Time Series Cross-Sectional Regression Analysis
The TSCSREG procedure provides combined
time series cross-sectional regression analysis.
The TSCSREG procedure includes the following features:
- estimation of the regression parameters under several common error
structures:
- Fuller and Battese method (variance component model)
- Parks method (autoregressive model)
- Da Silva method (mixed variance component moving-average model)
- one-way fixed effects
- two-way fixed effects
- one-way random effects
- two-way random effects
- any number of model specifications
- unbalanced panel data for the fixed or random effects models
- variety of estimates and statistics including
- underlying error components estimates
- regression parameter estimates
- standard errors of estimates
- t-tests
- R-squared statistic
- correlation matrix of estimates
- covariance matrix of estimates
- autoregressive parameter estimate
- cross-sectional components estimates
- autocovariance estimates
- F-tests of linear hypotheses about the regression parameters
- specification tests
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
