forecastingtechniques and and routes

1 Ch 3: Forecasting: Techniques and Routes Introduction Forecasting is the establishment of future expectations by the analysis of past data, or the formation of opinions. Forecasting is an essential element of capital budgeting. Capital budgeting requires the commitment of significant funds today in the hope of long term benefits. The role of forecasting is the estimation of these benefits. 2 Forecasting Techniques and Routes Technique s Routes Top-down route Bottom-up route Quantitative Qualitative Simple regressions Multiple regressions Time trends Moving averages Delphi method Nominal group technique Jury of executive opinion Scenario projection 3 Quantitative Forecasting Quantitative: Regression with related variable Data set of ‘Sales’ as related to both time and the number of households. YEAR HOUSEHOLDS SALES 1991 815 2109 1992 927 2530 1993 1020 2287 1994 987 3194 1995 1213 3785 1996 1149 3372 1997 1027 3698 1998 1324 3908 1999 1400 3725 2000 1295 4129 2001 1348 4532 2002 1422 4487 HISTORICAL DATA 4 Quantitative Forecasting Quantitative: Sales plotted related to households. SalesUnits Related to Number of Households 0 1000 2000 3000 4000 5000 0 500 1000 1500 Number of Households Sales Units Sales 5 Quantitative Forecasting Quantitative: Sales regressed on households. Edited output from the Excel regression. SUMMARY OUTPUT SALES REGRESSED AS A FUNCTION OF HOUSEHOLDS Regression Statistics Multiple R 0.824389811 R Square 0.67961856 Adjusted R Square 0.644020623 <== "Strength" of the regression Standard Error 429.2094572 Observations 11 Coefficients Standard Error t Stat P-value Y Axis Intercept -348.218 913.798 -0.381 0.712 Number of Households 3.316 0.759 4.369 0.002 6 Quantitative Forecasting Quantitative: Sales regressed on households. Predicting with the regression output. Regression equation is: Sales(for year) = -348.218 + ( 3.316 x households). Assuming that a separate data set forecasts the number of households at 1795 for the year 2006, then: Sales(year 2006) = -348.218 + ( 3.316 x 1795) = 5,604 units. 7 Quantitative Forecasting Quantitative: Multiple Regression Sales as a function of both time and the number of households. YEAR HOUSEHOLDS SALES 1991 815 2109 1992 927 2530 1993 1020 2287 1994 987 3194 1995 1213 3785 1996 1149 3372 1997 1027 3698 1998 1324 3908 1999 1400 3725 2000 1295 4129 2001 1348 4532 2002 1422 4487 HISTORICAL DATA 8 Quantitative Forecasting: Multiple Regression Line Information From the Excel spreadsheet. SUMMARY OUTPUT Regression Statistics Multiple R 0.9216 R Square 0.8494 Adjusted R Square 0.8118 <== "Strength" of regression. Standard Error 312.1217 Observations 11 Coefficients Standard Error t Stat P-value Lower 95% Y Axis Intercept -382643.9164 127299.584 -3.006 0.017 -676197.474 Calendar Year 193.3326 64.376 3.003 0.017 44.880 Households 0.1368 1.194 0.115 0.912 -2.616 MULTIPLE REGRESSION: SALES ON YEARS and HOUSEHOLDS 9 Quantitative Forecasting: Using Multiple Regression Multiple regression equation is: Sales in year = -382643.91 +(193.33 x Year) + (0.1368 x Households) Forecast of sales for the year 2005 is: Sales in year 2005 = -382643.91 + (193.33 x 2005) + (0.1368 x 1586) = 5200 Units (Note: the sales forecast relies upon a separate forecast of the number of households, given as 1 586, for 2005.) 10 Quantitative Forecasting Quantitative: Time Series Regression Sales plotted as a function of time. Plot of Past Sales Units By Year 0 1000 2000 3000 4000 5000 1990 1995 2000 2005 Year Sales Units Sales [...]... 16 Forecasting Routes Top-Down where international and national events affect the future behaviour of local variables 17 Forecasting Routes Where local events affect the future behaviour of local variables Bottom-Up 18 Forecasting: Summary  Sophisticated forecasting is essential for capital budgeting decisions  Quantitative forecasting uses historical data to establish relationships and trends which... EDITED SUMMARY OUTPUT REGRESSION OF SALES ON YEARS Regression Statistics Multiple R 0.9215 R Square 0.8492 Adjusted R Square 0.8324 . role of forecasting is the estimation of these benefits. 2 Forecasting Techniques and Routes Technique s Routes Top-down route Bottom-up route Quantitative Qualitative Simple regressions Multiple. automated trendline. 17 Forecasting Routes Top-Down where international and national events affect the future behaviour of local variables. 18 Forecasting Routes Bottom-Up Where local events. Square 0.644020623 <== "Strength" of the regression Standard Error 429.2094572 Observations 11 Coefficients Standard Error t Stat P-value Y Axis Intercept -348.218 913.798 -0.381