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ARIMA Forecasting: Variables without a Cause
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Michael Sack Elmaleh
Published/Copyright:
October 13, 2016
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.
Published Online: 2016-10-13
Published in Print: 2017-5-24
© 2017 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Research articles
- Valuation of a Company using Time Series Analysis
- The Impact of Default on Tax Shield Valuation
- An Independent Evaluation of the Reliability of the Implied Private Company Pricing Line Model in Appraisal Practice
- Patent Valuation with Forecasts of Forward Citations
- Takeover Premia and Leverage: Theory, Empirical Observations and Recommendations
- Note
- ARIMA Forecasting: Variables without a Cause
- Legal Case Study
- Are The Courts Unintentionally Promoting Unethical Behavior In Business Valuators?
Articles in the same Issue
- Frontmatter
- Research articles
- Valuation of a Company using Time Series Analysis
- The Impact of Default on Tax Shield Valuation
- An Independent Evaluation of the Reliability of the Implied Private Company Pricing Line Model in Appraisal Practice
- Patent Valuation with Forecasts of Forward Citations
- Takeover Premia and Leverage: Theory, Empirical Observations and Recommendations
- Note
- ARIMA Forecasting: Variables without a Cause
- Legal Case Study
- Are The Courts Unintentionally Promoting Unethical Behavior In Business Valuators?
