Powerful Analytical Tools
In contrast with most
other econometric software, there is no reason for most users to learn
a complicated command language. EViews' built-in procedures are a
mouse-click away and provide the tools most frequently used in
practical econometric and forecasting work.
Basic Statistical Analysis
EViews supports a wide range of basic statistical analyses, encompassing everything from simple descriptive statistics to parametric and nonparametric hypothesis tests.
Basic descriptive
statistics are quickly and easily computed over an entire sample, by a
categorization based on one or more variables, or by cross-section or
period in panel or pooled data. Hypothesis tests on mean, median and
variance may be carried out, including testing against specific values,
testing for equality between series, or testing for equality within a
single series when classified by other variables (allowing you to
perform one-way ANOVA). Tools for covariance and factor analysis allow
you to examine the relationships between variables.
You can visualize the distribution of your data
using histograms, theoretical distribution, kernel density, or
cumulative distribution, survivor, and quantile plots. QQ-plots
(quantile-quantile plots) may be used to compare the distribution of a
pair of series, or the distribution of a single series against a
variety of theoretical distributions.
You can even perform
Kolmogorov-Smirnov, Liliefors, Cramer von Mises, and Anderson-Darling
tests to see whether your series is distributed normally, or whether it
comes from another distribution such as an exponential, extreme value,
logistic, chi-square, Weibull, or gamma distribution.
EViews also produces scatter plots with curve fitting using ordinary, transformation, kernel, and nearest neighbor regression.
Time Series Statistics and Tools
EViews provides
autocorrelation and partial autocorrelation functions, Q-statistics,
and cross-correlation functions, as well as unit root tests (ADF,
Phillips-Perron, KPSS, DFGLS, ERS, or Ng-Perron for single time
series and Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, Fisher, or Hadri
for panel data), cointegration tests (Johansen for with
MacKinnon-Haug-Michelis critical values and p-values ordinary data, and
Pedroni, Kao, or Fisher for panel data), causality, and independence
tests.
EViews also provides easy-to-use front-end support
for the U.S. Census Bureau's X11 and X12-ARIMA seasonal adjustment
programs, as well as the Tramo/Seats software, which is quite
frequently used by European researchers. Simple seasonal adjustment
using additive and multiplicative difference methods is also supported
in EViews.
You can even use EViews to compute trends and cycles from time series data using the Hodrick-Prescott filter, Baxter-King, Christiano-Fitzgerald fixed length and Christiano-Fitzgerald asymmetric full sample band-pass (frequency) filters.
Panel and Pooled Data Statistics and Tools
EViews features a wide
variety of tools designed to facilitate working with both panel or
pooled/time series-cross section data. Define panel structures with virtually no limit on
the number of cross-sections or groups, or on the number of periods or
observations in a group. Dated or undated, balanced or unbalanced, and
regular or irregular frequency panel data sets are all handled
naturally within the EViews framework.
Data structure tools
facilitate transforming your data from stacked (panel) to
unstacked (pooled) formats, and back again. Smart links, auto series,
and data extraction tools, allow you to slice, match merge, frequency
convert, and summarize your data with ease.
Support for basic
longitudinal data analysis ranges from convenient by-group and
by-period statistics, tests, and graphs, to sophisticated panel unit
root (Levin-Lin-Chu, Breitung, Im-Pesaran-Shin, or Fisher) and cointegration diagnostics (Pedroni (2004), Pedroni (1999), and Kao, or the Fisher-type test).
Specialized tools for displaying panel data graphs
allow you to view stacked, individual, or summary displays. Display
line graphs of each graph in a single graph frame or in individual
frames. Or show summary statistics for the panel data taken across
cross-sections, with mean (or median) and standard deviation (or
quantile) bands.
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.
Generalized Method of Moments (GMM)
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.
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 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.
System Estimation
EViews also offers powerful tools for analyzing systems of equations.
You may use EViews to 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 in most cases, 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.
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-Specified Maximum Likelihood
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.
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