SAS/ETS software provides tools for a wide variety of applications in business, government, and academia. Major uses of SAS/ETS procedures are economic analysis, forecasting, economic and financial modeling, time series analysis, financial reporting, and manipulation of time series data.
The common theme relating the many applications of the software is time series data: SAS/ETS software is useful whenever it is necessary to analyze or predict processes that take place over time or to analyze models that involve simultaneous relationships.
Although SAS/ETS software is most closely associated with business, finance and economics, time series data also arise in many other fields. SAS/ETS software is useful whenever time dependencies, simultaneous relationships, or dynamic processes complicate data analysis. For example, an environmental quality study might use SAS/ETS software’s time series analysis tools to analyze pollution emissions data. A pharmacokinetic study might use SAS/ETS software’s features for nonlinear systems to model the dynamics of drug metabolism in different tissues.
The diversity of problems for which econometrics and time series analysis tools are needed is reflected in the applications reported by SAS users. The following listed items are some applications of SAS/ETS software presented by SAS users at past annual conferences of the SAS Users Group International (SUGI).
forecasting college enrollment (Calise and Earley 1997)
fitting a pharmacokinetic model (Morelock et al. 1995)
testing interaction effect in reducing sudden infant death syndrome (Fleming, Gibson, and Fleming 1996)
forecasting operational indices to measure productivity changes (McCarty 1994)
spectral decomposition and reconstruction of nuclear plant signals (Hoyer and Gross 1993)
estimating parameters for the constant-elasticity-of-substitution translog model (Hisnanick 1993)
applying econometric analysis for mass appraisal of real property (Amal and Weselowski 1993)
forecasting telephone usage data (Fishetti, Heathcote, and Perry 1993)
forecasting demand and utilization of inpatient hospital services (Hisnanick 1992)
using conditional demand estimation to determine electricity demand (Keshani and Taylor 1992)
estimating tree biomass for measurement of forestry yields (Parresol and Thomas 1991)
evaluating the theory of input separability in the production function of U.S. manufacturing (Hisnanick 1991)
forecasting dairy milk yields and composition (Benseman 1990)
predicting the gloss of coated aluminum products subject to weathering (Khan 1990)
learning curve analysis for predicting manufacturing costs of aircraft (Le Bouton 1989)
analyzing Dow Jones stock index trends (Early, Sweeney, and Zekavat 1989)
analyzing the usefulness of the composite index of leading economic indicators for forecasting the economy (Lin and Myers 1988)