This document provides an overview of various quantitative forecasting techniques, including moving averages, trend analysis, exponential smoothing, ARIMA models, and econometric models. It describes when each technique is best used, their advantages and disadvantages, and provides examples. The techniques range from simple methods like moving averages to more complex approaches like ARIMA and econometric models, with the key being choosing the right technique based on the characteristics of the data and forecasting needs.

1. Overview of Methods

2. Quantitative Techniques Moving Average Trend Analysis Exponential Smoothing ARIMA models Econometric models

3. Moving Average A simple average of the previous X months/years A six-month moving average forecast is an average of the previous six months

4. “I always avoid prophesying beforehand because it much better to prophesy after the event has already taken place.” Winston Churchill

5. Moving Average – When To Use Extremely “noisy” or little data Time constraint Degree of accuracy not important

6. Moving Average - Advantages Extremely simple Easy to implement

7. Moving Average - Disadvantages Not accurate; slow adjustment to changes in data Misses turning points All history is created equal

8. Moving Average Example

9. Trend Regression A straight ( or curved) line drawn through historical data “taking a ruler through your data”

10. “The best qualification of a prophet is to have a good memory.” Marquis of Halifax

11. Trend Regression – When To Use Steady rise or decline in data Time or software constraint Need easy explanation Little data

12. Trend Regression - Advantages Very simple Can be done in Excel

13. Trend Regression – Disadvantages Assumes future is exactly like past (prices, economy, etc.) All history is created equal One bad data point can greatly affect forecast

14. Trend Regression Example

15. Exponential Smoothing Simple Double (Brown) or Holt Winters

16. “A good forecaster is not smarter than everyone else, he merely has his ignorance better organized.” C. W. J. Granger

17. Simple Exponential Smoothing Weighted average of past values with exponentially decreasing weights Forecast this month equals last month’s forecast plus a proportion of the forecast error last month

18. Simple Exponential Smoothing – When To Use Stationary data with no trend or seasonality

19. Double (Brown) or Holt Exponential Smoothing Smooth the smoothed data with a weighted average of past values with exponentially decreasing weights Changes linearly with time (like linear regression) with recent data given more weight

20. Double (Brown) or Holt Exponential Smoothing – When to Use Data with a trend but no seasonality

21. Winter’s Exponential Smoothing Deseasonalize data, then find trend, then smooth

22. Winter’s Exponential Smoothing – When to Use Data with trend and seasonality

23. Exponential Smoothing Advantages Somewhat simple Recent data given more weight Fairly good accuracy for short-term forecasts Software can automate process

24. Exponential Smoothing - Disadvantages Requires forecasting software Bad data in recent month can cause great error in forecast Less accurate for medium to long-term forecasts Assumes history is like (recent) history

25. Exponential Smoothing Example

26. ARIMA (Box-Jenkins) Models A uto R egressive I ntegrative M oving A verage Autoregressive – future values depend on previous values of the data Moving average – future values depend on previous values of the errors Integrated – refers to differencing the data

27. “An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts – for support rather than illumination” - after Andrew Lang

28. ARIMA (Box-Jenkins) Models – When to Use Stable data that has regular correlations

29. ARIMA ( Box-Jenkins) Models - Advantages Outperforms exponential smoothing on homogenous and stable data Software can automate Sounds impressive

30. ARIMA (Box-Jenkins) Models - Disadvantages Requires software Needs a minimum of 40 data points Complicated to understand

31. ARIMA (Box-Jenkins) Models Example

32. Econometric Models Relates data series to explanatory variables Economists build demand models which relate Price, competition, income, population, etc.

33. “An economist is an expert who will know tomorrow why the things he predicted yesterday didn’t happen today.” Evan Esar

34. Econometric Models – When to Use Important to understand market Influences on product demand are changing Historically more acceptable in regulation

35. Econometric Models - Advantages Can give price elasticity Formally integrates economic impact Permits varied assumptions, i.e., “what if?” Forces you to make assumptions explicit Methods to deal with short time

36. Econometric Models - Disadvantages Large data gathering Expertise to build Requires forecasts of explanatory variables Not always best forecasting technique

37. Econometric Models Example

38. Res. DA Model #DA calls per person = 4.49-0.18*Price +1.04*Income per person+0.00016*Timetrend

39. Summary Graph data Choose appropriate technique for Output Time Data Know advantages and disadvantages