Time series analysis is a technique which has been utilized by econometricians and others for examining the relationship between events and time, particularly for forecasting purposes. More recent work has focused on time series analysis as a method to evaluate the effects of an exogenous event on a series. The major advantage of the interrupted time series design over a simple pre-post comparison is that the form of the change is taken into account. This paper will examine two alternative models for analyzing such data: regression and ARIMA. An example of the application of the two models will be demonstrated using data on highway deaths in North Carolina occurring before and after the national reduction in speed limits instituted in 1974. Conclusions are drawn about the comparative usefulness of these two techniques for program evaluation.
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Veney, J.E., Luckey, J.W. A comparison of regression and ARIMA models for assessing program effects: An application to the mandated highway speed limit reduction of 1974. Soc Indic Res 12, 83–105 (1983). https://doi.org/10.1007/BF00428862
- Time Series
- Recent Work
- Alternative Model
- Time Series Analysis
- Program Evaluation