EViews 13 Feature List

EViews offers a extensive array of powerful features for data handling, statistics and econometric analysis, forecasting and simulation, data presentation, and programming. While we can't possibly list everything, the following list offers a glimpse at the important 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, R data files, SAS® Transport files, SPSS native and portable files, Stata
  files, Tableau®, raw formatted ASCII text or binary files, HTML, or ODBC databases
  and queries (ODBC support is provided only in the Enterprise Edition).
• OLE support for linking EViews output, including tables and graphs, to other packages, including Microsoft Excel®, Word® and Powerpoint®.
• OLEDB support for reading EViews workfiles and databases using OLEDB-aware clients or custom programs.
• Support for FRED® (Federal Reserve Economic Data), DBNomics, World Bank, World Health Organization, OECD, United Nations SDMX, IMF SDMX,NOAA, US Census, US BEA, US BLS, ECB SDMX, EuroStat databases, plus many more!.
• Enterprise Edition support for Haver Analytics® DLX®, FAME, EcoWin, Bloomberg®, EIA®, CEIC®®, Datastream®, Trading Economics®, and Moody’s Economy.com databases
• The EViews Microsoft Excel® Add-in allows you to link or import data from EViews workfiles and databases from within Excel.
• Drag-and-drop support for reading data; simply drop files into EViews for automatic conversion and linking of foreign data and metadata into EViews workfile format.
• Powerful tools for creating new workfile pages from values and dates in existing series.
• Match merge, join, append, subset, resize, sort, and reshape (stack and unstack) workfiles.
• Easy-to-use automatic frequency conversion when copying or linking data between pages of different frequency.
• Frequency conversion and match merging support dynamic updating whenever underlying data change.
• Auto-updating formula series that are automatically recalculated whenever underlying data change.
• Easy-to-use frequency conversion: simply copy or link data between pages of different frequency.
• Tools for resampling and random number generation for simulation. Random number generation for 18 different distribution functions using three different random number generators.
• Support for cloud drive access, allowing you to open and save file directly to Dropbox, OneDrive, Google Drive and Box accounts.

TIME SERIES DATA HANDLING

• Integrated support for handling dates and time series data (both regular and irregular).
• Support for common regular frequency data (Annual, Semi-annual, Quarterly, Monthly, Bimonthly, Fortnight, Ten-day, Weekly, Daily - 5 day week, Daily - 7 day week).
• Support for high-frequency (intraday) data, allowing for hours, minutes, and seconds frequencies. In addition, there are a number of less commonly encountered regular frequencies, including Multi-year, Bimonthly, Fortnight, Ten-Day, and Daily with an arbitrary range of days of the week.
• Specialized time series functions and operators: lags, differences, log-differences, moving averages, etc.
• Frequency conversion: various high-to-low and low-to-high methods.
• Exponential smoothing: single, double, Holt-Winters, and ETS smoothing.
• Built-in tools for whitening regression.
• Hodrick-Prescott filtering.
• Band-pass (frequency) filtering: Baxter-King, Christiano-Fitzgerald fixed length and full sample asymmetric filters.
• Seasonal adjustment: Census X-13, STL Decomposition, MoveReg, X-12-ARIMA, Tramo/Seats, daily adjustment, moving average.
• Interpolation to fill in missing values within a series: Linear, Log-Linear, Catmull-Rom Spline, Cardinal Spline.
• Wavelets: transforms, decomposition of variance, outlier detection, and thresholding.

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.
• Granger causality tests, including panel Granger causality.
• Unit root tests: Augmented Dickey-Fuller, GLS transformed Dickey-Fuller, Phillips-Perron, KPSS, Eliot-Richardson-Stock Point Optimal, Ng-Perron, as well as tests for unit roots with breakpoints, and seasonal unit root tests.
• Cointegration tests: Johansen, Engle-Granger, Phillips-Ouliaris, Park added variables, and Hansen stability.
• Independence tests: Brock, Dechert, Scheinkman and LeBaron
• Variance ratio tests: Lo and MacKinlay, Kim wild bootstrap, Wright's rank, rank-score and sign-tests. Wald and multiple comparison variance ratio tests (Richardson and Smith, Chow and 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, PANIC, CIPS.
• Cointegration tests: Pedroni, Kao, Maddala and Wu.
• Panel within series covariances and principal components.
• Dumitrescu-Hurlin (2012) panel causality tests.
• Cross-section dependence tests.

ESTIMATION

Regression

• Linear and nonlinear ordinary least squares (multiple regression).
• Linear regression with PDLs on any number of independent variables.
• Robust regression.
• Analytic derivatives for nonlinear estimation.
• Weighted least squares.
• White and other heteroskedasticity consistent, and Newey-West robust standard errors. HAC standard errors may be computed using nonparametric kernel, parametric VARHAC, and prewhitened kernel methods, and allow for Andrews and Newey-West automatic bandwidth selection methods for kernel estimators, and information criteria based lag length selection methods for VARHAC and prewhitening estimation.
• Clustered standard errors.
• Linear quantile regression and least absolute deviations (LAD), including both Huber’s Sandwich and bootstrapping covariance calculations.
• Threshold regression including TAR and SETAR, and smooth threshold regression including STAR.
• ARDL estimation, including the Bounds Test approach to cointegration.
• Elastic net, ridge regression and LASSO estimation.
• Functional coefficient estimation.

Variable Selection and Machine Learning

• Stepwise regression with seven different selection procedures.
• LASSO variable selection.
• Elastic net, ridge regression and LASSO estimation.
• Auto-Search/GETS variable selection.
• Automatic ARIMA specification

ARMA and ARMAX

• Linear models with autoregressive moving average, seasonal autoregressive, and seasonal moving average errors.
• Nonlinear models with AR and SAR specifications.
• Estimation using the backcasting method of Box and Jenkins, conditional least squares, ML or GLS.
• Fractionally integrated ARFIMA models.

Instrumental Variables and GMM

• Linear and nonlinear two-stage least squares/instrumental variables (2SLS/IV) and Generalized Method of Moments (GMM) estimation.
• Linear and nonlinear 2SLS/IV estimation with AR and SAR errors.
• Limited Information Maximum Likelihood (LIML) and K-class estimation.
• Wide range of GMM weighting matrix specifications (White, HAC, User-provided) with control over weight matrix iteration.
• GMM estimation options include continuously updating estimation (CUE), and a host of new standard error options, including Windmeijer standard errors.
• IV/GMM specific diagnostics include Instrument Orthogonality Test, a Regressor Endogeneity Test, a Weak Instrument Test, and a GMM specific breakpoint test.

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.
• Fractionally integrated FIGARCH and FIEGARCH estimators.
• News Impact Curves.
• Stability tests and sign-bias tests.

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.
• Difference-in-Difference 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.