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Regression models are commonly applied when it comes to planning or forecasting. Financial data - due to their historical features of the trend component, cyclical component, and irregular component - might not fit well with traditional regression models. In such cases, an autoregression model or a dynamic regression model will be the best option. The autoregression model estimates and forecasts linear regression models for time series data when the errors are autocorrelated. PROC AUTOREG is used to adjust for autocorrelation, but it does not support differencing/integration or models that include moving average terms. For those models, PROC ARIMA is widely adopted. This paper demonstrates how to select the best model using SAS with a practical example in financial industry.

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Format:PDFSize:289 KB
Date:May 2007
Pages:12

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