ADALTA corsi in italiano e acquisti software scientifico Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews
Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews
Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews
Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews
Info e Commerciali Intel software, Wolfram Mathematica, Origin, Statgraphics, EViews
EViews 9.5
Lingua: Ing S.O.: Win
Produttore: IHS

Introduzione in Italiano

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 versione 9 è stata potenziata con numerosi miglioramenti sia alle funzionalità generali di base (64-bit, interfaccia utente, ecc.) sia a quelle delle aree di gestione dati, analisi econometriche e statistiche, produzione di grafici e tabelle, costruzione di modelli e supporto alla programmazione.




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A combination of power and ease-of-use make EViews 9 the ideal package for anyone working with time series, cross-section, or longitudinal data. With EViews, you can quickly and efficiently manage your data, perform econometric and statistical analysis, generate forecasts or model simulations, and produce high quality graphs and tables for publication or inclusion in other applications.

Featuring an innovative graphical object-oriented user-interface and a sophisticated analysis engine, EViews blends the best of modern software technology with the features you’ve always wanted. The result is a state-of-the art program that offers unprecedented power within a flexible, easy-to-use interface.

Find out for yourself why EViews is the worldwide leader in Windows-based econometric software and the choice of those who demand the very best.


Part 1: An Intuitive, Easy-to-Use Interface

EViews sets the standard for econometric software by incorporating modern windowing and object-based techniques. The result is powerful software with an intuitive, easy-to-use user interface.

An Object-Based Interface

At the heart of the innovative EViews interface is the concept of an object. Series, equations, and systems are just a few examples of objects. Each object has its own window, menus, procedures, and its own views of its data. Most statistical procedures are simply alternate views of the object. For example, a simple menu choice from a series window changes the display between a spreadsheet, various graph views, descriptive statistics and tests, tabulations, correlograms, unit root, and independence tests.

Similarly, an equation window allows you to switch between a display of the equation specification, basic estimation results, actual-fitted-residual graphs and tables, a display of the equation ARMA structure (if appropriate), gradients and derivatives of the specification, the coefficient covariance matrix, forecast graphs and evaluations, and over a dozen diagnostic and hypothesis tests.

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You can select a histogram view from the series-specific menu.

Multiple Window Display

Unlike traditional statistics programs that support viewing only one estimation equation or graph at a time, EViews allows for simultaneous display of multiple objects, each in its own window. This true multiple window support makes it easy to perform side-by-side comparisons of series plots, hypothesis tests, equation estimates, or model forecasts developed under alternative assumptions.

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EViews offers true multi-window 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 object-based approach includes sophisticated linking technology that allows you to define relationships between multiple objects and external data sources. Series objects, for example, may be linked by formula to data in other series, to match merged or frequency converted data from alternate data sets, or to data from external databases. When defined in this fashion, the linked series dynamically updates its data whenever the underlying data change.

Similarly, an EViews model simulation object can be linked to equation or system objects so that the model specification updates automatically when the underlying equation or system is re-specified or re-estimated.

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Modern linking technology offers dynamic updating of data.

Windows Integration

Couple all of this with strong Windows integration, including drag-and-drop file import for over twenty popular file formats and copy-and-paste export of presentation quality graphs and tables, and you have a modern interface that allows you to accomplish, with ease, tasks that are difficult or impossible using traditional statistical software.

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Easy data import using drag-and-drop.

Add-Ins and User Objects

EViews offers an easy-to-use EViews Add-ins infrastructure that provides seamless access to user-defined programs using the standard EViews command, menu, and object interface.

Add-ins offer you a exciting new way of running EViews programs. You may readily define Add-ins that augment the EViews language with user-defined commands, specify new menu entries for point-and-click program interaction, and display program output in standard EViews object windows.

User objects further extend EViews by allowing the creation of user-defined objects inside your workfiles. User objects can be as simple as a basic results storage containier to as complicated as a fully functioning estimation object.

All EViews users may benefit immediately by installing prepackaged Add-ins which add functionality to EViews. A single click is all that you need to download and install any of the Add-ins currently available on the EViews website, with the promise of more to come.

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Add-ins offer seamless access to user-defined 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' built-in procedures are a mouse-click away and provide the tools most frequently used in practical econometric and forecasting work.

Basic Statistical Analysis

EViews supports a wide range of basic statistical analyses, encompassing everything from simple descriptive statistics to parametric and nonparametric hypothesis tests.

Basic descriptive statistics are quickly and easily computed over an entire sample, by a categorization based on one or more variables, or by cross-section or period in panel or pooled data. Hypothesis tests on mean, median and variance may be carried out, including testing against specific values, testing for equality between series, or testing for equality within a single series when classified by other variables (allowing you to perform one-way ANOVA). Tools for covariance and factor analysis allow you to examine the relationships between variables.

You can visualize the distribution of your data using histograms, theoretical distribution, kernel density, or cumulative distribution, survivor, and quantile plots. QQ-plots (quantile-quantile plots) may be used to compare the distribution of a pair of series, or the distribution of a single series against a variety of theoretical distributions.

You can even perform Kolmogorov-Smirnov, Liliefors, Cramer von Mises, and Anderson-Darling tests to see whether your series is distributed normally, or whether it comes from another distribution such as an exponential, extreme value, logistic, chi-square, Weibull, or gamma distribution.

EViews also produces scatter plots with curve fitting using ordinary, transformation, kernel, and nearest neighbor regression.

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EViews performs a wide range of basic statistical analysis.
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Examine the distribution of your data.
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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 Q-statistics to unit root tests.

EViews provides autocorrelation and partial autocorrelation functions, Q-statistics, and cross-correlation functions, as well as unit root tests (ADF, Phillips-Perron,  KPSS, DFGLS, ERS, or Ng-Perron for single time series and Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, or Hadri for panel data), cointegration tests (Johansen with MacKinnon-Haug-Michelis critical values and p-values for ordinary data, and Pedroni, Kao, or Fisher for panel data), causality, and independence tests.

EViews also provides easy-to-use front-end support for the U.S. Census Bureau's X-13 Seasonal Adjustment programs, which is an updated version of both X-12 and Tramo/Seats. Simple seasonal adjustment using additive and multiplicative difference methods is also supported in EViews.

You can even use EViews to compute trends and cycles from time series data using the Hodrick-Prescott, Baxter-King, Christiano-Fitzgerald fixed length and Christiano-Fitzgerald asymmetric full sample band-pass (frequency) filters.

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Explore the time series properties of your data.
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EViews provides easy-to-use interfaces to X12 and Tramo/Seats.
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Use filters to compute trends and cycles from your time series data.


Panel and Pooled Data Statistics and Tools

EViews features a wide variety of tools designed to facilitate working with both panel or pooled/time series-cross section data. Define panel structures with virtually no limit on the number of cross-sections or groups, or on the number of periods or observations in a group. Dated or undated, balanced or unbalanced, and regular or irregular frequency panel data sets are all handled naturally within the EViews framework.

Data structure tools facilitate  transforming your data from stacked (panel) to unstacked (pooled) formats, and back again. Smart links, auto series, and data extraction tools, allow you to slice, match merge, frequency convert, and summarize your data with ease.

Support for basic longitudinal data analysis ranges from convenient by-group and by-period statistics, tests, and graphs, to sophisticated panel unit root (Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, or Fisher) and cointegration diagnostics (Pedroni (2004), Pedroni (1999), and Kao, or the Fisher-type test).

Specialized tools for displaying panel data graphs allow you to view stacked, individual, or summary displays. Display line graphs of each graph in a single graph frame or in individual frames. Or show summary statistics for the panel data taken across cross-sections, with mean (or median) and standard deviation (or quantile) bands.

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Dated or undated, balanced or balanced...EViews understands your panel data structure.
Perform panel unit root and cointegration tests.


Visualize your panel data in a variety of ways. 

Single Equation Estimation

EViews allows you to choose from a full set of basic single equation estimators including: ordinary and nonlinear least squares (multiple regression), weighted least squares, two-stage least squares (instrumental variables), quantile regression (including least absolute deviations estimation), and  stepwise linear regression. Weighted estimation is available for all of  these techniques. Specifications may include polynomial lag structures on any number of independent variables.

For time series analysis, EViews estimates ARMA and ARMAX models, and a wide range of ARCH specifications. Structural time series models may be estimated using the state space object.

In addition to these basic estimators, EViews supports estimation and diagnostics for a variety of advanced models.

Generalized Method of Moments (GMM)

EViews supports GMM estimation for both cross-section and time series data (single and multiple equation). Weighting options include the White covariance matrix for cross-section data and a variety of HAC covariance matrices for time series data. The HAC options include prewhitening, a variety of kernels, and fixed, Andrews, or Newey-West bandwith selection methods. You can estimate a GMM equation using either iterative procedures, or a continuously updating procedure. Post-estimation diagnostics for GMM equations, including weak instrument statistics, are also available.


If the variance of your series fluctuates over time, EViews can estimate the path of the variance using a wide variety of Autoregressive Conditional Heteroskedasticity (ARCH) models. EViews handles GARCH(p,q), EGARCH(p,q), TARCH(p,q), PARCH(p,q), and Component GARCH specifications and provides maximum likelihood estimation for errors following a normal, Student's t or Generalized Error Distribution. The mean equation of ARCH models may include ARCH and ARMA terms, and both the mean and variance equations allow for 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 quasi-maximum likelihood (QML) specifications. EViews optionally reports generalized linear model or QML standard errors.

Panel and Pooled Time Series-Cross Section

EViews offers various panel and pooled data estimation methods. In addition to ordinary linear and non-linear least-squares, equation estimation methods include 2SLS/IV and Generalized 2SLS/IV, and GMM, which can be used to estimate complex dynamic panel data specifications (including Anderson-Hsiao and Arellano-Bond types of estimators).

Most of the methods allow for both time and cross-section fixed and random effects specifications. For random effects models, quadratic unbiased estimators of component variances include Swamy-Arora, Wallace-Hussain and Wansbeek-Kapteyn.

Also supported are AR specifications (any effects are defined after transformation), weighted least squares, and seemingly unrelated regression. In pools, coefficients for specific variables (including AR terms) can be constrained to be identical, or allowed to differ across  cross-sections.

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EViews offers a full range of single equation estimators.
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GMM estimation offers a variety of weighting matrix and covariance options.
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Easy-to-use dialogs make it easy to specify your ARCH model.
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EViews estimates both ML and QML count models.
EViews offers a range of panel data estimators and options.
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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, two-stage least squares, seemingly unrelated regression, three-stage least squares, GMM, and FIML. The system may contain cross equation restrictions and in most cases, autoregressive errors of any order.

Vector Autoregression/Error Correction Models

Vector Autoregression (including Bayesian VARs) and Vector Error Correction models can be easily estimated by EViews. Once estimated, you may examine the impulse response functions and variance decompositions for the VAR or VEC. VAR impulse response functions and decompositions feature standard errors calculated either analytically or by Monte Carlo methods (analytic not available for decompositions) and may be displayed in a variety of graphical and tabular formats.

You may impose and test linear restrictions on the cointegrating relations and/or adjustment coefficients. EViews' VARs also allow you to estimate structural factorizations (VARs) by imposing short-run (Sims 1986) or long-run (Blanchard and Quah 1989) restrictions. Over-identifying restrictions may be tested using the LR statistic reported by EViews.

VARs support a variety of views to allow you to examine the structure of your estimated specification. With a few clicks of the mouse, you can display the inverse roots of the characteristic AR polynomial, perform Granger causality and joint lag exclusion tests, evaluate various lag length criteria, view correlograms and autocorrelations, or perform various multivariate residual based diagnostics.

Multivariate ARCH

Multivariate ARCH is useful in modeling time varying variance and covariance of multiple time series. A number of popular ARCH models, such as the Conditional Constant Correlation (CCC), the Diagonal VECH, and the Diagonal BEKK, are available. Exogenous variables are allowed in the mean and variance equations; nonlinear and AR terms can be included in the mean equations. The error is assumed to distributed either as multivariate Normal or Student's t. Bollerslev-Wooldridge robust standard errors are also available. Once the model is estimated, users can easily generate the in-sample variance, covariance, or correlation, in tabular or graphic format.

State-Space Models

The state-space object allows estimation of a wide variety of single- and multi-equation dynamic time-series models using the Kalman Filter algorithm. Among other things, you can use the state-space object to estimate random and time-varying coefficient models and ML ARMA specifications.

Sophisticated procs and views give you access to powerful filtering and smoothing tools so that you can view or generate one-step ahead, filtered, or smoothed signals, states, or errors. EViews' built-in forecasting procedures also provide easy-to-use tools for in- and out-of-sample forecasting using n-step ahead or smoothed values.

Specify and estimate systems of equations using the system object.
Estimate VAR or VEC models and easily produce impulse response graphs.
Model the system covariances and correlations using multivariate ARCH.
Estimate state space models using the Kalman Filter and display filtered results.

User-Specified Maximum Likelihood

For custom analysis, EViews' easy-to-use likelihood object permits estimation of user-specified maximum likelihood models. You simply provide standard EViews expressions to describe the log likelihood contributions for each observation in your sample, set coefficient starting values, and EViews will do the rest.

Define your own estimator using the likelihood object.

Part 3: Sophisticated Data Management

Powerful analytic tools are only useful if you can easily work with your data. EViews provides the widest range of data management tools available in any econometric software. From its extensive library of mathematical, statistical, date, string, and time series operators and functions, to comprehensive support for numeric, character, and date data, EViews offers the data handling features you’ve come to expect from modern statistical software.

Extensive Function Library

EViews includes an extensive library of functions for working with data. In addition to standard mathematical and trigonometric functions, EViews provides functions for descriptive statistics, cumulative and moving statistics, by-group statistics, special functions, specialized date and time series  operations, workfile, value map, and financial calculations.

EViews also provides random number generators (Knuth, L'Ecuyer or Mersenne-Twister), density functions and cumulative distribution functions for eighteen different distributions.These may be used in generating new series, or in calculating scalar and matrix expressions.

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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.

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Work directly with expressions in place of variables.

Links, Formulas and Values Maps

Link objects allow you to create series that link to data contained in other workfiles or workfile pages. Links allow you to combine data at different frequencies, or match merge in data from a summary page into an individual page such that the data is dynamically updated whenever the underlying data change. Similarly, within a workfile, formulas can be assigned to data series so that the data series are automatically recalculated whenever the underlying data is modified.

Value labels (e.g., "High", "Med", "Low", corresponding to 2, 1, 0) may be applied to numeric or alpha series so that categorical data can be displayed with meaningful labels. Built-in functions allow you to work with either the underlying or the mapped values when performing calculations.

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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, cross-section data with observation identifiers, and dated and undated panel data.

In addition to numerical data, an EViews workfile can also contain alphanumeric (character string) data, and series containing dates, all of which may be manipulated using an extensive library of functions.

EViews also provides a wide range of tools for working with datasets (workfiles), data including the ability to combine series by complex match merge criteria and  workfile procedures for changing the structure of your data: join, append, subset, resize, sort, and reshape (stack and unstack).

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EViews workfiles can be highly structured.
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Alphanumeric and date data are fully supported.

File Import and Export

Exchanging data with other programs is easy, since EViews reads and writes over 20 popular data formats (including Excel, formatted and unformatted ASCII/Text, SPSS, SAS (transport), Stata,  SPSS, Html, Microsoft Access, Gauss Dataset, Rats, GiveWin/PC Give, TSP, Aremos, dBase, Lotus, and binary files). Simply drag-and-drop your foreign file onto EViews and your data will automatically appear in an EViews workfile. Or use the easy-to-use dialogs and wizards to cutomize the importing of your data.

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EViews reads and writes an extensive list of data formats.

EViews Databases

EViews provides sophisticated built-in database features. An EViews database is a collection of EViews objects maintained in a single file on disk. It need not be loaded into memory in order to access an object inside it, and the objects in the database are not restricted to being of a single frequency or range. EViews databases offer powerful query features which can be used to search through the database for a particular series or select a set of series with a common property.

Series contained in EViews databases may be copied (fetched) into a workfile, or they may be accessed and used by EViews procedures without being fetched into workfiles. In both cases, EViews will automatically perform frequency conversion if necessary. Automatic search capabilities allow you to specify a list of databases to be searched when a series you need cannot be found in the current workfile.

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Built-in database tools offer powerful query features.
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Easily move data from databases to your EViews workfiles.

Enterprise Edition Support for ODBC, FAMETM, DRIBase, and Haver Analytics Databases

As part of the EViews Enterprise Edition (an extra cost option over EViews Standard Edition), support is provided for access to data contained in relational databases (via ODBC drivers) and to databases in a variety of proprietary formats used by commercial data and database vendors. Open Database Connectivity (ODBC) is a standard supported by many relational database systems including Oracle, Microsoft SQL Server and IBM DB2. EViews allows you to read or write entire tables from ODBC databases, or to create a new workfile from the results of a SQL query.

EViews Enterprise Edition also supports access to FAMETM format databases (both local and server based) Global Insight's DRIPro and DRIBase databanks, Haver Analytics DLX databases, Datastream, FactSet, and Moody's The familiar, easy-to-use EViews database interface has been extended to these data formats so that you may work with foreign databases as easily as native EViews databases.

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Read directly from ODBC sources using the EViews Enterprise Edition.
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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 irregular-frequency data. Series may be assigned a preferred conversion method, allowing you to use different methods for different series without having to specify the conversion method every time a series is accessed.

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

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Specify a series-specific 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 line-bar graphs, high-low 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.

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 copy-and-paste or by writing the graph to a Windows metafile, or a PostScript, bitmap, PNG, GIF, or JPEG file.

Export graphs in a variety of formations.

Export graphs in a variety of formations.

Extensive table customization tools allow you to produce presentation quality tables for inclusion in other programs. An easy-to-use, interactive interface gives you control over cell font face, size, and color, cell background color and borders, merging, and annotation.

EViews offers an extensive set of table customization tools.

EViews offers an extensive set of table customization tools.

When completed, you can copy-and-paste your customized table to another application or save it as an RTF, HTML, PDF, or text file.

Part 5: Traditional Command Line and Programming

Point-and-click is great, but what if you feel more comfortable entering commands? And what if you need programming capabilities? In addition to its state-of-the-art windowing interface, EViews includes a powerful command language that provides access to the features that are available through the menus.

All EViews features are available via the command line.

All EViews features are available via the command line.

Modeled loosely after the BASIC programming language but with object-oriented extensions and matrix handling capabilities, EViews allows you to enter individual commands for immediate or batch execution. Your programs can make use of looping and condition branching, as well as subroutine, macro, and string list processing.

Create program files for batch processing of commands.

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, 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® and Powerpoint®.
  • OLEDB support for reading EViews workfiles and databases using OLEDB-aware clients or custom programs.
  • Support for FRED® (Federal Reserve Economic Data) databases. Enterprise Edition support for Global Insight DRIPro and DRIBase, Haver Analytics® DLX®, FAME, EcoWin, Bloomberg, EIA, CEIC, Datastream, FactSet, and Moody’s 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 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, X-12-ARIMA, Tramo/Seats, moving average.
  • Interpolation to fill in missing values within a series: Linear, Log-Linear, Catmull-Rom Spline, Cardinal Spline.



  • Basic data summaries; by-group summaries.
  • Tests of equality: t-tests, ANOVA (balanced and unbalanced, with or without heteroskedastic variances.), Wilcoxon, Mann-Whitney, Median Chi-square, Kruskal-Wallis, van der Waerden, F-test, Siegel-Tukey, Bartlett, Levene, Brown-Forsythe.
  • One-way tabulation; cross-tabulation with measures of association (Phi Coefficient, Cramer’s V, Contingency Coefficient) and independence testing (Pearson Chi-Square, Likelihood Ratio G^2).
  • Covariance and correlation analysis including Pearson, Spearman rank-order, Kendall’s tau-a and tau-b and partial analysis.
  • Principal components analysis including scree plots, biplots and loading plots, and weighted component score calculations.
  • Factor analysis allowing computation of measures of association (including covariance and correlation), uniqueness estimates, factor loading estimates and factor scores, as well as performing estimation diagnostics and factor rotation using one of over 30 different orthogonal and oblique methods.
  • Empirical Distribution Function (EDF) Tests for the Normal, Exponential, Extreme value, Logistic, Chi-square, Weibull, or Gamma distributions (Kolmogorov-Smirnov, Lilliefors, Cramer-von Mises, Anderson-Darling, Watson).
  • Histograms, Frequency Polygons, Edge Frequency Polygons, Average Shifted Histograms, CDF-survivor-quantile, Quantile-Quantile, kernel density, fitted theoretical distributions, boxplots.
  • Scatterplots with parametric and non-parametric regression lines (LOWESS, local polynomial), kernel regression (Nadaraya-Watson, local linear, local polynomial)., or confidence ellipses.

Time Series

  • 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.
  • Cointegration tests: Johansen, Engle-Granger, Phillips-Ouliaris, Park added variables, and Hansen stability.
  • Independence tests: Brock, Dechert, Scheinkman and LeBaron
  • Variance ratio tests: Lo and MacKinlay, Kim wild bootstrap, Wright's rank, rank-score and sign-tests. Wald and multiple comparison variance ratio tests (Richardson and Smith, Chow and Denning).
  • Long-run variance and covariance calculation: symmetric or or one-sided long-run covariances using nonparametric kernel (Newey-West 1987, Andrews 1991), parametric VARHAC (Den Haan and Levin 1997), and prewhitened kernel (Andrews and Monahan 1992) methods. In addition, EViews supports Andrews (1991) and Newey-West (1994) automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.

Panel and Pool

  • By-group and by-period statistics and testing.
  • Unit root tests: Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, Hadri.
  • Cointegration tests: Pedroni, Kao, Maddala and Wu.
  • 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 Newey-West robust standard errors. HAC standard errors may be computed using nonparametric kernel, parametric VARHAC, and prewhitened kernel methods, and allow for Andrews and Newey-West automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.
  • Linear quantile regression and least absolute deviations (LAD), including both Huber’s Sandwich and bootstrapping covariance calculations.
  • Stepwise regression with seven different selection procedures.
  • Threshold regression including TAR and SETAR.


  • 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 breakpoint test.


  • 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, Wallace-Hussain, Wansbeek-Kapteyn.
  • 2SLS/IV estimation with cross-section and period fixed or random effects.
  • Estimation with AR errors using nonlinear least squares on a transformed specification
  • Generalized least squares, generalized 2SLS/IV estimation, GMM estimation allowing for cross-section or period heteroskedastic and correlated specifications.
  • Linear dynamic panel data estimation using first differences or orthogonal deviations with period-specific predetermined instruments (Arellano-Bond).
  • 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 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.
  • Bayesian VARs.
  • Impulse response functions in various tabular and graphical formats with standard errors calculated analytically or by Monte Carlo methods.
  • Impulse response shocks computed from Cholesky factorization, one-unit or one-standard deviation residuals (ignoring correlations), generalized impulses, structural factorization, or a user-specified vector/matrix form.
  • Impose and test linear restrictions on the cointegrating relations and/or adjustment coefficients in VEC models.
  • View or generate cointegrating relations from estimated VEC models.
  • Extensive diagnostics including: Granger causality tests, joint lag exclusion tests, lag length criteria evaluation, correlograms, autocorrelation, normality and heteroskedasticity testing, cointegration testing, other multivariate diagnostics.

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.
  • 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.

State Space

  • 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

  • Actual, fitted, residual plots.
  • Wald tests for linear and nonlinear coefficient restrictions; confidence ellipses showing the joint confidence region of any two functions of estimated parameters.
  • Other coefficient diagnostics: standardized coefficients and coefficient elasticities, confidence intervals, variance inflation factors, coefficient variance decompositions.
  • Omitted and redundant variables LR tests, residual and squared residual correlograms and Q-statistics, residual serial correlation and ARCH LM tests.
  • White, Breusch-Pagan, Godfrey, Harvey and Glejser heteroskedasticity tests.
  • Stability diagnostics: Chow breakpoint and forecast tests, Quandt-Andrews unknown breakpoint test, Bai-Perron breakpoint tests, Ramsey RESET tests, OLS recursive estimation, influence statistics, leverage plots.
  • ARMA equation diagnostics: graphs or tables of the inverse roots of the AR and/or MA characteristic polynomial, compare the theoretical (estimated) autocorrelation pattern with the actual correlation pattern for the structural residuals, display the ARMA impulse response to an innovation shock and the ARMA frequency spectrum.
  • Easily save results (coefficients, coefficient covariance matrices, residuals, gradients, etc.) to EViews objects for further analysis.
  • See also Estimation and Systems of Equations for additional specialized testing procedures.

Forecasting and Simulation

  • In- or out-of-sample static or dynamic forecasting from estimated equation objects with calculation of the standard error of the forecast.
  • Forecast graphs and in-sample forecast evaluation: RMSE, MAE, MAPE, Theil Inequality Coefficient and proportions
  • State-of-the-art model building tools for multiple equation forecasting and multivariate simulation.
  • Model equations may be entered in text or as links for automatic updating on re-estimation.
  • Display dependency structure or endogenous and exogenous variables of your equations.
  • Gauss-Seidel, Broyden and Newton model solvers for non-stochastic and stochastic simulation. Non-stochastic forward solution solve for model consistent expectations. Stochasitc simulation can use bootstrapped residuals.
  • Solve control problems so that endogenous variable achieves a user-specified target.
  • Sophisticated equation normalization, add factor and override support.
  • Manage and compare multiple solution scenarios involving various sets of assumptions.
  • Built-in model views and procedures display simulation results in graphical or tabular form.

Graphs and Tables

  • Line, dot plot, area, bar, spike, seasonal, pie, xy-line, scatterplots, boxplots, error bar, high-low-open-close, and area band.
  • Powerful, easy-to-use categorical and summary graphs.
  • Auto-updating graphs which update as underlying data change.
  • Observation info and value display when you hover the cursor over a point in the graph.
  • Histograms, average shifted historgrams, frequency polyons, edge frequency polygons, boxplots, kernel density, fitted theoretical distributions, boxplots, CDF, survivor, quantile, quantile-quantile.
  • Scatterplots with any combination parametric and nonparametric kernel (Nadaraya-Watson, local linear, local polynomial) and nearest neighbor (LOWESS) regression lines, or confidence ellipses.
  • Interactive point-and-click or command-based customization.
  • Extensive customization of graph background, frame, legends, axes, scaling, lines, symbols, text, shading, fading, with improved graph template features.
  • Table customization with control over cell font face, size, and color, cell background color and borders, merging, and annotation.
  • Copy-and-paste graphs into other Windows applications, or save graphs as Windows regular or enhanced metafiles, encapsulated PostScript files, bitmaps, GIFs, PNGs or JPGs.
  • Copy-and-paste tables to another application or save to an RTF, HTML, or text file.
  • Manage graphs and tables together in a spool object that lets you display multiple results and analyses in one object

Commands and Programming

  • Object-oriented command language provides access to menu items.
  • Batch execution of commands in program files.
  • Looping and condition branching, subroutine, and macro processing.
  • String and string vector objects for string processing. Extensive library of string and string list functions.
  • Extensive matrix support: matrix manipulation, multiplication, inversion, Kronecker products, eigenvalue solution, and singular value decomposition.

External Interface and Add-Ins

  • EViews COM automation server support so that external programs or scripts can launch or control EViews, transfer data, and execute EViews commands.
  • EViews offers COM Automation client support application for MATLAB® and R servers so that EViews may be used to launch or control the application, transfer data, or execute commands.
  • The EViews Microsoft Excel® Add-in offers a simple interface for fetching and linking from within Microsoft Excel® (2000 and later) to series and matrix objects stored in EViews workfiles and databases.
  • The EViews Add-ins infrastructure offers seamless access to user-defined programs using the standard EViews command, menu, and object interface.
  • Download and install predefined Add-ins from the EViews website.

What's new


EViews 9.5 is coming with Mixed-Data Sampling (MIDAS), a new econometric and statistical technique that will assist you in the analysis of data with different time frequencies.

MIDAS - 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 simply aggregate the higher frequency data into the lowest frequency. For example, monthly data is summed to give a single quarterly value. MIDAS allows estimation without aggregation, giving more accurate results.

We’ve also made enhancements to the Model interface that will allow you to better organize and summarize your models. Additionally, you will be able to navigate equations better and you can safely protect your model when sharing it with others.

Here are the 5 enhancements to the Model object’s interface:
Print View – Easily display the structure and relationships of the variables in your model.
Scenario Descriptions – Add a comment/description to each scenario allowing you to keep track of each scenario’s purpose.
Scenario View – Quickly display the settings and description of your scenarios.
Lock Protection – Password protect your model to disallow editing of the model specification.
Equation Finding – Search for equations by equation name, dependent variable name or by independent variable name.

These are just a few of the enhancements we have added to EViews 9.5. Any EViews 9 Single-User License will receive a complimentary upgraded to EViews 9.5 upon release. Upgrade your older EViews license today to receive the great new features of EViews 9.5.



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.
  • Multi-graph viewing slideshow.
  • Rectangle and ellipse drawing.
  • Arrow, rectangle, and ellipse data-based anchoring.
  • Tables, graphs, and spools may now be saved in LaTeX format.

Econometrics and Statistics

  • Automatic ARIMA forecasting of a series.
  • Forecast evaluation and combination testing.
  • Forecast averaging.
  • VAR Forecasting.
  • 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.
  • Cross-section 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.



Enterprise Edition

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.

Third Party Vendors

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.

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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.

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ODBC can connect you to your own private databases.

EViews Database Extension Interface (EDX)

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.png (60.5 KB)
EDX allows you to build your own data browsers for your data.

EViews Database Objects Library (EDO)

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


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).


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.

Acquista: EViews

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