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Time
Series
Features
- Exact full information
maximum likelihood (FIML) estimation of VARMAX
and VARMA, ARIMAX, ARIMA, ECM models.
Impose general linear and nonlinearand equality and inequality constraints on
the parameters.
Find Lagrangeanvalues associated with each constraint. Return ACF indicator matrices,
together with other summary information, including Akaike, Schwarz, and
Bayesian information criteria. Compute forecasts from VARMAX and VARMAmodels.
- Exact maximum likelihood
estimation of ECM models.
Unit root and cointegration tests, DF, ADF, Phillips-Perron, and Johansen's
Trace and Maximum Eigenvalue tests.
- Estimation of VAR
models.
Compute parameter estimates and standard errors for a regression model
with autoregressive errors. Can be used for models for which the
Cochrane-Orcutt or similar procedures are used. Also computes autocovariances
and autocorrelations of the error term.
- ARIMA Models
The Time Series module includes tools for estimating general ARIMA (p,d,q)
models using an exact MLE procedure based on C. Ansley (Biometrika 1979,
pp. 59-65). Procedures for computing forecasts, theoretical autocovariances,
sample autocorrelations, and partial autocorrelations (using Durbin's
algorithm), as well as for simulating ARIMA models are provided.
- Time-Series Cross-Sectional
Regression Models: TSCS
This module provides procedures to compute estimates for "pooled time-series
cross-sectional" models. The assumption is that there are multiple observations
over time on a set of cross-sectional units (e.g., people, firms, countries).
For example, the analyst
may have data for a cross-section of individuals each
measured over 10 time periods. While these models were devised to study a
cross-section of units over multiple time periods, they also correspond to
models in which there are data for groups such as schools or firms with
measurements on multiple observations within the group (e.g., students,
teachers, employees).
The specific model that
can be estimated with this program is a regression
model with variable intercepts. That is, a model with individual-specific
effects. The regression parameters for the exogenous variables are assumed
to be constant across cross-sectional units. The intercept varies across
individuals. This program provides three estimators:
- Fixed-effects OLS estimator
(analysis of covariance estimator)
- Constrained OLS estimator
- Random effects estimator
using GLS
A Hausman test is computed
to show whether the error components (random effects)
model is the correct specification. In addition to providing the analysis of
computed. The first partial squared correlation shows the percentage of
variation in the dependent variable that can be explained by the set of
independent variables while holding constant the group variables. The second
shows the extent to which variation in the dependent variable can be accounted
for by the group variable after the other independent variables have been
statistically held constant.
A key feature of this program
is that it allows for a variable number of
time-series observations per cross-sectional unit. For instance, there might
be 5 time-series observations for the first individual, 10 for the second, and
so on. This is useful when there are missing values.
Platform: Windows, LINUX and UNIX.
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