statistical software can be by incorporating modern windowing and object-based
techniques in econometric software. The result is a program that provides unprecedented
power, wrapped in an intuitive, easy-to-use user interface.
An Object-Based Interface
At the heart of the innovative
EViews interface is the concept of an object. Series, equations, and systems are just a
few examples of objects. Each object has its own window, menus, procedures, and its own
views of its data. Most statistical procedures are simply alternate views of the object.
For example, a simple menu choice from a series window changes the display between a
spreadsheet, various graph views, descriptive statistics and tests, tabulations,
correlograms, unit root, and independence tests.
Similarly, an equation window allows you to
switch between a display of the equation specification, basic estimation results,
actual-fitted-residual graphs and tables, a display of the equation ARMA structure (if
appropriate), gradients and derivatives of the specification, the coefficient covariance
matrix, forecast graphs and evaluations, and over a dozen diagnostic and hypothesis tests.
|You can select a
histogram view from the series-specific menu.
Multiple Window Display
Unlike traditional statistics
programs that support viewing only one estimation equation or graph at a time, EViews
allows for simultaneous display of multiple objects, each in its own window. This true
multiple window support makes it easy to perform side-by-side comparisons of series plots,
hypothesis tests, equation estimates, or model forecasts developed under alternative
|EViews offers true
Dynamic Object Updating
EViews incorporates the best of modern spreadsheet and relational database technology into tools for performing
the traditional tasks of statistical software. The EViews object-based approach includes sophisticated
linking technology that allows you to define relationships between multiple objects and
external data sources. Series objects, for example, may be linked by formula to data
in other series, to match merged or frequency converted data from alternate data sets, or
to data from external databases. When defined in this fashion, the linked series
dynamically updates its data whenever the underlying data change.
Similarly, an EViews model simulation object can be linked to equation or system objects so that the model
specification updates automatically when the underlying equation or system is re-specified
technology offers dynamic updating of data.
Couple all of this with strong Windows integration,
including drag-and-drop file import for over twenty popular file formats and
copy-and-paste export of presentation quality graphs and tables, and you have a modern
interface that allows you to accomplish, with ease, tasks that are difficult or impossible
using traditional statistical software.
|Easy data import
Add-Ins and User Objects
EViews offers an easy-to-use EViews Add-ins infrastructure that provides seamless access to user-defined programs using the standard EViews command,
menu, and object interface.
Add-ins offer you a exciting new way of running EViews programs. You may readily define
Add-ins that augment the EViews language with user-defined commands, specify new menu
entries for point-and-click program interaction, and display program output in standard
EViews object windows.
User objects further extend EViews by allowing the creation of user-defined objects inside your workfiles. User objects can be as simple as a basic results storage containier, to as complicated as a fully functioning estimation object.
All EViews users may benefit immediately by installing prepackaged Add-ins which add
functionality to EViews.
|Add-ins offer seamless access to user-defined programs.
Part 2: Powerful Analytical Tools
In contrast with most other econometric software, there is no reason for most users to learn a complicated command language. EViews' built-in procedures are a mouse-click away and provide the tools most frequently used in practical econometric and forecasting work.
Basic Statistical Analysis
EViews supports a wide range
of basic statistical analyses, encompassing everything from simple descriptive statistics
to parametric and nonparametric hypothesis tests.
Basic descriptive statistics are quickly and easily
computed over an entire sample, by a categorization based on one or more variables, or by
cross-section or period in panel or pooled data. Hypothesis tests on mean, median and
variance may be carried out, including testing against specific values, testing for
equality between series, or testing for equality within a single series when classified by
other variables (allowing you to perform one-way ANOVA). Tools for covariance and factor
analysis allow you to examine the relationships between variables.
You can visualize the distribution of your data using
histograms, theoretical distribution, kernel density, or cumulative distribution,
survivor, and quantile plots. QQ-plots (quantile-quantile plots) may be used to compare
the distribution of a pair of series, or the distribution of a single series against a
variety of theoretical distributions.
You can even perform Kolmogorov-Smirnov, Liliefors, Cramer
von Mises, and Anderson-Darling tests to see whether your series is distributed normally,
or whether it comes from another distribution such as an exponential, extreme value,
logistic, chi-square, Weibull, or gamma distribution.
EViews also produces scatter plots with curve fitting using
ordinary, transformation, kernel, and nearest neighbor regression.
|EViews performs a
wide range of basic statistical analysis.
distribution of your data.
|Add regression and
curve fitting (and histogram borders) to your scatterplots.
Time Series Statistics and Tools
Explore the time series
properties of your data with tools ranging from simple autocorrelation plots to frequency
filters, from Q-statistics to unit root tests.
EViews provides autocorrelation and partial autocorrelation
functions, Q-statistics, and cross-correlation functions, as well as unit root tests (ADF,
Phillips-Perron, KPSS, DFGLS, ERS, or Ng-Perron for single time series and
Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, or Hadri for panel data), cointegration
tests (Johansen for with MacKinnon-Haug-Michelis critical values and p-values ordinary
data, and Pedroni, Kao, or Fisher for panel data), causality, and independence tests.
EViews also provides easy-to-use front-end
support for the U.S. Census Bureau's X-13 Seasonal Adjustment programs, which is an updated version of both X-12, and Tramo/Seats.
Simple seasonal adjustment using additive and multiplicative difference methods is also
supported in EViews.
You can even use EViews to compute trends and
cycles from time series data using the Hodrick-Prescott filter, Baxter-King,
Christiano-Fitzgerald fixed length and Christiano-Fitzgerald asymmetric full sample
band-pass (frequency) filters.
|Explore the time
series properties of your data.
easy-to-use interfaces to X12 and Tramo/Seats.
|Use filters to
compute trends and cycles from your time series data.
Panel and Pooled Data Statistics and Tools
EViews features a wide variety of tools designed to facilitate working with both panel or pooled/time series-cross
section data. Define panel structures with virtually no limit on the number of
cross-sections or groups, or on the number of periods or observations in a group. Dated or
undated, balanced or unbalanced, and regular or irregular frequency panel data sets are
all handled naturally within the EViews framework.
Data structure tools
facilitate transforming your data from stacked (panel) to unstacked (pooled)
formats, and back again. Smart links, auto series, and data extraction tools, allow you to
slice, match merge, frequency convert, and summarize your data with ease.
Support for basic longitudinal data analysis ranges from convenient by-group and by-period
statistics, tests, and graphs, to sophisticated panel unit root (Levin-Lin-Chu,
Breitung, Im-Pesaran-Shin, or Fisher) and cointegration diagnostics (Pedroni
(2004), Pedroni (1999), and Kao, or the Fisher-type test).
Specialized tools for displaying panel data graphs allow you to view stacked, individual,
or summary displays. Display line graphs of each graph in a single graph frame or in
individual frames. Or show summary statistics for the panel data taken across
cross-sections, with mean (or median) and standard deviation (or quantile) bands.
Single Equation Estimation
EViews allows you to
choose from a full set of basic single equation estimators including: ordinary and
nonlinear least squares (multiple regression), weighted least squares, two-stage least
squares (instrumental variables), quantile regression (including least absolute deviations
estimation), and stepwise linear regression. Weighted
estimation is available for all of these techniques. Specifications may include
polynomial lag structures on any number of independent variables.
For time series analysis, EViews estimates ARMA and ARMAX models, and a wide range of ARCH specifications.
Structural time series models may be estimated using the state space object.
In addition to these basic estimators, EViews supports
estimation and diagnostics for a variety of advanced models.
Generalized Method of Moments (GMM)
EViews supports GMM estimation for both
cross-section and time series data (single and multiple equation). Weighting options
include the White covariance matrix for cross-section data and a variety of HAC covariance
matrices for time series data. The HAC options include prewhitening, a variety of kernels, and fixed, Andrews, or Newey-West bandwith selection methods.
You can estimate a GMM equation using either iterative procedures, or a continuously updating procedure. Post-estimation diagnostics for GMM equations,
including weak instrument statistics, are also available.
If the variance of your series fluctuates over time,
EViews can estimate the path of the variance using a wide variety of Autoregressive
Conditional Heteroskedasticity (ARCH) models. EViews handles GARCH(p,q), EGARCH(p,q),
TARCH(p,q), PARCH(p,q), and Component GARCH specifications and provides maximum likelihood
estimation for errors following a normal, Student's t
or Generalized Error Distribution. The mean equation of ARCH
models may include ARCH and ARMA terms, and both the mean and variance equations allow for
Limited Dependent Variables
EViews also supports estimation of a range of limited dependent variable models. Binary, ordered, censored, and truncated models may be estimated for likelihood
functions based on normal, logistic, and extreme value errors. Count models may use
Poisson, negative binomial, and quasi-maximum likelihood (QML) specifications. EViews
optionally reports generalized linear model or QML standard errors.
Panel and Pooled Time Series-Cross Section
EViews offers various panel and pooled data estimation
methods. In addition to ordinary linear and non-linear least-squares, equation estimation
methods include 2SLS/IV and Generalized 2SLS/IV, and GMM, which can be used to estimate
complex dynamic panel data specifications (including Anderson-Hsiao and Arellano-Bond
types of estimators).
Most of the methods allow for both time and cross-section
fixed and random effects specifications. For random effects models, quadratic unbiased
estimators of component variances include Swamy-Arora, Wallace-Hussain and
Also supported are AR specifications (any effects are
defined after transformation), weighted least squares, and seemingly unrelated regression.
In pools, coefficients for specific variables (including AR terms) can be constrained to
be identical, or allowed to differ across cross-sections.
offers a full range of single equation estimators.
estimation offers a variety of weighting matrix and covariance options.
dialogs make it easy to specify your ARCH model.
estimates both ML and QML count models.
offers a range of panel data estimators and options.
(optional) wizard leads you through the specification of your dynamic panel data
EViews also offers powerful tools for analyzing
systems of equations. You may use EViews to estimation
of both linear and nonlinear systems of equations by OLS, two-stage least squares,
seemingly unrelated regression, three-stage least squares, GMM, and FIML. The system may
contain cross equation restrictions and in most cases, autoregressive errors of any order.
Vector Autoregression/Error Correction Models
Vector Autoregression (including Bayesian VARs) and Vector Error Correction models
can be easily estimated by EViews. Once estimated, you may examine the impulse response
functions and variance decompositions for the VAR or VEC. VAR impulse response functions
and decompositions feature standard errors calculated either analytically or by Monte
Carlo methods (analytic not available for decompositions) and may be displayed in a
variety of graphical and tabular formats.
You may impose and test linear restrictions on the
cointegrating relations and/or adjustment coefficients. EViews' VARs also allow you to
estimate structural factorizations (VARs) by imposing short-run (Sims 1986) or long-run
(Blanchard and Quah 1989) restrictions. Over-identifying restrictions may be tested using
the LR statistic reported by EViews.
VARs support a variety of views to allow you to examine the
structure of your estimated specification. With a few clicks of the mouse, you can display
the inverse roots of the characteristic AR polynomial, perform Granger causality and joint
lag exclusion tests, evaluate various lag length criteria, view correlograms and
autocorrelations, or perform various multivariate residual based diagnostics.
Multivariate ARCH is useful in modeling time varying
variance and covariance of multiple time series. A number of popular ARCH models, such as
the Conditional Constant Correlation (CCC), the Diagonal VECH, and the Diagonal BEKK, are
available. Exogenous variables are allowed in the mean and variance equations; nonlinear and
AR terms can be included in the mean equations. The error is assumed to
distributed either as
multivariate Normal or Student's t. Bollerslev-Wooldridge robust standard errors are also available.
Once the model is estimated, users can easily generate the in-sample variance,
covariance, or correlation, in tabular or graphic format.
The state-space object allows estimation of a wide variety
of single- and multi-equation dynamic time-series models using the Kalman Filter
algorithm. Among other things, you can use the state-space object to estimate random and
time-varying coefficient models and ML ARMA specifications.
Sophisticated procs and views give you access to
powerful filtering and smoothing tools so that you can view or generate one-step ahead,
filtered, or smoothed signals, states, or errors. EViews' built-in forecasting procedures
also provide easy-to-use tools for in- and out-of-sample forecasting using n-step ahead or smoothed values.
|Specify and estimate
systems of equations using the system object.
|Estimate VAR or VEC models
and easily produce impulse response graphs.
|Model the system
covariances and correlations using multivariate ARCH.
|Estimate state space models
using the Kalman Filter and display filtered results.
User-Specified Maximum Likelihood
For custom analysis, EViews' easy-to-use likelihood object
permits estimation of user-specified maximum likelihood models. You simply provide
standard EViews expressions to describe the log likelihood contributions for each
observation in your sample, set coefficient starting values, and EViews will do the rest.
|Define your own estimator
using the likelihood object.
Part 3: Sophisticated Data Management
Powerful analytic tools are only useful if you can easily work with your data. EViews provides the widest range of data management tools available in any econometric software. From its extensive library of mathematical, statistical, date, string, and time series operators and functions, to comprehensive support for numeric, character, and date data, EViews offers the data handling features you've come to expect from modern statistical software.
Extensive Function Library
EViews includes an extensive library of functions for working with data. In addition to standard mathematical
and trigonometric functions, EViews provides functions for descriptive statistics,
cumulative and moving statistics, by-group statistics, special functions, specialized date
and time series operations, workfile, value map, and financial calculations.
EViews also provides random number generators (Knuth,
L'Ecuyer or Mersenne-Twister), density functions and cumulative distribution functions for
eighteen different distributions.These may be used in generating new series, or in
calculating scalar and matrix expressions.
|EViews offers an
extensive library of functions.
Sophisticated Expression Handling
EViews' powerful tools for expression handling mean that you can use expressions virtually
anywhere you would use a series. You don't have to create new variables to work with the
logarithm of Y, the moving average of W, or the ratio of X to Y (or any other valid
expression). Instead, you can use the expression in computing descriptive statistics, as
part of an equation or model specification, or in constructing graphs.
When you forecast using an equation with an expression for
the dependent variable, EViews will (if possible) allow you to forecast the underlying
dependent variable and will adjust the estimated confidence interval accordingly. For
example, if the dependent variable is specified as LOG(G), you can elect to forecast
either the log or the level of G, and to compute the appropriate, possibly asymmetric,
|Work directly with
expressions in place of variables.
Links, Formulas and Values Maps
Link objects allow you to create series that link to data contained in other workfiles or workfile
pages. Links allow you to combine data at different frequencies, or match merge in data
from a summary page into an individual page such that the data is dynamically updated
whenever the underlying data change. Similarly, within a workfile, formulas can be
assigned to data series so that the data series are automatically recalculated whenever
the underlying data is modified.
Value labels (e.g., "High", "Med",
"Low", corresponding to 2, 1, 0) may be applied to numeric or alpha series so
that categorical data can be displayed with meaningful labels. Built-in functions allow
you to work with either the underlying or the mapped values when performing calculations.
|Links may be used for
dynamic frequency conversion or match merging.
Structures and Types
EViews can handle complex data structures, including regular and irregular dated data, cross-section data
with observation identifiers, and dated and undated panel data.
In addition to numerical data, an EViews workfile can also
contain alphanumeric (character string) data, and series containing dates, all of which
may be manipulated using an extensive library of functions.
EViews also provides a wide range of tools for working with
datasets (workfiles), data including the ability to combine series by complex match merge
criteria and workfile procedures for changing the structure of your data: join,
append, subset, resize, sort, and reshape (stack and unstack).
|EViews workfiles can
be highly structured.
|Alphanumeric and date
data are fully supported.
File Import and Export
Exchanging data with other programs is easy, since EViews reads and writes over 20 popular data
formats (including Excel, formatted and unformatted
ASCII/Text, SPSS, SAS (transport), Stata, SPSS, Html, Microsoft Access, Gauss
Dataset, Rats, GiveWin/PC Give, TSP, Aremos, dBase, Lotus, and binary files). Simply drag-and-drop your foreign file onto
EViews and your data will automatically appear in an EViews workfile. Or use the
easy-to-use dialogs and wizards to cutomize the importing of your data.
|EViews reads and
writes an extensive list of data formats.
EViews provides sophisticated
built-in database features. An EViews database is a collection of EViews objects
maintained in a single file on disk. It need not be loaded into memory in order to access
an object inside it, and the objects in the database are not restricted to being of a
single frequency or range. EViews databases offer powerful query features which can be
used to search through the database for a particular series or select a set of series with
a common property.
Series contained in EViews databases may be copied
(fetched) into a workfile, or they may be accessed and used by EViews procedures without
being fetched into workfiles. In both cases, EViews will automatically perform frequency
conversion if necessary. Automatic search capabilities allow you to specify a list of
databases to be searched when a series you need cannot be found in the current workfile.
tools offer powerful query features.
|Easily move data from
databases to your EViews workfiles.
Edition Support for ODBC, FAMETM, DRIBase, and Haver Analytics Databases
As part of the EViews Enterprise
Edition (an extra cost option over EViews Standard Edition), support is provided for
access to data contained in relational databases (via ODBC drivers) and to databases in a
variety of proprietary formats used by commercial data and database vendors. Open Database
Connectivity (ODBC) is a standard supported by many relational database systems including
Oracle, Microsoft SQL Server and IBM DB2. EViews allows you to read or write entire tables
from ODBC databases, or to create a new workfile from the results of a SQL query.
EViews Enterprise Edition also supports access to FAMETM
format databases (both local and server based) Global Insight's DRIPro and DRIBase
databanks, Haver Analytics DLX databases, Datastream, FactSet, and Moody's Economy.com.
The familiar, easy-to-use EViews database interface has been extended to these data
formats so that you may work with foreign databases as easily as native EViews databases.
|Read directly from
ODBC sources using the EViews Enterprise Edition.
Edition offers direct access to additional commercial vendor data formats.
When you import data from a database or from another workfile or
workfile page, it is automatically converted to the frequency of your current project.
EViews offers many options for frequency conversion, and
includes support for the conversion of daily, weekly, or irregular-frequency data. Series
may be assigned a preferred conversion method, allowing you to use different methods for
different series without having to specify the conversion method every time a series is
You can even create links so that the frequency converted
data series are automatically recalculated whenever the underlying data is modified.
series-specific automatic conversion or select a specific method.
Part 4: Presentation Quality Output
EViews 7 supports a wide range of basic graph types including line graphs, bar graphs, filled area graphs, pie charts, scatter diagrams, mixed line-bar graphs, high-low graphs, scatterplots, and boxplots. Any number of graphs can be combined in a single graph for presentation.
EViews 7 supports a wide range of graph types
Various options give you control over line types, symbols, color,
frame and border characteristics, headings, shading, and scaling, including logarithmic
scaling and dual scale graphs. Legends are created automatically. You may further
customize your graph by adding labels in any scalable Windows font.
Customizing a graph is as simple as modifying or moving
graphic elements on the screen. Everything from aspect ratios, to line and symbol
characteristics, to axes scaling and labeling is right at your fingertips. Want to change
the font or other characteristics of a legend or a text label? Just click on an element of
the graph and your choices are presented in an easy to understand dialog. You can even use
a customized graph template to modify all of your graph settings at once.
You can quickly incorporate customized graphs into
other applications using copy-and-paste or by writing the graph to a Windows metafile, or
a PostScript, bitmap, PNG, GIF, or JPEG file.
Export graphs in a variety of formations.
customization tools allow you to produce presentation quality tables for inclusion in
other programs. An easy-to-use, interactive interface gives you control over cell font
face, size, and color, cell background color and borders, merging, and annotation.
EViews offers an extensive set of table
you can copy-and-paste your customized table to another application or save it as an RTF,
HTML, PDF, or text file.
Part 5: Traditional Command Line and Programming
Point-and-click is great, but what if you feel more
comfortable entering commands? And what if you need programming capabilities? In addition
to its state-of-the-art windowing interface, EViews includes a powerful command language
that provides access to the features that are available through the menus.
All EViews features are
available via the command line.
Modeled loosely after the BASIC programming language but with object-oriented extensions and
matrix handling capabilities, EViews allows you to enter individual commands for immediate
or batch execution. Your programs can make use of looping and condition branching, as well
as subroutine, macro, and string list processing.
Create program files for batch processing of commands.
primitives, from simple multiplication and inversion, to more advanced procedures for
Kronecker products, eigenvector solution, and singular value decomposition, provide you
with the tools you need for solving complex mathematical problems.
What's New in EViews 8
EViews 8 features a wide range of exciting changes and improvements. The following is an overview of the most important new features in Version 8.
Graphs, Tables and Spools
Econometrics and Statistics
Testing and Diagnostics
- Improved model data editing.
- Solution comparison tools.
- Enhanced command line manipulation of models.
EViews 8 performance has been enhanced through the introduction of a 64-bit version.
EViews is now available in both 32 and 64-bit versions.
One of the advantages of using a 64-bit version of Windows is the ability to access physical memory (RAM) beyond the 4-gigabyte (GB) range. By comparison, 32-bit versions of Windows are limited to a maximum of approximately 3.2 gigabytes of memory.
The 64-bit version of EViews 8 allows access to the larger amounts of physical RAM in machines running 64-bit Windows, allowing you to work with much bigger workfiles, both in terms of the number of observations per workfile page, and the number of individual objects allowed in a page.
For example, 32-bit versions of EViews only allow a maximum of 15 million observations in a page (and even then we recommend much smaller workfile ranges). The 64-bit version of EViews 8 allows up to 120 million observations per page. Similarly, the 32-bit versions of EViews can exhaust available memory with more than a few hundred thousand objects in the workfile, while the 64-bit version supports workfiles with millions of objects.
EViews has always been known for its unmatched ease-of-use, but there's always room for improvement. We've raised the ante in EViews 8 with a number of interface improvements. Here are but a few of the highlights:
Enhanced Dialog Edit Fields
Edit fields (boxes that let you type an entry) in EViews 8 have been enhanced with two new features: smart auto-complete and expansion.
Smart auto-complete allows you to quickly enter object names in edit fields, by bringing up a list of objects in the current workfile from which you may select to populate the edit field.
Edit field expansion lets you increase the size of edit fields in EViews so that you may more easily see and enter information. To expand an edit field, simply double click on the white space in the box, or right click and select Expand.
EViews 8 features a new look workfile Details view. You may toggle between the ordinary workfile display and the Details view by clicking on the Details +/- button on the workfile.
Each object attribute now has a separate column in the details view, and you may sort the objects by an attribute by clicking on the column header. Columns are also draggable and resizable, allowing you to alter their position and width. If you right-click on a column header, you may also choose which columns to display.
The new Workfile Compare view, available from the View menu on a workfile allows you to compare the differences between a workfile and another workfile stored on disk. Once you have chosen a second workfile with which to compare, EViews will display a list of all objects in the two workfiles, and let you see how those objects differ.
Object Linking and Embedding (OLE)
EViews 8 support for OLE offers you the ability to have your output data and graphs update whenever you make changes within EViews.
You may use OLE to paste links to EViews objects in your external document so that the underlying information is tied to the EViews workfile. Then, any time modifications are made in EViews, the changes may be pushed to the objects in your document. Alternately, you may use OLE to embed graph and table output in external documents so you may later modify the appearance of the output using EViews.
New Data Handling Features
EViews 8 offers a number of features for data handling. Among the highlights:
Spreadsheet Editing Tools
EViews 8 provides sophisticated new tools for editing and adjusting the values in an EViews series or group.
Typically, the primary method of generating series values is to use a series expression. EViews will evaluate the series expression for all observations in the current sample and assign values accordingly. Note that working with subsets of data requires specifying a new sample for each subset operation. Alternately, standard editing of series values by entering numbers can be cumbersome, at best.
EViews 8 changes all of this by providing tools that allow you to enter and modify individual values in a series using a powerful array expression language, and to view the effects of those changes on the series values.
Standard editing now allows you to use the expression language to assign or modify one or more cells. When Edit mode is enabled by toggling the Edit +/- button, you may simply select the cells you wish to edit, then use array expressions to describe how you would like the multiple cells to be modified.
EViews 8 offers an adjust mode which may be enabled by toggling the Adjust+/- button on the spreadsheet toolbar. The adjust mode allows you to use sophisticated editing tools to make prospective changes in the series and to see the impact of those changes in an interactive fashion. These changes may be specified in natural units, so, for example, if you wish to examine the impact of a 10% increase in the values in a series over some range, you simply tell EViews that "Delta %" equals 10.
Since changes made in adjust mode are not permanent unless specifically made so when you close the series window, this powerful tool you to changes to a series to perform quick "what if" analysis without permanently changing the series.
Group Comparison Tools
EViews 8 lets you easily compare the data between the series in your group. When looking at the Spreadsheet view of the group, simply press the Compare +/- button on the toolbar to enter compare mode. Compare mode will behave differently depending upon whether there are only two series in the group, or more than two. In both cases the main feature of compare mode is that it will highlight, in red, any observations for which the series in the group have different values. This can be useful when comparing revisions to series in order to quickly find for which observations any revisions or changes have been made.
Dated Data Table Support
Dated data tables have been improved with added customization options, including unit and label formatting, font and color selection on an individual series level, and tools for customizing date format and appearance.
Additionally, program language support is now offered for the customization of dated data tables.
Excel .XLSX file support
EViews now offers write support for Excel XLSX files. Previously, EViews 7 offered read, but not write, support for XLSX files, and prior versions of EViews did not support the format.
EViews also offers the ability to write into an existing Excel file without over-writing the data currently stored in that file. You may specify the exact cell you wish EViews to start writing to.
Transposed Foreign Data Reads
In EViews 8 You may now read transposed data from a foreign file into a new or existing workfile page using the File/Open Foreign Data as Workfile..., Proc/Load Workfile Page..., or Proc/Import/Load Workfile Page... dialogs.
Previously you could only read transposed data into an existing workfile page using the older (now mostly deprecated) Proc/Import/Import from File... dialog and the corresponding read command, which supported fewer foreign source formats. Notable among the formats that read did not support was Excel ".XLSX".
Custom Object Attributes
Objects in an EViews workfile may now be assigned custom attributes. These attributes may be used by search queries in EViews workfile and database operations. In some cases, EViews will be able to import custom attributes along with the data from third-party databases.
You may create or edit a custom attribute by clicking on the Label view of an object, and typing the name of the custom attribute below the Description field and the value of the attribute in the field to the right.
New Graph, Table and Spool Features
Graph Sample Slider Bar
EViews 8 graphs now feature a sample slider bar, located at the bottom of a sample based graph window which allows you to adjust dynamically the sample displayed in the graph window by resizing and moving the slider bar:
Graph Arrows and Lines
It's often useful to accentuate a data point in a graph or draw a comparison between two points. In EViews 8, you can draw custom straight lines at any angle, anywhere in a frozen graph window. You may also choose from multiple designs for the arrowheads, including none (plain line), filled arrow, and open arrow.
User-Defined Fit Lines
Just as you may wish to highlight a particular data point in your graph with an arrow, you might like to add custom fit lines to a scatter plot. Earlier versions of EViews supported fit lines drawn using calculations based upon the underlying data. The new fit line option, User-defined, lets you specify your own definition line definition.
PDF and Enhanced Metafile Export
The PDF format is one of the most commonly used standards for saving and sharing documents, and is arguably the standard for documents on the web. EViews 8 now supports the saving of graph, table, and spool output to PDF. The options to save as PDF are included in the standard save dialogs for graphs, tables, and spools.
In addition, table output may now be saved to Enhanced Metafile (EMF) format.
EViews 8 New Econometrics and Statistics: Computation
EViews 8 features a number of additions and improvements to its toolbox of basic statistical procedures. Among the highlights are new tools for exponential smoothing, panel series covariances and principle components, as well as support for the new Census X-13 seasonal adjustment tools.
ETS Exponential Smoothing
EViews 8 now offers support for exponential smoothing using the dynamic nonlinear model framework of Hyndman, Koehler, et al. (2002).
The ETS (Error-Trend-Seasonal or ExponenTial Smoothing) framework which defines an extended class of exponential smoothing methods that encompasses standard ES models (e.g., Holt and Holt-Winters additive and multiplicative methods), but offer a variety of new methods.
In addition ETS smoothing offers a theoretical foundation for analysis of these models using state-space based likelihood calculations, with support for model selection and calculation of forecast standard errors.
EViews 8 offers an easy-to-use front-end for working with the U.S. Census Bureau's new X-13 seasonal adjustment tools. In addition to providing a wide range of new features (including ARIMA regression prior to the seasonal adjustment step), X-13 is capable of performing updated versions of X-11/X-12 or TRAMO/SEATS ARIMA seasonal adjustment.
Panel covariances and correlations are widely used in panel data analysis. For example:
- Contemporaneous correlations between macroeconomic variables are often used to examine the nature of relationships between different countries (see for example, Obstfeld and Rogoff, 2001, p. 368).
- The contemporaneous covariances of residuals from panel regression are used in computing cross-sectional Zellner SUR-type estimators (Johnston and Dinardo, 1997, p.318) and in tests of cross-section dependence (Pesaran, 2004). Similarly, panel covariances are used as a first step in obtaining common factors for unit root and other tests (Bai and Ng, 2004).
Panel Principle Components
In addition to computing measures of association for a series across cross-sections or periods EViews 8 also computes the principal components of the panel variable using one of the measures of association.
As with the other principal components tools in EViews, you may display the table of eigenvalues and eigenvectors, display line graphs of the ordered eigenvalues, and examine scatterplots of the loadings and component scores. Furthermore you may save the component scores and corresponding loadings to the workfile.
EViews 8 New Econometrics and Statistics: Estimation
EViews 8 new estimation features include Switching Regression (including Markov Switching), Bayesian VARs, Robust Least Squares, Breakpoint Regression, Heckman Selection, and Panel Cointegration.
EViews 8 now estimates single-equation switching regression models-linear regression models with nonlinearities arising from unobserved discrete changes in regime, including models with independent and Markov switching. EViews also offers tools for filtering, smoothing, and forecasting from your estimated equation.
Dynamics specifications are permitted through the use of lagged dependent variables as explanatory variables and through the presence of auto-correlated errors (Goldfeld and Quandt, 1973, 1976; Maddala, 1986; Hamilton, 1994; Frühwirth-Schnatter, 2006).
EViews 8 now estimates Bayesian Vector Autoregression (BVAR) models which, as the name suggests, employ uses Bayesian methods to estimate a vector autoregression (VAR). EViews supports four different prior specifications on the parameters:
- Litterman/Minnesota prior.
- Normal-Wishart prior.
- Sims-Zha Normal-Wishart prior.
- Sims-Zha Normal-flat.
to provide shrinkage (restrictions on parameters to reduce the size of the parameter set) over the unrestricted least squares VAR estimates.
Robust Least Squares
EViews 8 supports robust least squares regression methods designed to be robust, or less sensitive, to outliers. EViews offers three different methods for robust least squares: M-estimation (Huber, 1973), S-estimation (Rousseeuw and Yohai, 1984), and MM-estimation (Yohai 1987).
EViews 8 offers new tools for estimating linear regression models that are subject to structural change. The regime breakpoints may be known and specified a priori, or they may be estimated using the Bai (1997) and Bai and Perron (1998), global maximizer or sequential methods, and related techniques. You may estimate "pure" breakpoint specifications in which all of the regressors have regime specific coefficients, or specifications in which only
some coefficients vary with the regime.
Heckman Selection Models
Under the Heckman selection framework, the dependent variable, yi in a linear regression model is only observable for a portion of the data. A classic example, in economics, of the sample selection problem is the wage equation for women, whereby a woman's wage is only observed if she makes the decision to enter the work place, and is unobservable if she does not. The resulting selectivity bias implies that ordinary least squares is no longer an
EViews 8 offers two different methods of estimating the Heckman (1979) least squares model with sample selection: Heckman's original two-step method, and maximum likelihood estimation.
Panel Cointegration Estimation
EViews 8 now offers tools for estimation of single equation panel cointegration estimators. You may estimate your specification using the Fully Modified OLS (FMOLS) panel estimators outlined by Pedroni (2000), or the panel dynamic ordinary least squares (DOLS) estimators described by (Kao and Chiang, 2000; Mark and Sul, 2003).
For both classes of estimators, EViews offers the pooled and weighted forms of the estimators, which combine data across cross-sections, and the grouped estimators, which combine across cross-sections, the estimates obtained for each cross-section.
EViews offers a wide variety of built-in estimation methods that involve optimization, including (but not limited to) those supported by the Equation, System, Sspace, and VAR objects.
In addition, the EViews Logl object lets you maximize user-defined likelihood functions. but the Logl object is restricted to computations that can be specified using series expressions, with a log-likelihood objective represented as a series containing log-likelihood contributions for each observation.
In contrast, the new EViews 8 optimize command provides tools that allow you to find the optimal parameters or control values of a user-defined function. Notably, optimize supports quite general functions so that the computations and the user-defined objective need not be series-based.
EViews 8 New Econometrics and Statistics: Testing and Diagnostics
EViews 8 features a number of additions and improvements to its extensive set of diagnostics and tests.
Multiple Breakpoint Testing
EViews 8 extends the existing Chow and Quandt-Andrews structural break test tools to allow for multiple breakpoint testing (Bai, 1997; Bai and Perron, 1998, 2003). You may now, for a regression model estimated using linear least squares specified by list, ask EViews to test for multiple unknown breakpoints up to a specified maximum.
EViews offers the following test methods:
- Sequential L+1 breaks vs. L.
- Sequential tests all subsets.
- Global L breaks vs. none.
- L+1 breaks vs. global L.
- Global information criteria.
You may test against "pure" breakpoint specifications in which all of the regressors have regime specific coefficients, or specifications in which only some coefficients vary with the regime.
Panel Serial Correlation Testing
For panel equations estimated by GMM, EViews 8 computes the first and second order serial correlation statistics proposed by Arellano and Bond (1991) as one method of testing for serial correlation. The test is actually two separate statistics, one for first order correlation and one for second. If the innovations are i.i.d. we expect the first order statistic to be significant (with a negative auto-correlation coefficient), and the second order statistic to be insignificant.
Panel Causality Tests
EViews 8 extends the existing Granger Causality tests to perform panel data specific testing.
EViews 8 supports two of the simplest approaches to causality testing in panels. The first is to treat the panel data as one large stacked set of data, and then perform the Granger Causality test in the standard way, with the exception of not letting data from one cross-section enter the lagged values of data from the next cross-section. The second approach, the Dumitrescu-Hurlin (2012) approach, makes an extreme opposite assumption; it allows all coefficients to be different across cross-sections.
HAC Covariance in GLM Models
EViews 8 now offers heteroskedasticity and autocorrelation consistent (HAC) covariance computation in equations estimated by GLM.
Robust Wald F-statistics
EViews 8 now reports the robust Wald test of the null hypothesis that all non-intercept coefficients are zero in cases where you specify a robust coefficient covariance method.
Previously, EViews only reported the residual based F-statistic for testing the null hypothesis. This F-statistic statistic depends only on the coefficient point estimates, and not their standard errors, and was valid only under the maintained hypotheses of no heteroskedasticity or serial correlation. For ordinary least squares without conventionally estimated standard errors, this statistic is numerically identical to the Wald statistic for the hypothesis that all non-intercept coefficients are equal to zero. However, the numerical equivalence between the two test statistics breaks down if robust standard errors are employed.
EViews 8 New Modeling Features
EViews 8 has improved tools for manipulating models.
Improved Model Data Editing
EViews 8 offers improved tools for managing variables in a model. You may use the new Edit override feature, to quickly exclude, override and edit endogenous and exogenous variables for the current scenario.
Comparing Solution Data
EViews 8 offers new tools for quickly viewing differences between the solution values for different scenarios.
and to produce a comparison table:
New Model Commands
EViews 8 introduces a number of new model commands that allow manipulation of the model in a program. You can easily drop, add or edit equations in your model, or replace variables using smart replacement tools.
Programming in EViews 8 has been improved in a number of important ways.
User-defined objects are an exciting new feature in EViews 8. A EViews user object allows you to create your own object types inside of EViews. A user object may be as simple as a storage container for other EViews objects, or it may be a sophisticated new estimation object defined by multiple EViews programs, with views containing post-estimation tests and results, and procedures producing output from the estimation results. Once defined, a user object is almost indistinguishable from a built-in EViews object.
Defining a user object is quite easy-simply specify the types of data and objects stored inside your object, and if desired, define a set of views and procedures that be accessed via commands, menus and dialogs.
Even if you do not go to the trouble of creating your own objects, you may take advantage of this powerful tool by using user objects downloaded from the IHS EViews website or obtained from third-parties.
Program Editor Enhancements
EViews 8 features two important enhancements to the program editor; program line numbers,
and the ability to selectively run parts of a program.
EViews programs now allow you to view line numbers in the program.
You may right click anywhere in your program and select Go To Line... to jump directly to a specific line number.
You may choose to only run part of your program by highlight the lines you wish to run, then right-clicking and selecting Run Selected. EViews will then execute only the selected line of code as a new program.
As with every release of EViews, EViews 8 comes with a host of new functions.
EViews 8 Feature List
EViews 8 offers a extensive array of powerful features for data handling, statistics and econometric analysis, forecasting and simulation, data presentation, and programming. While we can't possible list everything, the following list offers a glimpse at the important EViews features:
Basic Data Handling
- Numeric, alphanumeric (string), and date series; value labels.
- Extensive library of operators and statistical, mathematical, date and string functions.
- Powerful language for expression handling and transforming existing data using operators and functions.
- Samples and sample objects facilitate processing on subsets of data.
- Support for complex data structures including regular dated data, irregular dated data, cross-section data with observation identifiers, dated, and undated panel data.
- Multi-page workfiles.
- EViews native, disk-based databases provide powerful query features and integration with EViews workfiles.
- Convert data between EViews and various spreadsheet, statistical, and database formats, including (but not limited to): Microsoft Access® and Excel® files (including .XSLX and .XLSM), Gauss
Dataset files, SAS® Transport files, SPSS native and portable files, Stata
files, raw formatted ASCII text or binary files, HTML, or ODBC databases
and queries (ODBC support is provided only in the Enterprise Edition).
- OLE support for linking EViews output to other packages, including Microsoft Excel®, Word® and Powerpoint®.
- OLEDB support for reading EViews workfiles and databases using OLEDB-aware clients or custom programs.
- Support for FRED® (Federal Reserve Economic Data) databases. Enterprise Edition support for Global
Insight DRIPro and DRIBase, Haver Analytics® DLX®, FAME, EcoWin,
Datastream, FactSet, and Moody's Economy.com databases.
- The EViews Microsoft Excel® Add-in allows you to link or import data from EViews workfiles and databases from within Excel.
- Drag-and-drop support for reading data; simply drop files into EViews for automatic conversion of foreign data into EViews workfile format.
- Powerful tools for creating new workfile pages from values and dates in existing series.
- Match merge, join, append, subset, resize, sort, and reshape (stack and unstack) workfiles.
- Easy-to-use automatic frequency conversion when copying or linking data between pages of different frequency.
- Frequency conversion and match merging support dynamic updating whenever underlying data change.
- Auto-updating formula series that are automatically recalculated whenever underlying data change.
- Easy-to-use frequency conversion, simply copy or link data between pages of different frequency.
- Tools for resampling and random number generation for simulation. Random number generation for 18 different distribution functions using three different random number generators.
Time Series Data Handling
- Integrated support for handling dates and time series data (both regular and irregular).
- Support for common regular frequency data (Annual,
Semi-annual, Quarterly, Monthly,
Bimonthly, Fortnight, Ten-day,
Weekly, Daily - 5 day week, Daily
- 7 day week).
- Support for high-frequency (intraday) data, allowing for hours, minutes, and seconds frequencies. In addition, there are a number of less commonly encountered regular frequencies, including Multi-year, Bimonthly, Fortnight, Ten-Day, and Daily with an arbitrary range of days of the week.
- Specialized time series functions and operators: lags, differences, log-differences, moving averages, etc.
- Frequency conversion: various high-to-low and low-to-high.
- Exponential smoothing: single, double, Holt-Winters, and ETS smoothing.
- Built-in tools for whitening regression.
- Hodrick-Prescott filtering.
- Band-pass (frequency) filtering: Baxter-King, Christiano-Fitzgerald fixed length and full sample asymmetric filters.
- Seasonal adjustment: Census X-13, X-12-ARIMA, Tramo/Seats, moving average.
- Interpolation to fill in missing values within a series: Linear, Log-Linear, Catmull-Rom Spline, Cardinal Spline.
- Basic data summaries; by-group summaries.
- Tests of equality: t-tests, ANOVA (balanced and unbalanced, with or without heteroskedastic variances.), Wilcoxon, Mann-Whitney, Median Chi-square, Kruskal-Wallis, van der Waerden, F-test, Siegel-Tukey, Bartlett, Levene, Brown-Forsythe.
- One-way tabulation; cross-tabulation with measures of association (Phi Coefficient, Cramer's V, Contingency Coefficient) and independence testing (Pearson Chi-Square, Likelihood Ratio G^2).
- Covariance and correlation analysis including Pearson, Spearman rank-order, Kendall's tau-a and tau-b and partial analysis.
- Principal components analysis including scree plots, biplots and loading plots, and weighted component score calculations.
- Factor analysis allowing computation of measures of association (including covariance and correlation), uniqueness estimates, factor loading estimates and factor scores, as well as performing estimation diagnostics and factor rotation using one of over 30 different orthogonal and oblique methods.
- Empirical Distribution Function (EDF) Tests for the Normal, Exponential, Extreme value, Logistic, Chi-square, Weibull, or Gamma distributions (Kolmogorov-Smirnov, Lilliefors, Cramer-von Mises, Anderson-Darling, Watson).
- Histograms, Frequency Polygons, Edge Frequency Polygons, Average Shifted Histograms, CDF-survivor-quantile, Quantile-Quantile, kernel density, fitted theoretical distributions, boxplots.
- Scatterplots with parametric and non-parametric regression lines (LOWESS, local polynomial), kernel regression (Nadaraya-Watson, local linear, local polynomial)., or confidence ellipses.
- Autocorrelation, partial autocorrelation, cross-correlation, Q-statistics.
- Granger causality tests, including panel Granger causality.
- Unit root tests: Augmented Dickey-Fuller, GLS transformed Dickey-Fuller, Phillips-Perron, KPSS, Eliot-Richardson-Stock Point Optimal, Ng-Perron.
- Cointegration tests: Johansen, Engle-Granger, Phillips-Ouliaris, Park added variables, and Hansen stability.
- Independence tests: Brock, Dechert, Scheinkman and LeBaron
- Variance ratio tests: Lo and MacKinlay, Kim wild bootstrap, Wright's rank, rank-score and sign-tests. Wald and multiple comparison variance ratio tests (Richardson and Smith, Chow and Denning).
- Long-run variance and covariance calculation: symmetric or or one-sided long-run covariances using nonparametric kernel (Newey-West 1987, Andrews 1991), parametric VARHAC (Den Haan and Levin 1997), and prewhitened kernel (Andrews and Monahan 1992) methods. In addition, EViews supports Andrews (1991) and Newey-West (1994) automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.
Panel and Pool
- By-group and by-period statistics and testing.
- Unit root tests: Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, Hadri.
- Cointegration tests: Pedroni, Kao, Maddala and Wu.
- Linear and nonlinear ordinary least squares (multiple regression).
- Linear regression with PDLs on any number of independent variables.
- Robust regression.
- Analytic derivatives for nonlinear estimation.
- Weighted least squares.
- White and Newey-West robust standard errors. HAC standard errors may be computed using nonparametric kernel, parametric VARHAC, and prewhitened kernel methods, and allow for
Andrews and Newey-West automatic bandwidth selection
methods for kernel estimators, and information criteria based lag
length selection methods for VARHAC and prewhitening estimation.
- Linear quantile regression and least absolute deviations (LAD), including both Huber's Sandwich and bootstrapping covariance calculations.
- Stepwise regression with 7 different selection procedures.
ARMA and ARMAX
- Linear models with autoregressive moving average, seasonal autoregressive, and seasonal moving average errors.
- Nonlinear models with AR and SAR specifications.
- Estimation using the backcasting method of Box and Jenkins, or by conditional least squares.
Instrumental Variables and GMM
- Linear and nonlinear two-stage least squares/instrumental variables (2SLS/IV) and Generalized Method of Moments (GMM) estimation.
- Linear and nonlinear 2SLS/IV estimation with AR and SAR errors.
- Limited Information Maximum Likelihood (LIML) and K-class estimation.
- Wide range of GMM weighting matrix specifications (White, HAC, User-provided) with control over weight matrix iteration.
- GMM estimation options include continuously updating estimation (CUE), and a host of new
standard error options, including Windmeijer standard errors.
- IV/GMM specific diagnostics include Instrument Orthogonality Test, a
Regressor Endogeneity Test, a Weak Instrument Test, and a GMM specific
- GARCH(p,q), EGARCH, TARCH, Component GARCH, Power ARCH, Integrated GARCH.
- The linear or nonlinear mean equation may include ARCH and ARMA terms; both the mean and variance equations allow for exogenous variables.
- Normal, Student's t, and Generalized Error Distributions.
- Bollerslev-Wooldridge robust standard errors.
- In- and out-of sample forecasts of the conditional variance and mean, and permanent components.
Limited Dependent Variable Models
- Binary Logit, Probit, and Gompit (Extreme Value).
- Ordered Logit, Probit, and Gompit (Extreme Value).
- Censored and truncated models with normal, logistic, and extreme value errors (Tobit, etc.).
- Count models with Poisson, negative binomial, and quasi-maximum likelihood (QML) specifications.
- Heckman Selection models.
- Huber/White robust standard errors.
- Count models support generalized linear model or QML standard errors.
- Hosmer-Lemeshow and Andrews Goodness-of-Fit testing for binary models.
- Easily save results (including generalized residuals and gradients) to new EViews objects for further analysis.
- General GLM estimation engine may be used to estimate several of these models.
Panel Data/Pooled Time Series, Cross-Sectional Data
- Linear and nonlinear estimation with additive cross-section and period fixed or random effects.
- Choice of quadratic unbiased estimators (QUEs) for component variances in random effects models: Swamy-Arora, Wallace-Hussain, Wansbeek-Kapteyn.
- 2SLS/IV estimation with cross-section and period fixed or random effects.
- Estimation with AR errors using nonlinear least squares on a transformed specification
- Generalized least squares, generalized 2SLS/IV estimation, GMM estimation allowing for cross-section or period heteroskedastic and correlated specifications.
- Linear dynamic panel data estimation using first differences or orthogonal deviations with period-specific predetermined instruments (Arellano-Bond).
- Robust standard error calculations include seven types of robust White and Panel-corrected standard errors (PCSE).
- Testing of coefficient restrictions, omitted and redundant variables, Hausman test for correlated random effects.
- Panel unit root tests: Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher-type tests using ADF and PP tests (Maddala-Wu, Choi), Hadri.
Generalized Linear Models
- Normal, Poisson, Binomial, Negative Binomial, Gamma, Inverse Gaussian, Exponential Mena, Power Mean, Binomial Squared families.
- Identity, log, log-complement, logit, probit, log-log, complimentary log-log, inverse, power, power odds ratio, Box-Cox, Box-Cox odds ratio link functions.
- Prior variance and frequency weighting.
- Fixed, Pearson Chi-Sq, deviance, and user-specified dispersion specifications. Support for QML estimation and testing.
- Quadratic Hill Climbing, Newton-Raphson, IRLS - Fisher Scoring, and BHHH estimation algorithms.
- Ordinary coefficient covariances computed using expected or observed Hessian or the outer product of the gradients. Robust covariance estimates using GLM or Huber/White methods.
Single Equation Cointegrating Regression
- Support for three fully efficient estimation methods, Fully Modified OLS (Phillips and Hansen 1992), Canonical Cointegrating Regression (Park 1992), and Dynamic OLS (Saikkonen 1992, Stock and Watson 1993
- Engle and Granger (1987) and Phillips and Ouliaris (1990) residual-based tests, Hansen's (1992b) instability test, and Park's (1992) added variables test.
- Flexible specification of the trend and deterministic regressors in the equation and cointegrating regressors specification.
- Fully featured estimation of long-run variances for FMOLS and CCR.
- Automatic or fixed lag selection for DOLS lags and leads and for long-run variance whitening regression.
- Rescaled OLS and robust standard error calculations for DOLS.
User-specified Maximum Likelihood
- Use standard EViews series expressions to describe the log likelihood contributions.
- Examples for multinomial and conditional logit, Box-Cox transformation models, disequilibrium switching models, probit models with heteroskedastic errors, nested logit, Heckman sample selection, and Weibull hazard models.
Systems of Equations
- Linear and nonlinear estimation.
- Least squares, 2SLS, equation weighted estimation, Seemingly Unrelated Regression, Three-Stage Least Squares
- GMM with White and HAC weighting matrices.
- AR estimation using nonlinear least squares on a transformed specification.
- Full Information Maximum Likelihood (FIML).
- Estimate structural factorizations in VARs by imposing short- or long-run restrictions.
- Bayesian VARs.
- Impulse response functions in various tabular and graphical formats with standard errors calculated analytically or by Monte Carlo methods.
- Impulse response shocks computed from Cholesky factorization, one-unit or one-standard deviation residuals (ignoring correlations), generalized impulses, structural factorization, or a user-specified vector/matrix form.
- Impose and test linear restrictions on the cointegrating relations and/or adjustment coefficients in VEC models.
- View or generate cointegrating relations from estimated VEC models.
- Extensive diagnostics including: Granger causality tests, joint lag exclusion tests, lag length criteria evaluation, correlograms, autocorrelation, normality and heteroskedasticity testing, cointegration testing, other multivariate diagnostics.
- Conditional Constant Correlation (p,q), Diagonal VECH (p,q), Diagonal BEKK (p,q), with asymmetric terms.
- Extensive parameterization choice for the Diagonal VECH's coefficient matrix.
- Exogenous variables allowed in the mean and variance equations; nonlinear and AR terms allowed in the mean equations.
- Bollerslev-Wooldridge robust standard errors.
- Normal or Student's t multivariate error distribution
- A choice of analytic or (fast or slow) numeric derivatives. (Analytics derivatives not available for some complex models.)
- Generate covariance, variance, or correlation in various tabular and graphical formats from estimated ARCH models.
- Kalman filter algorithm for estimating user-specified single- and multiequation structural models.
- Exogenous variables in the state equation and fully parameterized variance specifications.
- Generate one-step ahead, filtered, or smoothed signals, states, and errors.
- Examples include time-varying parameter, multivariate ARMA, and quasilikelihood stochastic volatility models.
Testing and Evaluation
Forecasting and Simulation
- In- or out-of-sample static or dynamic forecasting from estimated equation objects with calculation of the standard error of the forecast.
- Forecast graphs and in-sample forecast evaluation: RMSE, MAE, MAPE, Theil Inequality Coefficient and proportions
- State-of-the-art model building tools for multiple equation forecasting and multivariate simulation.
- Model equations may be entered in text or as links for automatic updating on re-estimation.
- Display dependency structure or endogenous and exogenous variables of your equations.
- Gauss-Seidel, Broyden and Newton model solvers for non-stochastic and stochastic simulation. Non-stochastic forward solution solve for model consistent expectations. Stochasitc simulation can use bootstrapped residuals.
- Solve control problems so that endogenous variable achieves a user-specified target.
- Sophisticated equation normalization, add factor and override support.
- Manage and compare multiple solution scenarios involving various sets of assumptions.
- Built-in model views and procedures display simulation results in graphical or tabular form.
Graphs and Tables
- Line, dot plot, area, bar, spike, seasonal, pie, xy-line, scatterplots, boxplots, error bar, high-low-open-close, and area band.
- Powerful, easy-to-use categorical and summary graphs.
- Auto-updating graphs which update as underlying data change.
- Observation info and value display when you hover the cursor over a point in the graph.
- Histograms, average shifted historgrams, frequency polyons, edge frequency polygons, boxplots, kernel density, fitted theoretical distributions, boxplots, CDF, survivor, quantile, quantile-quantile.
- Scatterplots with any combination parametric and nonparametric kernel (Nadaraya-Watson, local linear, local polynomial) and nearest neighbor (LOWESS) regression lines, or confidence ellipses.
- Interactive point-and-click or command-based customization.
- Extensive customization of graph background, frame, legends, axes, scaling, lines, symbols, text, shading, fading, with improved graph template features.
- Table customization with control over cell font face, size, and color, cell background color and borders, merging, and annotation.
- Copy-and-paste graphs into other Windows applications, or save graphs as Windows regular or enhanced metafiles, encapsulated PostScript files, bitmaps, GIFs, PNGs or JPGs.
- Copy-and-paste tables to another application or save to an RTF, HTML, or text file.
- Manage graphs and tables together in a spool object that lets you display multiple results and analyses in one object
Commands and Programming
- Object-oriented command language provides access to menu items
- Batch execution of commands in program files.
- Looping and condition branching, subroutine, and macro processing.
- String and string vector objects for string processing. Extensive library of string and string list functions.
- Extensive matrix support: matrix manipulation, multiplication, inversion, Kronecker products, eigenvalue solution, and singular value decomposition.
External Interface and Add-Ins
- EViews COM automation server support so that external programs or scripts can launch or control EViews, transfer data, and execute EViews commands.
- EViews offers COM Automation client support application for MATLAB® and R servers so that EViews may be used to launch or control the application, transfer data, or execute commands.
- The EViews Microsoft Excel® Add-in offers a simple interface for fetching and linking from within Microsoft Excel® (2000 and later) to series and matrix objects stored in EViews workfiles and databases.
- The EViews Add-ins infrastructure offers seamless access to user-defined
programs using the standard EViews command, menu, and object
- Download and install predefined Add-ins from the EViews website.
Maximum observations per series (32bit version)
4 million (by default), may be increased up to 15 million, if
desired, subject to memory restrictions.
Maximum observations per series (64bit version)
Total observations: (series x obs per series)
limited only by available RAM.
Maximum objects per workfile
limited only by available RAM.
Maximum objects per database
limited to 10 million objects, 64 gigabytes or
available disk space.
7 Student Version*
Maximum observations per series
4 million (by default), may be increased if
desired, subject to memory restrictions (saving and exporting are
only available for dataset with fewer than 1,500 observations per series*).
Total observations: (series x obs per series)
limited only by available RAM
(saving and exporting are only available for dataset with fewer than 15,000 total
Maximum objects per workfile
limited only by available RAM
(saving and exporting are only available for workfile with fewer than 60 objects*).
Maximum objects per database
save/store data into database, but can fetch/read from one.
*Note: The Student
Version places "soft" capacity restrictions on the amount of
data (1,500 observations per series, 15,000 total observations, 60
objects) that may be saved or exported. Students may, without
restriction, work with larger amounts of data, but workfiles that
exceed the soft limits may not be saved nor the data exported.
EViews 7 and 8 System Requirements
Pentium or better
Windows 2000, Windows 2003, Windows XP (32bit or 64bit*), Windows Vista (32bit or 64bit*), Windows Server 2008 (32bit or 64bit*), Windows 7 (32bit or 64bit*), Windows 8 (32bit or 64bit), Windows Server 2008 (32bit or 64bit*).
32 MB for Windows 98, Me or NT
64 MB for Windows 2000, 2003
256 MB Windows XP
512 MB Windows Vista, Windows 7 or Windows 8
270 MB of available hard disk space for the EViews executable, supporting files, full documentation, and example files.
|| *Note although EViews 7 and 8 will run on 64bit operating systems, only EViews 8 comes in a 64bit version. It should also be noted that the Excel add-in that comes with EViews 7 has a 32bit version only, and thus will not run under a 64bit version of Excel.