Semplicità, flessibilità e potenza d'analisi: questo è EViews!
EViews è il software leader di mercato per la stima e la simulazione di modelli econometrici e statistici. Grazie all'interfaccia object-oriented, EViews offre una vasta scelta di analisi statistiche e visualizzazioni grafiche senza bisogno di memorizzare complicati comandi.
La nuova versione 11 è stata potenziata con numerosi cambiamenti e migliorie.
Tra le caratteristiche di EViews migliorate troviamo:
EViews Interface, Data Handling, Graphs, Tables and Spools, Econometrics and Statistics, etc...
EViews 11 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 best.
A combination of power and ease-of-use make EViews the ideal
package for anyone working with time series, cross-section, or
longitudinal data. With EViews, you can quickly and efficiently manage
your data, perform econometric and statistical analysis, generate
forecasts or model simulations, and produce high quality graphs and
tables for publication or inclusion in other applications.
Featuring an innovative graphical object-oriented user-interface and a
sophisticated analysis engine, EViews blends the best of modern
software technology with the features you've always wanted. The result
is a state-of-the art program that offers unprecedented power within a
flexible, easy-to-use interface.
Find out for yourself why EViews is the worldwide leader in
Windows-based econometric software and the choice of those who demand
the very best.
EViews 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
Basic Data Handling
- Numeric, alphanumeric (string), and date series; value labels.
- Extensive library of operators and statistical, mathematical, date and string functions.
- Powerful language for expression handling and transforming existing data using operators and functions.
- Samples and sample objects facilitate processing on subsets of data.
- Support for complex data structures
including regular dated data, irregular dated data, cross-section data
with observation identifiers, dated, and undated panel data.
- Multi-page workfiles.
- EViews native, disk-based databases provide powerful query features and integration with EViews workfiles.
- Convert data between EViews and various
spreadsheet, statistical, and database formats, including (but not
limited to): Microsoft Access® and Excel® files (including .XSLX and
Dataset files, SAS® Transport files, SPSS native and portable files, Stata
files, Tableau®, 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 OLEDB-aware clients or custom programs.
- Support for FRED® (Federal Reserve Economic
Data), World Bank, NOAA, US Census, US BEA, US BLS, ECB SDMX, IMF SDMX,
UN SDMX and EuroStat 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 and metadata 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 the week.
- Specialized time series functions and operators: lags, differences, log-differences, moving averages, etc.
- Frequency conversion: various high-to-low and low-to-high methods.
- 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, STL Decomposition, MoveReg, X-12-ARIMA, Tramo/Seats, moving average.
- Interpolation to fill in missing values within a series: Linear, Log-Linear, Catmull-Rom Spline, Cardinal Spline.
- Basic data summaries; by-group summaries.
- Tests of equality: t-tests, ANOVA (balanced
and unbalanced, with or without heteroskedastic variances.), Wilcoxon,
Mann-Whitney, Median Chi-square, Kruskal-Wallis, van der Waerden,
F-test, Siegel-Tukey, Bartlett, Levene, Brown-Forsythe.
- One-way tabulation; cross-tabulation with
measures of association (Phi Coefficient, Cramer's V, Contingency
Coefficient) and independence testing (Pearson Chi-Square, Likelihood
- Covariance and correlation analysis including Pearson, Spearman rank-order, Kendall's tau-a and tau-b and partial analysis.
- Principal components analysis including scree plots, biplots and loading plots, and weighted component score calculations.
- Factor analysis allowing computation of
measures of association (including covariance and correlation),
uniqueness estimates, factor loading estimates and factor scores, as
well as performing estimation diagnostics and factor rotation using one
of over 30 different orthogonal and oblique methods.
- Empirical Distribution Function (EDF) Tests
for the Normal, Exponential, Extreme value, Logistic, Chi-square,
Weibull, or Gamma distributions (Kolmogorov-Smirnov, Lilliefors,
Cramer-von Mises, Anderson-Darling, Watson).
- Histograms, Frequency Polygons, Edge
Frequency Polygons, Average Shifted Histograms, CDF-survivor-quantile,
Quantile-Quantile, kernel density, fitted theoretical distributions,
- Scatterplots with parametric and
non-parametric regression lines (LOWESS, local polynomial), kernel
regression (Nadaraya-Watson, local linear, local polynomial)., or
- Autocorrelation, partial autocorrelation, cross-correlation, Q-statistics.
- Granger causality tests, including panel Granger causality.
- Unit root tests: Augmented Dickey-Fuller,
GLS transformed Dickey-Fuller, Phillips-Perron, KPSS,
Eliot-Richardson-Stock Point Optimal, Ng-Perron, as well as tests for
unit roots with breakpoints, and seasonal unit root tests.
- Cointegration tests: Johansen, Engle-Granger, Phillips-Ouliaris, Park added variables, and Hansen stability.
- Independence tests: Brock, Dechert, Scheinkman and LeBaron
- Variance ratio tests: Lo and MacKinlay, Kim
wild bootstrap, Wright's rank, rank-score and sign-tests. Wald and
multiple comparison variance ratio tests (Richardson and Smith, Chow and
- Long-run variance and covariance
calculation: symmetric or or one-sided long-run covariances using
nonparametric kernel (Newey-West 1987, Andrews 1991), parametric VARHAC
(Den Haan and Levin 1997), and prewhitened kernel (Andrews and Monahan
1992) methods. In addition, EViews supports Andrews (1991) and
Newey-West (1994) automatic bandwidth selection methods for kernel
estimators, and information criteria based lag length selection methods
for VARHAC and prewhitening estimation.
Panel and Pool
- By-group and by-period statistics and testing.
- Unit root tests: Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, Hadri.
- Cointegration tests: Pedroni, Kao, Maddala and Wu.
- Panel within series covariances and principal components.
- Dumitrescu-Hurlin (2012) panel causality tests.
- Cross-section dependence tests.
- 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 other heteroskedasticity
consistent, and Newey-West robust standard errors. HAC standard errors
may be computed using nonparametric kernel, parametric VARHAC, and
prewhitened kernel methods, and allow for
Andrews and Newey-West automatic bandwidth selection
methods for kernel estimators, and information criteria based lag
length selection methods for VARHAC and prewhitening estimation.
- Clustered standard errors.
- 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, and smooth threshold regression including STAR.
- ARDL estimation, including the Bounds Test approach to cointegration.
- Elastic net, ridge regression and LASSO estimation.
- Functional coefficient estimation.
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 SAR errors.
- Limited Information Maximum Likelihood (LIML) and K-class estimation.
- Wide range of GMM weighting matrix specifications (White, HAC, User-provided) with control over weight matrix iteration.
- GMM estimation options include continuously updating estimation (CUE), and a host of new
standard error options, including Windmeijer standard errors.
- IV/GMM specific diagnostics include Instrument Orthogonality Test, a
Regressor Endogeneity Test, a Weak Instrument Test, and a GMM specific
- GARCH(p,q), EGARCH, TARCH, Component GARCH, Power ARCH, Integrated GARCH.
- The linear or nonlinear mean equation may
include ARCH and ARMA terms; both the mean and variance equations allow
for exogenous variables.
- Normal, Student's t, and Generalized Error Distributions.
- Bollerslev-Wooldridge robust standard errors.
- In- and out-of sample forecasts of the conditional variance and mean, and permanent components.
Limited Dependent Variable Models
- Binary Logit, Probit, and Gompit (Extreme Value).
- Ordered Logit, Probit, and Gompit (Extreme Value).
- Censored and truncated models with normal, logistic, and extreme value errors (Tobit, etc.).
- Count models with Poisson, negative binomial, and quasi-maximum likelihood (QML) specifications.
- Heckman Selection models.
- Huber/White robust standard errors.
- Count models support generalized linear model or QML standard errors.
- Hosmer-Lemeshow and Andrews Goodness-of-Fit testing for binary models.
- Easily save results (including generalized residuals and gradients) to new EViews objects for further analysis.
- General GLM estimation engine may be used to estimate several of these models, 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,
- 2SLS/IV estimation with cross-section and period fixed or random effects.
- Estimation with AR errors using nonlinear least squares on a transformed specification
- Generalized least squares, generalized
2SLS/IV estimation, GMM estimation allowing for cross-section or period
heteroskedastic and correlated specifications.
- Linear dynamic panel data estimation using
first differences or orthogonal deviations with period-specific
predetermined instruments (Arellano-Bond).
- 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
Single Equation Cointegrating Regression
- Support for three fully efficient estimation
methods, Fully Modified OLS (Phillips and Hansen 1992), Canonical
Cointegrating Regression (Park 1992), and Dynamic OLS (Saikkonen 1992,
Stock and Watson 1993
- Engle and Granger (1987) and Phillips and
Ouliaris (1990) residual-based tests, Hansen's (1992b) instability test,
and Park's (1992) added variables test.
- Flexible specification of the trend and deterministic regressors in the equation and cointegrating regressors specification.
- Fully featured estimation of long-run variances for FMOLS and CCR.
- Automatic or fixed lag selection for DOLS lags and leads and for long-run variance whitening regression.
- Rescaled OLS and robust standard error calculations for DOLS.
User-specified Maximum Likelihood
- Use standard EViews series expressions to describe the log likelihood contributions.
- Examples for multinomial and conditional
logit, Box-Cox transformation models, disequilibrium switching models,
probit models with heteroskedastic errors, nested logit, Heckman sample
selection, and Weibull hazard models.
Systems of Equations
- Linear and nonlinear estimation.
- Least squares, 2SLS, equation weighted estimation, Seemingly Unrelated Regression, and Three-Stage Least Squares.
- GMM with White and HAC weighting matrices.
- AR estimation using nonlinear least squares on a transformed specification.
- Full Information Maximum Likelihood (FIML).
- Estimate structural factorizations in VARs by imposing short- or long-run restrictions, or both.
- Bayesian VARs, with Bayesian sampling of forecasts and impulse responses.
- Mixed frequency VARs.
- Markov Switching VARs.
- Impulse response functions in various
tabular and graphical formats with standard errors calculated
analytically or by Monte Carlo methods.
- Impulse response shocks computed from
Cholesky factorization, one-unit or one-standard deviation residuals
(ignoring correlations), generalized impulses, structural factorization,
or a user-specified vector/matrix form.
- Historical decomposition of standard VAR models.
- 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
- Conditional Constant Correlation (p,q), Diagonal VECH (p,q), Diagonal BEKK (p,q), with asymmetric terms.
- Extensive parameterization choice for the Diagonal VECH's coefficient matrix.
- Exogenous variables allowed in the mean and variance equations; nonlinear and AR terms allowed in the mean equations.
- Bollerslev-Wooldridge robust standard errors.
- Normal or Student's t multivariate error distribution
- A choice of analytic or (fast or slow) numeric derivatives. (Analytics derivatives not available for some complex models.)
- Generate covariance, variance, or correlation in various tabular and graphical formats from estimated ARCH models.
- Kalman filter algorithm for estimating user-specified single- and multiequation structural models.
- Exogenous variables in the state equation and fully parameterized variance specifications.
- Generate one-step ahead, filtered, or smoothed signals, states, and errors.
- Examples include time-varying parameter, multivariate ARMA, and quasilikelihood stochastic volatility models.
Testing and Evaluation
Forecasting and Simulation
- In- or out-of-sample static or dynamic
forecasting from estimated equation objects with calculation of the
standard error of the forecast.
- Forecast graphs and in-sample forecast evaluation: RMSE, MAE, MAPE, Theil Inequality Coefficient and proportions
- State-of-the-art model building tools for multiple equation forecasting and multivariate simulation.
- Model equations may be entered in text or as links for automatic updating on re-estimation.
- Display dependency structure or endogenous and exogenous variables of your equations.
- Gauss-Seidel, Broyden and Newton model solvers
for non-stochastic and stochastic simulation. Non-stochastic forward
solution solve for model consistent expectations. Stochasitc simulation
can use bootstrapped residuals.
- Solve control problems so that endogenous variable achieves a user-specified target.
- Sophisticated equation normalization, add factor and override support.
- Manage and compare multiple solution scenarios involving various sets of assumptions.
- Built-in model views and procedures display simulation results in graphical or tabular form.
Graphs, Tables and Maps
- Line, dot plot, area, bar, spike, seasonal,
pie, xy-line, scatterplots, bubbleplots, boxplots, error bar,
high-low-open-close, and area band.
- Powerful, easy-to-use categorical and summary graphs.
- Auto-updating graphs which update as underlying data change.
- Observation info and value display when you hover the cursor over a point in the graph.
- Histograms, average shifted historgrams,
frequency polyons, edge frequency polygons, boxplots, kernel density,
fitted theoretical distributions, boxplots, CDF, survivor, quantile,
- Scatterplots with any combination parametric
and nonparametric kernel (Nadaraya-Watson, local linear, local
polynomial) and nearest neighbor (LOWESS) regression lines, or
- Interactive point-and-click or command-based customization.
- Extensive customization of graph background,
frame, legends, axes, scaling, lines, symbols, text, shading, fading,
with improved graph template features.
- Table customization with control over cell font
face, size, and color, cell background color and borders, merging, and
- 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, LaTeX, PDF, or text file.
- Manage graphs and tables together in a spool object that lets you display multiple results and analyses in one object.
- Open geographical map ShapeFiles and tie the
regions to data in your EViews workfile, allowing coloring and labelling
of those regions by data.
Commands and Programming
- Object-oriented command language provides access to menu items.
- Batch execution of commands in program files.
- Looping and condition branching, subroutine, and macro processing.
- String and string vector objects for string processing. Extensive library of string and string list functions.
- Extensive matrix support: matrix manipulation,
multiplication, inversion, Kronecker products, eigenvalue solution, and
singular value decomposition.
External Interface and Add-Ins
- EViews COM automation server support so that
external programs or scripts can launch or control EViews, transfer
data, and execute EViews commands.
- EViews offers integration with MATLAB®, R and
Python, so that EViews may be used to launch or control these
applications, transfer data, or execute commands.
- The EViews Microsoft Excel® Add-in offers a
simple interface for fetching and linking from within Microsoft Excel®
(2000 and later) to series and matrix objects stored in EViews
workfiles and databases.
- The EViews Add-ins infrastructure offers seamless access to user-defined
programs using the standard EViews command, menu, and object
- Download and install predefined Add-ins from the EViews website.
EViews 11 features a wide range of exciting changes and
improvements. The following is an overview of the most important new
features in Version 11.
Graphs, Tables and Spools
Econometrics and Statistics
Testing and Diagnostics
Maximum observations per series (32bit version)
4 million (by default), may be increased up to 15 million, if
desired, subject to memory restrictions.
Maximum observations per series (64bit version)
Total observations: (series x obs per series)
limited only by available RAM.
Maximum objects per workfile
limited only by available RAM.
Maximum objects per database
limited to 10 million objects, 64 gigabytes or
available disk space.
EViews Enterprise offers all the features of the Standard Version of EViews, 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 third-party 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.
Pentium or better
Windows 10 (32bit or 64bit)
Windows 8.1 (32bit or 64bit)
Windows 8 (32bit or 64bit)
Windows 7 (32bit or 64bit)
Windows Vista (32bit or 64bit)
Windows Server 2016 (32bit or 64bit)*
Windows Server 2012 (32bit or 64bit)*
Windows Server 2008 (32bit or 64bit)*
.Net 4.0 is required for connectivity to certain external databases and installation of the EViews-Excel add-in.
400 MB of available hard disk space for the EViews executable, supporting files, full documentation, and example files.
For information on running EViews under a virtual or remote environment, such as VMWare, Citrix or Remote Desktop, please wrote to firstname.lastname@example.org