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.

Requirements: GAUSS/GAUSS Light version 3.6.16 or higher.