EViews 9 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.
What's New in EViews 9
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.
Command capture from the interactive interface
Dockable command and capture window interface
Database and workfile object preview
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
Autoregressive Distributed Lag regression (ARDL) with automatic lag selection
ML and GLS ARMA estimation
Pooled mean group estimation of panel data ARDL models
A new optimization engine
Testing and Diagnostics
Click here to view the full list of new features
Powerful Analytical Tools
In contrast with most 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.
Time Series Statistics and Tools
Explore the time series properties of your data with tools ranging from simple autocorrelation plots to frequency filters, from 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 for with MacKinnon-Haug-Michelis critical values and p-values 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 X11 and X12-ARIMA seasonal adjustment programs, as well as the Tramo/Seats software, which is quite frequently used by European researchers. 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 filter, Baxter-King, Christiano-Fitzgerald fixed length and Christiano-Fitzgerald asymmetric full sample band-pass (frequency) filters.
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.
EViews includes a wide range of single and multiple equation estimation techniques for both time series and cross section data. Basic estimators include ordinary least squares (multiple regression), two-stage least squares, and nonlinear least squares. Weighted estimation is available with all of these techniques. Specifications may include polynomial lag structures on any number of independent variables.
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.
Generalised Method of Moments
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.
Limited Dependent Variables
When your dependent variable takes on a limited set of values or is censored or truncated, EViews can take account of this information in the estimation procedure. 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 generalised linear model or QML standard errors.
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 Data Analysis 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.
Vector Autoregression / Error Correction Models
Vector Autoregression 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 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.
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.
User-Defined Maximum Likelihood Estimation
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.
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.
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.
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.
Data Structure 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).
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.
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.
Views 7 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.
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.
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.
When completed, you can copy-and-paste your customized table to another application or save it as an RTF, HTML, or text file.