EViews 7.2 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.
Single Equation Estimation
EViews allows you to choose from a full set of basic single equation estimators including: ordinary and nonlinear least squares (multiple regression), weighted least squares, two-stage least squares (instrumental variables), quantile regression (including least absolute deviations estimation), and stepwise linear regression. Weighted estimation is available for all of these techniques. Specifications may include polynomial lag structures on any number of independent variables.
For time series analysis, EViews estimates ARMA and ARMAX models, and a wide range of ARCH specifications. Structural time series models may be estimated using the state space object.
In addition to these basic estimators, EViews supports estimation and diagnostics for a variety of advanced models.
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, a variety of kernels, and fixed, Andrews, or Newey-West bandwith selection methods. You can estimate a GMM equation using either iterative procedures, or a continuously updating procedure. Post-estimation diagnostics for GMM equations, including weak instrument statistics, are also available.
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.
ARCH
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 Generalized 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.
Limited Dependent Variables
EViews also supports estimation of a range of limited dependent variable models. 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 generalized linear model or QML standard errors.
Panel Data Analysis and Pooled Time Series-Cross Section
EViews offers various panel and pooled data estimation methods. In addition to ordinary linear and non-linear least-squares, equation estimation methods include 2SLS/IV and Generalized 2SLS/IV, and GMM, which can be used to estimate complex dynamic panel data specifications (including Anderson-Hsiao and Arellano-Bond types of estimators).
Most of the methods allow for both time and cross-section fixed and random effects specifications. For random effects models, quadratic unbiased estimators of component variances include Swamy-Arora, Wallace-Hussain and Wansbeek-Kapteyn.
Also supported are AR specifications (any effects are defined after transformation), weighted least squares, and seemingly unrelated regression. In pools, coefficients for specific variables (including AR terms) can be constrained to be identical, or allowed to differ across cross-sections.
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.
You may impose and test linear restrictions on the cointegrating relations and/or adjustment coefficients. EViews' VARs also allow you to estimate structural factorizations (VARs) by imposing short-run (Sims 1986) or long-run (Blanchard and Quah 1989) restrictions. Over-identifying restrictions may be tested using the LR statistic reported by EViews.
VARs support a variety of views to allow you to examine the structure of your estimated specification. With a few clicks of the mouse, you can display the inverse roots of the characteristic AR polynomial, perform Granger causality and joint lag exclusion tests, evaluate various lag length criteria, view correlograms and autocorrelations, or perform various multivariate residual based diagnostics.
Multivariate ARCH
Multivariate ARCH is useful in modeling time varying variance and covariance of multiple time series. A number of popular ARCH models, such as the Conditional Constant Correlation (CCC), the Diagonal VECH, and the Diagonal BEKK, are available. Exogenous variables are allowed in the mean and variance equations; nonlinear and AR terms can be included in the mean equations. The error is assumed to distributed either as multivariate Normal or Student's t. Bollerslev-Wooldridge robust standard errors are also available. Once the model is estimated, users can easily generate the in-sample variance, covariance, or correlation, in tabular or graphic format.
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.
Sophisticated procs and views give you access to powerful filtering and smoothing tools so that you can view or generate one-step ahead, filtered, or smoothed signals, states, or errors. EViews' built-in forecasting procedures also provide easy-to-use tools for in- and out-of-sample forecasting using n-step ahead or smoothed values.
User-Defined Maximum Likelihood Estimation
For custom analysis, EViews' easy-to-use likelihood object permits estimation of user-specified maximum likelihood models. You simply provide standard EViews expressions to describe the log likelihood contributions for each observation in your sample, set coefficient starting values, and EViews will do the rest.