EViews 6 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 files, Excel files, Gauss dataset files, ODBC Dsn files, ODBC Query files, SAS Transport files, native SPSS files, SPSS Portable files, Stata files, Rats files, GiveWin files, TSP Portable files, raw formatted ASCII text or binary files, HTML. (Note: ODBC support is provided only in the Enterprise Edition).
Drag-and-drop support for reading data; simply drop files into EViews for automatic conversion 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.
Frequency conversion and match merging support dynamic updating whenever underlying data change.
Auto-updating formula series that are automatically recalculated whenever underlying data change.
Auto-updating links that are automatically recalculated whenever underlying data changes on another workfile page which could be of different frequency or containing partial over-lapping data.
Resampling.
Random number generation (18 different distribution functions) and 3 types of random number generator.

Time Series Data Handling

Integrated support for handling dates and time series data.
Specialized time series functions and operators: lags, differences, log-differences, moving averages, etc.
Frequency conversion: various high-to-low and low-to-high.
Exponential smoothing: single, double, Holt-Winters.
Hodrick-Prescott filtering.
Band-pass (frequency) filtering: Baxter-King, Christiano-Fitzgerald fixed length and full sample asymmetric filters.
Seasonal adjustment: X11, X12-ARIMA, Tramo/Seats, moving average.

Statistics

Basic

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.
Unit root tests: Augmented Dickey-Fuller, GLS transformed Dickey-Fuller, Phillips-Perron, KPSS, Eliot-Richardson-Stock Point Optimal, Ng-Perron.
Johansen cointegration tests.
Granger causality tests.
Independence tests: Brock, Dechert, Scheinkman and LeBaron.

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.

Estimation

Regression

Linear and nonlinear ordinary least squares (multiple regression).
Linear regression with PDLs on any number of independent variables.
Analytic derivatives for nonlinear estimation.
Weighted least squares.
White and Newey-West robust standard errors.
Linear quantile regression and least absolute deviations (LAD), including both Huber’s Sandwich and bootstrapping covariance calculations.
Stepwise regression with 7 different selection procedures available.

Instrumental Variables and GMM

Linear and nonlinear two-stage least squares/instrumental variables (2SLS/IV) and Generalized Method of Moments (GMM) estimation.
White GMM weighting for cross section data.
HAC GMM weighting for time series data. HAC options including prewhitening, quadratic or Bartlett kernels, and fixed, Andrews, or Newey-West bandwidth selection methods.

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, or by conditional least squares.

ARCH/GARCH

GARCH(p,q), EGARCH, TARCH, Component GARCH, Power ARCH, Integrated GARCH.
The linear or nonlinear mean equation may include ARCH and ARMA terms; both the mean and variance equations allow for exogenous variables.
Normal, Student’s t, and Generalized Error Distributions.
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.
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.

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).
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.

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.

System of Equations

Basic

Linear and nonlinear estimation.
Least squares, 2SLS, equation weighted estimation, Seemingly Unrelated Regression, 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).

VAR/VEC

Estimate structural factorizations in VARs by imposing short- or long-run restrictions.
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 generator 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.
In- and out-of-sample forecasting, using n-step ahead or smoothed values.
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.
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.
Chow breakpoint and forecast tests, Quandt-Andrews unknown breakpoint test, Ramsey RESET tests, OLS recursive estimation.
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.
Easily save results (coefficients, coefficient covariance matrices, residuals, gradients, etc.) to EViews objects for further analysis.
<See also Estimation and Systems of Equations for 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.
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.
Extensive matrix support: matrix manipulation, multiplication, inversion, Kronecker products, eigenvalue solution, and singular value decomposition.

Improved Performance & Capacity

Nonlinear estimation, model solution, and other operations involving evaluation of series expressions are significantly faster since EViews now compiles expressions to native machine code.
Using Windows XP with the /3GB switch, Vista, or 64-bit XP or Vista, data capacity can be up to two and one-half times as large as under EViews 5.1.

Statistics and Econometrics Features

Statistics

EViews 6 features a new factor analysis object that allows you to: (1) compute covariances, correlations, or other measure of association (if necessary), (2) specify the number of factors, (3) obtain initial uniqueness estimates, (4) extract (estimate) factor loadings and uniquenesses, (5) examine diagnostics, (5) perform factor rotation, (6) estimate factor scores.

You may select from a menu of automatic methods for choosing the number of factors to be retained, or you may specify an arbitrary number of factors. You may estimate your model using principal factors, iterated principal factors, maximum likelihood, unweighted least squares, generalized least squares, and noniterative partitioned covariance estimation (PACE). Once you obtain initial estimates, rotations may be performed using any of more than 30 orthogonal and oblique methods, and factor scores may be estimated in more than a dozen ways.

Principal components analysis in EViews 6 has been greatly enhanced. You may now display line graphs of the ordered eigenvalues (screen plots), and examine scatterplots of the loadings and component scores (biplots). Loadings and component scores may now be computed with various weightings so that you may, for example, construct orthonormal or eigenvalue matching scores.
In addition to the previously supported ordinary (Pearson) correlations and covariances, you may now compute alternative measures of association: Spearman rank-order, Kendall's tau-a and tau-b, as well as partial correlations and covariances. EViews 6 now performs pairwise tests of zero correlation, with or without multiple comparison adjustments.
Mean equality tests (ANOVA) now perform tests both under the standard maintained assumption of equal variances across subgroups, and now, under the assumption that the variances are heteroskedastic (Welch 1951, Satterthwaite 1946).

Econometrics

General

Linear quantile regression and least absolute deviations (LAD) specifications (Koenker, 2005) may now be estimated. Asymptotic covariance matrices for the quantile regression estimates may be calculated assuming i.i.d. errors, Huber's Sandwich, or bootstrap methods. Specialized tools permit you to test for slope equality across quantile estimates (Koenker and Bassett, 1982), or to test for symmetry across quantile estimates (Newey and Powell, 1987).
EViews 6 provides stepwise regression tools for variable selection in ordinary least squares models. Among the methods and criteria that EViews supports are: undirectional-forwards, uni-directional-backwards, stepwise-forwards, stepwise backwards, swapwise-max R-squared increment, and combinatorial.
EViews 6 offers expanded heteroskedasticity testing (including Breusch-Pagan (1979), Godfrey (1978), Harvey (1978), Glejser (1969)), as well as the ability to specify custom tests in which you can test against departures from the homoskedastic null in a number of directions (say, by combining a White and Harvey test).
EViews 6 now offers the Quandt-Andrews Breakpoint Test (Andrews, 1993 and Andrews and Ploberger, 1994) which tests for one or more unknown structural breakpoints in an equation's sample.
The Binary, Count, Censored, and Ordered equation estimation methods now permit you to specify your equation by expression (instead of restricting you to providing a list). This flexibility allows you to construct non-linear index specifications, or models with coefficient restrictions.

Time-series

You may now perform cointegration tests with panel and pooled time series cross-section data using the panel cointegration statistics of Pedroni (2004), Pedroni (1999), and Kao (1999), or the Fisher-type test suggested by Maddala and Wu (1999).
EViews now estimates multivariate GARCH models, providing support for the most popular multivariate specifications: Conditional Constant Correlation, the Diagonal VECH and (indirectly) the Diagonal BEKK. You may estimate the model assuming multivariate normal or multivariate t-distribution errors. Once estimated, you may examine the fitted conditional covariances, variances, and correlations and save results to your workfile. In addition, you may perform residuals tests on the raw or standardized residuals, where the latter may be computed using various standardization methods.
EViews 6 allows you to estimate univariate integrated GARCH models that constrain the persistent parameters of univariate GARCH model to sum to unity. The constant term in a GARCH model can be restricted, or the variance targeted, so that the long run variance of the model equals to the sample variance of the data. Users may now choose the weight when backcasting is used to calculate the pre-sample variance.

Graphics

We have completely revamped our graphics engine, allowing you greater control over the display of data, and supporting the construction of categorical graphs.

Basic Features

Our all new graph specification interface offers control over almost every aspect of your graph, makes creating and customizing graphs a breeze.
New basic graph types: Dot plot, Area Band.
EViews 6 supports character labeling of axes using the workfile structure, with optional rotation of the label, along with symbol labeling in observation graphs.
Data may now be assigned to any axis (including bottom and top). Among other things, this allows you to produce rotated graphs.
Graphs may quickly and easily be displayed for summary statistics of your data (e.g., showing a bar graph of the mean values of each series in a group).
Histograms, boxplots, or kernel density graphs may be displayed in the margins of observation (line, bar, scatter, etc.) graphs.
EViews 6 offers a number of new univariate statistical graphs: histograms with options for controlling bins, frequency polygons, histogram edge polygons, average shifted histograms, fitted theoretical distribution plots (e.g., a normal density fit to sample data), empirical log survivor plots, confidence ellipses.
In addition, statistical graphs may now be overlaid on other graphs so that you may, for example, draw a kernel density and fitted normal distribution graph on top of a histogram, or you can overlay both a fitted linear regression line and a kernel regression plot on top of a scatterplot.
EViews 6 supports line graphs containing mixed frequency data.
You may now save EViews graph output in .bmp, .gif, .png, and .jpg formats.

Categorical Graph Tools

Categorical graph tools allow you to construct observation or analytical graphs formed using various subsets of the data, where the subsets are defined using the values of one or more categorical conditioning variables. Using these tools, you may quickly and easily perform complex tasks such as:

Displaying a bar plot comparing the mean incomes of individuals living in each state.
Producing a scatterplot of wages and hours worked, where the subset of males is drawn using one plotting symbol, and the subset of females uses a different symbol.
Showing wage-education profiles for both male and female workers.
Drawing histograms and boxplots of wages for union and non-union workers in different industries.

Customization Tools

You may now specify custom label elements for axes in frozen graphs.
You may now apply fade effects to fill colors in bars and backgrounds

Output Management

EViews 6 offers a new spool object that allows you to create collections of various EViews output. The EViews spool object is essentially a container that allows you to store multiple tables, graphs, text, and spools. Various management tools allow you to add, delete, extract, resize, annotate, hide and edit the objects in the spool.

You may find spools to be useful for organizing results, for example for creating a log of the results for a project or an EViews session, or perhaps for gathering output for a presentation.

Models

EViews 6 model solution may be up to 30 times faster than under EViews 5.1. Among the improvements:

A new solution algorithm has been added to models. Broyden's method is a quasi-Newton method that uses a secant approximation to the Jacobian instead of the true Jacobian when solving for the Newton step. The method has many of the desirable properties of Newton's method without requiring the Jacobian to be evaluated and factored at each step.
The model solver can now reorder equations within simultaneous blocks so that a set of variables in the block can be solved for recursively, conditional on the values of the remaining variables in the block. This structure is used by the Newton and Broyden solution algorithms to substantially reduce the time required to solve models consisting of large sparse systems of equations.
Stochastic simulations can now be based on bootstrapped residuals as an alternative to normally distributed random numbers. Bootstrapped residuals may be drawn independently for each equation, or may be drawn from the same period across all equations.
The complete set of results from each repetition of a stochastic simulation can now be saved as a new page in the workfile.
Equations for endogenous variables can now be excluded from the model (treated as exogenous variables) automatically based on whether actual values are available for the variable in each period. This makes it easy to perform forecasts using all available data when new data arrive at different dates.

Databases

Support has been added for direct access from within EViews to databases from Thomson Financials' Datastream (Thomson Datafeeds required), Moody's Economy.Com and FactSet, for users who are subscribers to these services. (Enterprise edition only.)
Series imported into workfiles from a database can now maintain a link to the source database, allowing the data to be refreshed from the database each time the workfile is opened, or upon user request.

Miscellaneous

EViews 6 provides over 100 new series expression functions, including new sets of functions for moving statistics (e.g., @movstdev), cumulative statistics (e.g., @cumstdev), and statistics on the rows of a group (e.g., @rmean, which computes the mean across the series in the group), financial calculations (various present value and rate calculations), ranks, and maximum likelihood and unbiased variance calculations.
New matrix language functions for various element operations (matrix element multiply divide, power), and for row and column scaling.
Series classification tools allow you to create classification variables based on the values in a series. You may use this to create custom "binning" of series, for example, using an income series to group observations into categories using a grid of income values, marginal tax brackets, or quantiles of income.
New functions allow you to start the Windows command shell or to spawn a process from within EViews.

EViews 6 Features

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