EViews 6.0 Standard
Estimation
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.
ARCH models
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 Generalised 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.
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, either quadratic or Bartlett kernels, and fixed, Andrews,
or Newey-West bandwith selection methods.
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.
System Estimation
EViews supports estimation of both linear and nonlinear systems of
equations by OLS, two-stage least squares, seemingly unrelated
regression, three-stage least squares, GMM, and FIML. The system may
contain cross equation restrictions and autoregressive errors of any
order.
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.
Panel Data Analysis and Pooled Time Series-Cross Section
EViews features a wide variety of tools designed to facilitate working with panel or pooled/time series-cross section data. Unbalanced or balanced data sets with unlimited length time and/or cross-sections are easily analyzed. In addition to ordinary linear and non-linear least-squares, equation estimation methods include 2SLS/IV and Generalized 2SLS/IV, which can be used to estimate complex dynamic panel data estimation including Anderson-Hsiao and Arellano-Bond types of estimators.
State-Space Models
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.
User-Defined Maximum Likelihood Estimation
EViews 5 features an object (the LogL) for handling user-specified maximum likelihood estimation problems. Simply use standard EViews expressions to describe the log likelihood contribution of each observation in your sample, and EViews will do the rest.