EViews 5.1 Features List

DATA HANDLING

BASIC:

TIME SERIES:

• Integrated support for handling dates and time series data.
• Specialized time series functions and operators: lags, differences, log-differ-ences, 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), 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).
• Covariances and correlations.
• Principal components.
• Empirical Distribution Function (EDF) Tests for the Normal, Exponential, Ex­treme value, Logistic, Chi-square, Weibull, or Gamma distributions (Kolmogo-rov-Smirnov, Lilliefors, Cramer-von Mises, Anderson-Darling, Watson).
• Histograms, CDF-survivor-quantile, Quantile-Quantile, kernel density, boxplots.
• Scatterplots with parametric and non-parametric regression lines (LOWESS, local polynomial), or kernel regression (Nadaraya-Watson, local linear, local polynomial).

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.

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.

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.
• 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.

SYSTEMS OF EQUATIONS

• 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).

VECTOR AUTOREGRESSION (VAR) / VECTOR ERROR CORRECTION (VEC) MODELS:

• 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.
• 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.

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 heteroskedasticity tests.
• Chow breakpoint and forecast tests, 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.