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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