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
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, easy-to-use interface.
EViews blends the best of modern software technology with cutting edge
features. The result is a state-of-the art program that offers unprecedented
power within a flexible, object-oriented interface.
Explore the world of EViews and discover why it's the worldwide leader in
Windows-based econometric software and the choice of those who demand the very
Mixed-Data 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
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 step-by-step demonstration
of MIDAS using a paper by the Federal Reserve Bank of St Louis..
The Forecast Evaluation Series View has been extended with the addition of
the Diebold-Mariano test as part of the output whenever two forecasts are being
The Diebold-Mariano 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:
If you choose the user-specified, 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 un-editable,
existing scenarios cannot be modified or deleted, and other minor features
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
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
Additionally, you may change the group by right-clicking 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 type-specific
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.
Two new commands added to EViews Programming Language:
CARACTERISTICAS VERSION 9 Y 9.5
Basic Data Handling
- Numeric, alphanumeric (string), and date series; value
- 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, including
tables and graphs, to other packages, including Microsoft Excel®, Word®
- 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, Bloomberg, EIA, CEIC, 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 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.
- 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.
- 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,
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
- 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,
- 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,
- 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,
- Granger causality tests, including panel Granger
- Unit root tests: Augmented Dickey-Fuller, GLS
transformed Dickey-Fuller, Phillips-Perron, KPSS, Eliot-Richardson-Stock
Point Optimal, Ng-Perron, as well as tests for unit roots with breakpoints.
- Cointegration tests: Johansen, Engle-Granger, Phillips-Ouliaris,
Park added variables, and Hansen stability.
- Independence tests: Brock, Dechert, Scheinkman and
- 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,
- Cointegration tests: Pedroni, Kao, Maddala and Wu.
- Panel within series covariances and principal
- Dumitrescu-Hurlin (2012) panel causality tests.
- Cross-section dependence tests.
- Linear and nonlinear ordinary least squares (multiple
- Linear regression with PDLs on any number of
- 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
- Linear quantile regression and least absolute
deviations (LAD), including both Huber’s Sandwich and bootstrapping
- Stepwise regression with seven different selection
- 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 two-stage least squares/instrumental
variables (2SLS/IV) and Generalized Method of Moments (GMM) estimation.
- Linear and nonlinear 2SLS/IV estimation with AR and
- Limited Information Maximum Likelihood (LIML) and K-class
- 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 breakpoint test.
- GARCH(p,q), EGARCH, TARCH, Component GARCH, Power ARCH,
- The linear or nonlinear mean equation may include ARCH
and ARMA terms; both the mean and variance equations allow for exogenous
- Normal, Student’s t, and Generalized Error
- 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
- 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, with the option to include robust covariances.
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,
- 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
- Panel serial correlation tests (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.
- 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, 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, 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
- 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
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
Systems of Equations
- Linear and nonlinear estimation.
- Least squares, 2SLS, equation weighted estimation,
Seemingly Unrelated Regression, and Three-Stage Least Squares.
- GMM with White and HAC weighting matrices.
- AR estimation using nonlinear least squares on a
- 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
- Impulse response shocks computed from Cholesky
factorization, one-unit or one-standard deviation residuals (ignoring
correlations), generalized impulses, structural factorization, or a user-specified
- 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
- 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
- Solve control problems so that endogenous variable
achieves a user-specified target.
- Sophisticated equation normalization, add factor and
- 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
- 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
- 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
- Batch execution of commands in program files.
- Looping and condition branching, subroutine, and macro
- 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 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
- Download and install predefined Add-ins from the