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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.
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