ARIMA Forecasting: Variables without a Cause

Sack Elmaleh Michael ()

Journal of Business Valuation and Economic Loss Analysis, 2017, vol. 12, issue 1, 141-143

Abstract: ARIMA forecast methods offer short term accuracy but have severe limitations in the appraisal context. ARIMA forecasts fail to identify or model causal variables, require more data points than are usually available and are very difficult to explain to non-statisticians. Better forecast alternatives are available to appraisers.

Keywords: explanatory clarity; ARIMA forecasts; causal variables (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/jbvela-2016-0009

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