Forecasting Using Long-Order Autoregressive Processes: An Example Using Housing Starts |
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| Authors: | Michael A Sklarz Norman G Miller Will Gersch |
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| Institution: | Locations Inc., 1339 Hunakai Street, Honolulu, Hawaii 96816.;College of Business Administration, University of Cincinnati, Cincinnati, Ohio 45221.;Department of Information and Computer Sciences, University of Hawaii, Honolulu, Hawaii 96822. |
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| Abstract: | A long autoregressive (AR) modeling procedure for monthly U.S. housing starts data is considered. Neither differencing to remove the trend, nor differencing to remove the seasonal component is required in this method. The model is fitted by a Householder transformation-Akaike AIC criterion algorithm. Forecast performance is compared to that obtained by the Box-Jenkins ARIMA method. The prediction error variance of the long AR model method tends to be smaller than the prediction error variance of the Box-Jenkins model method. The long AR method is well suited for housing market time-series which are characterized by both strong seasonal and slowly changing trend components. |
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