Introduzione
in Italiano
Veloce, potente e flessibile più che mai!
EViews
è il software leader di mercato per la stima e la simulazione
di modelli econometrici.
Grazie all'interfaccia objectoriented, 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.
Versioni
disponibili
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
FAME4.
A combination of power and easeofuse make EViews 9 the ideal package for anyone working with time series, crosssection, 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 objectoriented userinterface and a sophisticated analysis engine, EViews blends the best of modern software technology with the features you’ve always wanted. The result is a stateofthe art program that offers unprecedented power within a flexible, easytouse interface.
Find out for yourself why EViews is the worldwide leader in Windowsbased econometric software and the choice of those who demand the very best.
Part 1: An Intuitive, EasytoUse Interface
EViews sets the standard for econometric software by incorporating modern windowing and objectbased
techniques. The result is powerful software with an intuitive, easytouse user interface. 

An ObjectBased 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,
actualfittedresidual 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 seriesspecific 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 sidebyside comparisons of series plots,
hypothesis tests, equation estimates, or model forecasts developed under alternative
assumptions. 

EViews offers true
multiwindow support. 


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 objectbased 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 respecified
or reestimated. 

Modern linking
technology offers dynamic updating of data. 


Windows Integration
Couple all of this with strong Windows integration,
including draganddrop file import for over twenty popular file formats and
copyandpaste 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
using draganddrop. 

AddIns and User Objects
EViews offers an easytouse EViews Addins infrastructure that provides seamless access to userdefined programs using the standard EViews command,
menu, and object interface.
Addins offer you a exciting new way of running EViews programs. You may readily define
Addins that augment the EViews language with userdefined commands, specify new menu
entries for pointandclick program interaction, and display program output in standard
EViews object windows.
User objects further extend EViews by allowing the creation of userdefined 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 Addins which add
functionality to EViews. A single click is all that you need to download and install any of the
Addins currently available on the EViews website, with the promise of more to come. 

Addins offer seamless access to userdefined programs. 


Part 2: Powerful Analytical Tools
In contrast with other econometric software, there is no reason for most users to learn a complicated command language. EViews' builtin procedures are a mouseclick 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
crosssection 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 oneway 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. QQplots (quantilequantile 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 KolmogorovSmirnov, Liliefors, Cramer
von Mises, and AndersonDarling tests to see whether your series is distributed normally,
or whether it comes from another distribution such as an exponential, extreme value,
logistic, chisquare, 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. 


Examine the
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 to Qstatistics to unit root tests.
EViews provides autocorrelation and partial autocorrelation
functions, Qstatistics, and crosscorrelation functions, as well as unit root tests (ADF,
PhillipsPerron, KPSS, DFGLS, ERS, or NgPerron for single time series and
LevinLinChu, Breitung, ImPesaranShin, Fisher, or Hadri for panel data), cointegration
tests (Johansen with MacKinnonHaugMichelis critical values and pvalues for ordinary
data, and Pedroni, Kao, or Fisher for panel data), causality, and independence tests.
EViews also provides easytouse frontend
support for the U.S. Census Bureau's X13 Seasonal Adjustment programs, which is an updated version of both X12 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 HodrickPrescott, BaxterKing,
ChristianoFitzgerald fixed length and ChristianoFitzgerald asymmetric full sample
bandpass (frequency) filters. 

Explore the time
series properties of your data. 


EViews provides
easytouse 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 seriescross
section data. Define panel structures with virtually no limit on the number of
crosssections 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 bygroup and byperiod
statistics, tests, and graphs, to sophisticated panel unit root (LevinLinChu,
Breitung, ImPesaranShin, or Fisher) and cointegration diagnostics (Pedroni
(2004), Pedroni (1999), and Kao, or the Fishertype 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
crosssections, 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, twostage 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
crosssection and time series data (single and multiple equation). Weighting options
include the White covariance matrix for crosssection 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 NeweyWest bandwith selection methods.
You can estimate a GMM equation using either iterative procedures, or a continuously updating procedure. Postestimation diagnostics for GMM equations,
including weak instrument statistics, are also available.
ARCH
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
exogenous variables.
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 quasimaximum likelihood (QML) specifications. EViews
optionally reports generalized linear model or QML standard errors.
Panel and Pooled Time SeriesCross Section
EViews offers various panel and pooled data estimation
methods. In addition to ordinary linear and nonlinear leastsquares, equation estimation
methods include 2SLS/IV and Generalized 2SLS/IV, and GMM, which can be used to estimate
complex dynamic panel data specifications (including AndersonHsiao and ArellanoBond
types of estimators).
Most of the methods allow for both time and crosssection
fixed and random effects specifications. For random effects models, quadratic unbiased
estimators of component variances include SwamyArora, WallaceHussain and
WansbeekKapteyn.
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 crosssections. 


EViews
offers a full range of single equation estimators. 


GMM
estimation offers a variety of weighting matrix and covariance options. 


Easytouse
dialogs make it easy to specify your ARCH model. 


EViews
estimates both ML and QML count models. 


EViews
offers a range of panel data estimators and options. 


An
(optional) wizard leads you through the specification of your dynamic panel data
model. 


System Estimation
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, twostage least squares,
seemingly unrelated regression, threestage 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 shortrun (Sims 1986) or longrun
(Blanchard and Quah 1989) restrictions. Overidentifying 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
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. BollerslevWooldridge robust standard errors are also available.
Once the model is estimated, users can easily generate the insample variance,
covariance, or correlation, in tabular or graphic format.
StateSpace Models
The statespace object allows estimation of a wide variety
of single and multiequation dynamic timeseries models using the Kalman Filter
algorithm. Among other things, you can use the statespace object to estimate random and
timevarying 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 onestep ahead,
filtered, or smoothed signals, states, or errors. EViews' builtin forecasting procedures
also provide easytouse tools for in and outofsample forecasting using nstep 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. 


UserSpecified Maximum Likelihood
For custom analysis, EViews' easytouse likelihood object
permits estimation of userspecified 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, bygroup 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 MersenneTwister), 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,
confidence interval. 

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. Builtin 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. 


Data
Structures and Types
EViews can handle complex data structures, including regular and irregular dated data, crosssection 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 draganddrop your foreign file onto
EViews and your data will automatically appear in an EViews workfile. Or use the
easytouse dialogs and wizards to cutomize the importing of your data. 

EViews reads and
writes an extensive list of data formats. 


EViews Databases
EViews provides sophisticated
builtin 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. 

Builtin database
tools offer powerful query features. 


Easily move data from
databases to your EViews workfiles. 


Enterprise
Edition Support for ODBC, FAME^{TM}, 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 FAME^{TM}
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, easytouse 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. 


EViews Enterprise
Edition offers direct access to additional commercial vendor data formats. 


Frequency Conversion
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 irregularfrequency 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
accessed.
You can even create links so that the frequency converted
data series are automatically recalculated whenever the underlying data is modified. 

Specify a
seriesspecific automatic conversion or select a specific method. 


Part 4: Presentation Quality Output
EViews 9 supports a wide range of basic graph types including line graphs, bar graphs, filled area graphs, pie charts, scatter diagrams, mixed linebar graphs, highlow graphs, scatterplots, and boxplots. Any number of graphs can be combined in a single graph for presentation.
EViews 9 supports a wide range of graph types
and features. 
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 copyandpaste 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. 
Extensive table
customization tools allow you to produce presentation quality tables for inclusion in
other programs. An easytouse, 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
customization tools. 
When completed,
you can copyandpaste your customized table to another application or save it as an RTF,
HTML, PDF, or text file. 
Part 5: Traditional Command Line and Programming
Pointandclick is great, but what if you feel more
comfortable entering commands? And what if you need programming capabilities? In addition
to its stateoftheart 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 objectoriented 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. 
Matrix
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. 
EViews 9 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 possibly 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, crosssection data with observation identifiers, dated, and undated panel data.
 Multipage workfiles.
 EViews native, diskbased 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, including tables and graphs, to other packages, including Microsoft Excel®, Word® and Powerpoint®.
 OLEDB support for reading EViews workfiles and databases using OLEDBaware 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, Bloomberg, EIA, CEIC,
Datastream, FactSet, and Moody’s Economy.com databases.
 The EViews Microsoft Excel® Addin allows you to link or import data from EViews workfiles and databases from within Excel.
 Draganddrop support for reading data; simply drop files into EViews for automatic conversion and linking 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.
 Easytouse 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.
 Autoupdating formula series that are automatically recalculated whenever underlying data change.
 Easytouse 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.
 Support for cloud drive access, allowing you to open and save file directly to Dropbox, OneDrive, Google Drive and Box accounts.
Time Series Data Handling
 Integrated support for handling dates and time series data (both regular and irregular).
 Support for common regular frequency data (Annual,
Semiannual, Quarterly, Monthly,
Bimonthly, Fortnight, Tenday,
Weekly, Daily  5 day week, Daily
 7 day week).
 Support for highfrequency (intraday) data, allowing for hours, minutes, and seconds frequencies. In addition, there are a number of less commonly encountered regular frequencies, including Multiyear, Bimonthly, Fortnight, TenDay, and Daily with an arbitrary range of days of the week.
 Specialized time series functions and operators: lags, differences, logdifferences, moving averages, etc.
 Frequency conversion: various hightolow and lowtohigh methods.
 Exponential smoothing: single, double, HoltWinters, and ETS smoothing.
 Builtin tools for whitening regression.
 HodrickPrescott filtering.
 Bandpass (frequency) filtering: BaxterKing, ChristianoFitzgerald fixed length and full sample asymmetric filters.
 Seasonal adjustment: Census X13, X12ARIMA, Tramo/Seats, moving average.
 Interpolation to fill in missing values within a series: Linear, LogLinear, CatmullRom Spline, Cardinal Spline.
Statistics
Basic
 Basic data summaries; bygroup summaries.
 Tests of equality: ttests, ANOVA (balanced and unbalanced, with or without heteroskedastic variances.), Wilcoxon, MannWhitney, Median Chisquare, KruskalWallis, van der Waerden, Ftest, SiegelTukey, Bartlett, Levene, BrownForsythe.
 Oneway tabulation; crosstabulation with measures of association (Phi Coefficient, Cramer’s V, Contingency Coefficient) and independence testing (Pearson ChiSquare, Likelihood Ratio G^2).
 Covariance and correlation analysis including Pearson, Spearman rankorder, Kendall’s taua and taub 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, Chisquare, Weibull, or Gamma distributions (KolmogorovSmirnov, Lilliefors, Cramervon Mises, AndersonDarling, Watson).
 Histograms, Frequency Polygons, Edge Frequency Polygons, Average Shifted Histograms, CDFsurvivorquantile, QuantileQuantile, kernel density, fitted theoretical distributions, boxplots.
 Scatterplots with parametric and nonparametric regression lines (LOWESS, local polynomial), kernel regression (NadarayaWatson, local linear, local polynomial)., or confidence ellipses.
Time Series
 Autocorrelation, partial autocorrelation, crosscorrelation, Qstatistics.
 Granger causality tests, including panel Granger causality.
 Unit root tests: Augmented DickeyFuller, GLS transformed DickeyFuller, PhillipsPerron, KPSS, EliotRichardsonStock Point Optimal, NgPerron, as well as tests for unit roots with breakpoints.
 Cointegration tests: Johansen, EngleGranger, PhillipsOuliaris, 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, rankscore and signtests. Wald and multiple comparison variance ratio tests (Richardson and Smith, Chow and Denning).
 Longrun variance and covariance calculation: symmetric or or onesided longrun covariances using nonparametric kernel (NeweyWest 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 NeweyWest (1994) automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.
Panel and Pool
 Bygroup and byperiod statistics and testing.
 Unit root tests: LevinLinChu, Breitung, ImPesaranShin, Fisher, Hadri.
 Cointegration tests: Pedroni, Kao, Maddala and Wu.
 Panel within series covariances and principal components.
 DumitrescuHurlin (2012) panel causality tests.
 Crosssection dependence tests.
Estimation
Regression
 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 NeweyWest robust standard errors. HAC standard errors may be computed using nonparametric kernel, parametric VARHAC, and prewhitened kernel methods, and allow for
Andrews and NeweyWest 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 seven different selection procedures.
 Threshold regression including TAR and SETAR.
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, conditional least squares, ML or GLS.
 Fractionally integrated ARFIMA models.
Instrumental Variables and GMM
 Linear and nonlinear twostage 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 Kclass estimation.
 Wide range of GMM weighting matrix specifications (White, HAC, Userprovided) 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
breakpoint test.
ARCH/GARCH
 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.
 BollerslevWooldridge robust standard errors.
 In and outof 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 quasimaximum likelihood (QML) specifications.
 Heckman Selection models.
 Huber/White robust standard errors.
 Count models support generalized linear model or QML standard errors.
 HosmerLemeshow and Andrews GoodnessofFit 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, with the option to include robust covariances.
Panel Data/Pooled Time Series, CrossSectional Data
 Linear and nonlinear estimation with additive crosssection and period fixed or random effects.
 Choice of quadratic unbiased estimators (QUEs) for component variances in random effects models: SwamyArora, WallaceHussain, WansbeekKapteyn.
 2SLS/IV estimation with crosssection 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 crosssection or period heteroskedastic and correlated specifications.
 Linear dynamic panel data estimation using first differences or orthogonal deviations with periodspecific predetermined instruments (ArellanoBond).
 Panel serial correlation tests (ArellanoBond).
 Robust standard error calculations include seven types of robust White and Panelcorrected standard errors (PCSE).
 Testing of coefficient restrictions, omitted and redundant variables, Hausman test for correlated random effects.
 Panel unit root tests: LevinLinChu, Breitung, ImPesaranShin, Fishertype tests using ADF and PP tests (MaddalaWu, Choi), Hadri.
 Panel cointegration estimation: Fully Modified OLS (FMOLS, Pedroni 2000) or Dynamic Ordinary Least Squares (DOLS, Kao and Chaing 2000, Mark and Sul 2003).
 Pooled Mean Group (PMG) estimation.
Generalized Linear Models
 Normal, Poisson, Binomial, Negative Binomial, Gamma, Inverse Gaussian, Exponential Mena, Power Mean, Binomial Squared families.
 Identity, log, logcomplement, logit, probit, loglog, complimentary loglog, inverse, power, power odds ratio, BoxCox, BoxCox odds ratio link functions.
 Prior variance and frequency weighting.
 Fixed, Pearson ChiSq, deviance, and userspecified dispersion specifications. Support for QML estimation and testing.
 Quadratic Hill Climbing, NewtonRaphson, 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, HAC, 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) residualbased 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 longrun variances for FMOLS and CCR.
 Automatic or fixed lag selection for DOLS lags and leads and for longrun variance whitening regression.
 Rescaled OLS and robust standard error calculations for DOLS.
Userspecified Maximum Likelihood
 Use standard EViews series expressions to describe the log likelihood contributions.
 Examples for multinomial and conditional logit, BoxCox transformation models, disequilibrium switching models, probit models with heteroskedastic errors, nested logit, Heckman sample selection, and Weibull hazard models.
Systems of Equations
Basic
 Linear and nonlinear estimation.
 Least squares, 2SLS, equation weighted estimation, Seemingly Unrelated Regression, and ThreeStage Least Squares.
 GMM with White and HAC weighting matrices.
 AR estimation using nonlinear least squares on a transformed specification.
 Full Information Maximum Likelihood (FIML).
VAR/VEC
 Estimate structural factorizations in VARs by imposing short or longrun 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, oneunit or onestandard deviation residuals (ignoring correlations), generalized impulses, structural factorization, or a userspecified 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.
Multivariate ARCH
 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.
 BollerslevWooldridge 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.
State Space
 Kalman filter algorithm for estimating userspecified single and multiequation structural models.
 Exogenous variables in the state equation and fully parameterized variance specifications.
 Generate onestep ahead, filtered, or smoothed signals, states, and errors.
 Examples include timevarying parameter, multivariate ARMA, and quasilikelihood stochastic volatility models.
Testing and Evaluation
Forecasting and Simulation
 In or outofsample static or dynamic forecasting from estimated equation objects with calculation of the standard error of the forecast.
 Forecast graphs and insample forecast evaluation: RMSE, MAE, MAPE, Theil Inequality Coefficient and proportions
 Stateoftheart model building tools for multiple equation forecasting and multivariate simulation.
 Model equations may be entered in text or as links for automatic updating on reestimation.
 Display dependency structure or endogenous and exogenous variables of your equations.
 GaussSeidel, Broyden and Newton model solvers for nonstochastic and stochastic simulation. Nonstochastic forward solution solve for model consistent expectations. Stochasitc simulation can use bootstrapped residuals.
 Solve control problems so that endogenous variable achieves a userspecified target.
 Sophisticated equation normalization, add factor and override support.
 Manage and compare multiple solution scenarios involving various sets of assumptions.
 Builtin model views and procedures display simulation results in graphical or tabular form.
Graphs and Tables
 Line, dot plot, area, bar, spike, seasonal, pie, xyline, scatterplots, boxplots, error bar, highlowopenclose, and area band.
 Powerful, easytouse categorical and summary graphs.
 Autoupdating 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, quantilequantile.
 Scatterplots with any combination parametric and nonparametric kernel (NadarayaWatson, local linear, local polynomial) and nearest neighbor (LOWESS) regression lines, or confidence ellipses.
 Interactive pointandclick or commandbased 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.
 Copyandpaste graphs into other Windows applications, or save graphs as Windows regular or enhanced metafiles, encapsulated PostScript files, bitmaps, GIFs, PNGs or JPGs.
 Copyandpaste 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
 Objectoriented 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 AddIns
 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® Addin 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 Addins infrastructure offers seamless access to userdefined
programs using the standard EViews command, menu, and object
interface.
 Download and install predefined Addins from the EViews website.
EViews 9 features a wide range of exciting changes and improvements. The following is an overview of the most important new features in EViews 9.
EViews Interface
 Command capture from the interactive interface.
 Dockable command and capture window interface.
 Database and workfile object preview.
Data Handling
 Enhanced import and linking of data.
 A powerful new FRED database interface.
 Direct read and write access to data stored on cloud drive services.
 Dated data table template support for saving and importing customized settings.
 New frequency conversion methods.
Graphs, Tables and Spools
 New mixed graph types.
 Graph pan and zoom.
 Multigraph viewing slideshow.
 Rectangle and ellipse drawing.
 Arrow, rectangle, and ellipse databased anchoring.
 Tables, graphs, and spools may now be saved in LaTeX format.
Econometrics and Statistics
Forecasting
 Automatic ARIMA forecasting of a series.
 Forecast evaluation and combination testing.
 Forecast averaging.
 VAR Forecasting.
Estimation
 Autoregressive Distributed Lag regression (ARDL) with automatic lag selection.
 ML and GLS ARMA estimation.
 ARFIMA estimation.
 Pooled mean group estimation of panel data ARDL models.
 Threshold regression.
 A new optimization engine.
Testing and Diagnostics
 Unit root tests with a structural break.
 Crosssection Dependence Tests.
 Panel Effects Tests.

EViews
9 
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) 
120 million. 
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. 

EViews
8 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 datasets 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 datasets with fewer than 15,000 total
observations*.) 
Maximum objects per workfile 
limited only by available RAM
(saving and exporting are only available for workfiles with fewer than 60 objects*). 
Maximum objects per database 
Cannot
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 Enterprise offers all the features of the Standard Version of EViews 9, but also provides flexibility to directly connect to different data sources. Whether you want to connect to a third party vendor, use ODBC to connect to a relational database, or use EViews’ Database Extension Interface (“EDX”) or EViews’ Database Object (“EDO”) Library to connect to your propriety data sources, EViews Enterprise is the tool for you!
With EViews Enterprise, you will improve your work efficiency by minimizing the steps needed to bring data into your EViews workfile and improve modeling accuracy with the most recent data from your direct connection to your data source. 

With EViews Enterprise and an account with your data provider, you can seamlessly search, query, and retrieve data from thirdparty data sources such as Bloomberg databases, IHS databases, FactSet databases … and many more. 

You can drag and drop from a third party vendor directly into your workfile. 


ODBC Compliant Databases
Enterprise Edition allows direct access to any database with an ODBC driver, providing transparent connection to common relational databases such as Oracle, Microsoft SQL Server, IBM DB2, or Sybase. 

ODBC can connect you to your own private databases. 


The EDX API provides an open programming interface that allows users to develop their own customized connection to any public or proprietary data source providing simple and immediate access to the data within EViews. 

EDX allows you to build your own data browsers for your data. 


The EDO library allows you to work with data stored in EViews file formats from within other applications. EDO makes it simple to pull the finished results of your work directly from your EViews workfile, or to write a simple application to regularly update your EViews database from an external data source. 

Use EViews databases in your own applications with EDO. 

EViews 8 & 9 System Requirements 




CPU: 
Pentium or better 
Operating System: 
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 8.1 (32bit or 64bit), Windows Server 2012 (32bit or 64bit). 
Memory: 
64 MB for Windows 2003
256 MB Windows XP
512 MB Windows Vista, Windows 7 or Windows 8 
Disk Space: 
300 MB of available hard disk space for the EViews executable, supporting files, full documentation, and example files. 
