EViews 9.5
La versión 9.5
ofrece a investigadores, estudiantes, agencias gubernamentales y corporraciones
una potente herramienta estadística ,asi como innovadoras herramientas de
modelado y predicciones, junto con un interfaz de usuario muy intuitivo y fácil
de usar.
La versión 9.5 es un
upgrade gratuito para la mayoría de los usuarios de la version 9
Eviews ofrece la mas
moderna tecnología de desarrollo de software en esta versión, y como resultado
proporciona un intrfaz flexible , y un software potente orientado a objetos.
NOVEDADES VERSION 9.5
EViews 9.5 for Windows
EViews 9.5 offers academic researchers, corporations, government agencies,
and students access to powerful statistical, forecasting, and modeling tools
through an innovative, easytouse interface.
EViews blends the best of modern software technology with cutting edge
features. The result is a stateofthe art program that offers unprecedented
power within a flexible, objectoriented interface.
Explore the world of EViews and discover why it's the worldwide leader in
Windowsbased econometric software and the choice of those who demand the very
best
MixedData Sampling (MIDAS) is a method of estimating and
forecasting from models where the dependent variable is recorded at a lower
frequency than one or more of the independent variables. Traditional approaches
to dealing with the issue of mixed frequencies is to simply aggregate the higher
frequency data into the lowest frequency. A significant disadvantage to this
approach is that through the aggregation you discard data which can lead to less
accurate estimation.
EViews workfiles natively support easy organization of mixed frequency data,
and allow easy conversion from one frequency to another. EViews’ MIDAS
implementation makes use of this easy handling of mixed frequencies to allow
easy specification of MIDAS models.
EViews allows 4 different MIDAS weighting schemes:
EViews also offers automatic lag selection methods for determining the number
of lags/periods of the higher frequency variables.
We have a complete stepbystep demonstration
of MIDAS using a paper by the Federal Reserve Bank of St Louis..
DieboldMariano Test
The Forecast Evaluation Series View has been extended with the addition of
the DieboldMariano test as part of the output whenever two forecasts are being
evaluated.
The DieboldMariano test allows for statistical comparison of the accuracy of
two competing forecasts of the same data.
FIML with Variance Restrictions
The system FIML estimator now has an option specifying the form of the
residual covariance matrix used in estimation. You may choose between:

unrestricted

diagonal

userspecified
If you choose the userspecified, you must also provide the name of a Sym
object containing values for all of the residual variances and covariances.
Following estimation, EViews offers you the ability to examine the covariance
matrix used in estimation.
Model Interface Enhancements
5 new enhancements to the Model object’s interface:

Print View – Produces a text representation of the
Model, similar to the existing Text View. However the Print View allows you
to display equations in broken form without having to break the link to the
underlying equation object, and allows you to specify output features such
as display decimal places and whether to use variable display names or not.

Scenario Descriptions – The Scenario Dialog has an
additional page that lets you enter a text description for each scenario.
You may also choose to export that description to any series created during
a solve of the Model under that scenario.

Scenario View – Displays a table containing each
Scenario, their alias, overrides, excludes, and description.

Lock Protection – The proc menu has a new item
allowing password protection of the model. Once protected, the Text View of
a model is disabled, the Equation and Variable Views are uneditable,
existing scenarios cannot be modified or deleted, and other minor features
are disabled.

Equation Finding – The Equation View has a new Find
button. The Find lets you search for equation by name, endogenous variable
or exogenous variable. Once found, EViews will select the equation for you.
Group Members View
This view displays a list of all the names of the series currently in the
group.
You may change the group by dragging and dropping series objects from the
workfile to the Group Members window. Also, rearranging members
may be accomplished by dragging and dropping members in the desired position.
Changes that you make to the group are finalized immediately. You may sort the
members by number or by name by clicking on the headers (# or Name) of the list
window.
Additionally, you may change the group by rightclicking on the window and
then make a selection in the popup dialog. Select Edit Member, to edit the
contents of this window to add, remove, or rearrange the series in a group.
Group Object Preview
Object preview allows the user to quickly look through a number of objects.
In the case of group preview, instead of opening the object and going through
its different views (graph, spreadsheet, group members), you may use the preview
to quickly view metadata (name, type, description, etc.) and object typespecific
information (for example, members list and a graph of 5 series members).
This replaces previous group preview where the user could see only the group
members with no graph.
The group preview lists all the members of a group under the Group Members,
but displays only the line graphs of 5 group members at a time. The members that
are displayed have the colored line symbol before their name. If you click on a
group member within the list, that will change the selection. The object that
you click will be the first of the 5 objects you will be previewing.
Programming Support
Two new commands added to EViews Programming Language:
CARACTERISTICAS VERSION 9 Y 9.5
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.
INICIO