Forecasting. Forecasting Application. Practical Forecasting. Chapter 7 OVERVIEW KEY CONCEPTS. Chapter 7. Chapter 7

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1 Forecastig Chapter 7 Chapter 7 OVERVIEW Forecastig Applicatios Qualitative Aalysis Tred Aalysis ad Projectio Busiess Cycle Expoetial Smoothig Ecoometric Forecastig Judgig Forecast Reliability Choosig the Best Forecast Techique 1 2 macroecoomic forecastig microecoomic forecastig qualitative aalysis persoal isight pael cosesus delphi method survey techiques tred aalysis secular tred cyclical fluctuatio seasoality irregular or radom iflueces liear tred aalysis growth tred aalysis busiess cycle ecoomic idicators Chapter 7 KEY CONCEPTS composite idex ecoomic recessio ecoomic expasio expoetial smoothig oe-parameter (simple) expoetial smoothig two-parameter (Holt) expoetial smoothig three-parameter (Witers) expoetial smoothig ecoometric methods idetities behavioral equatios forecast reliability test group forecast group sample mea forecast error Forecastig Applicatio Macroecoomic Applicatios Predictios of ecoomic activity at the atioal or iteratioal level Microecoomic Applicatios Predictios of compay ad idustry performace Forecast Techiques Qualitative aalysis Tred aalysis ad projectio Expoetial smoothig Ecoometric methods 3 4 Practical Forecastig POS ad sydicated data measure cosumer purchases from a retail outlet CUSTOMER ORDERS MANUFACTURER SHIPMENTS CUSTOMER HQ/BUYER CUSTOMER WAREHOUSE RETAIL STORE CONSUMER TAKEAWAY aka CONSUMPTION SELL-THROUGH SHOPPING HOUSEHOLD 5 6 1

2 Although both measure the same thig, there are some key differeces betwee POS ad sydicated data POS POS Sydicated (poit-of-sale) (poit-of-sale) (scaer) (scaer) Who supplies it Retailer 3 rd party vedors (IRI, ACNielse, etc) SOURCES How is sydicated data collected? Retailer POS System Ad Ad clearig I-store auditors (dollar (dollar sales, sales, uit uit sales) sales) house Measures available Chaels available Coverage Volume, (pricig) Idividual retailer All stores Volume, pricig, distributio, merchadisig Grocery, Drug, Club, C-Store, Mass Merch (excl Wal*Mart) Some cesus, some projected from sample Volume Price Feature Ads Displays Delivery lag time Processig required Varies (1 day mothly) Wide variatio 10 days mothly (ca ofte pay for faster for some chaels) Miimal Distributio more accurate less 7 8 Expert Opiio Qualitative Aalysis Iformed persoal isight is always useful Pael cosesus recociles differet views Delphi method seeks iformed cosesus Survey Techiques Radom samples give populatio profile Stratified samples give detailed profiles of populatio segmets Tred Aalysis ad Projectio Treds i Ecoomic Data Secular treds reflect growth ad declie Cyclical fluctuatios show rhythmic variatio Seasoal variatio (weather, custom) Radom iflueces are upredictable Liear Tred Aalysis Assumes Costat Period-by by-period Chage Illustrated i Figure 72 o Page 202 S t = a + bxt See page 200 Growth Tred Aalysis Comes i two versios Costat Rate of Growth Cotiuous (as opposed to aual) Compoudig See Table 71 Data, Pg 203 See Table 71 Data, Pg

3 Costat Aual Rate of Growth Estimated by usig a semi-log trasform Take the log of the depedet variable to the base 10 Assumes Aual compoudig See page 203/4 Cotiuous Compoudig Rate of Growth Estimated usig the semi-log trasform Take the atural log of the depedet variable (ie, to the base e) Assumes Cotiuous (ot aual) compoudig See page 204/5 See Table 71 Data, Pg 203 See Table 71 Data, Pg Liear Tred Aalysis Growth Tred Aalysis Liear ad Growth Tred Compariso Figure 72 Figure Figure 72 3

4 Busiess Cycle What Is the Busiess Cycle? Rhythmic patter of ecoomic expasio ad cotractio Ecoomic Idicators Useful leadig, coicidet ad laggig idicators help forecasters Ecoomic Recessios Periods of decliig ecoomic activity Sources of Forecast Iformatio Figure Idicators (Busiess Cycle Idicators) Developed by NBER Idicators are related to turig poits i busiess cycles Busiess Cycle defied as "expasios occurrig at about the same time i may ecoomic activities" See Pg 207 Idicators cotiued Leadig Idicators -lead turig poits Coicidet Idicators -are coicidet with turig poits Laggig Idicators -lag turig poits Turig poits are the key to forecastig with idicators Level of Ecoomic Activity Idicators <- Period -> Composite Composite Idexes Idexes of of Leadig, Leadig, Four Four Coicidet, Coicidet, ad ad Seve Seve Laggig Laggig Idicators Idicators (1987 ( ) 100) Time Source: The Coferece Board Web site at

5 Composite Idicators Idex of Leadig Idicators Curretly Curretly icludes 10 idicators: Average Workweek; Iitial Claims Uemploymet; New Orders Cosumer Goods; Vedor Performace; New Orders Capital Goods; Buildig Permits; Stock Prices; M2; Iterest Rate Spread; Idex of Cosumer Expectatios (See wwwtcb-idicatorsorg) See Page 209 Composite Idicators Idex of Coicidet Idicators Curretly icludes 4 idicators: Employees i oagricultural payrolls; Idustrial productio idex; Persoal icome less trasfer paymets; Maufacturig ad trade sales (See wwwtcb-idicatorsorg) See Page Composite Idicators Idex of Laggig Idicators Curretly icludes 7 idicators: Chage i labor cost; Ratio of ivetories to sales; Average duratio of uemploymet; Ratio cosumer istallmet credit to persoal icome; Commercial ad idustrial loas; Prime rate; Chage i CPI for services (See wwwtcb-idicatorsorg) See Page Expoetial Smoothig Oe-parameter Expoetial Smoothig Used to forecast relatively stable activity Two-parameter Expoetial Smoothig Used to forecast relatively stable growth Three-parameter Expoetial Smoothig Used to forecast irregular growth Practical Use of Expoetial Smoothig Techiques Simple Expoetial Smoothig The simple expoetial smoothig model ca be writte i the followig maer: Ft + 1 = wat + 1 w ( )Ft 31 Figure Ft + 1 = forecasted value for ext period w = The smoothig costat ( 0 < a < 1) At = Actual value of time series ow (i period t) Ft = Forecasted value for time t See Pg 215 5

6 Alpha Factor i Smoothig Alpha Factor i Smoothig 33 Time Calculatio Weight for X t t 1 t-1 9 X α = 1 t-2 9 X 9 X 1 9 X 9 X t-3 9 X 9 X 9 X Total = α=9 Time Calculatio Weight for X t t 9 t-1 1 X 9 09 =9 t-2 1 X 1 X t-3 1 X 1 X 1 X Total = Root Mea Square Error Root Mea Square Error is used to evaluate the relative accuracy of various forecastig methods; it is easy for most people to iterpret because of similarity to the basic statistical cocept of a stadard deviatio RMSE = ( At Ft) 2 t =1 36 Calculatig Root Mea Square Error Calculate the sum of the squared errors: ( At Ft) 2 t =1 Calculate the mea squared error: ( At Ft) 2 t =1 37 Calculatio cotiued Fially, take the square root of the mea sum of squared errors: RMSE = ( At Ft) 2 t =1 The smaller the RMSE, the "better" the forecast model RMSE ca be used to evaluate ay forecastig model 38 Smoothig Rule of Thumb I actual practice, alpha values from 005 to 030 work very well i most simple smoothig models If a value of greater tha 030 gives the best RMSE this usually idicates that aother forecastig techique would work eve better 6

7 Pros ad Cos of Smoothig Pros ad Cos of Smoothig Pros: Requires a limited amout of data Pros: Requires a limited amout of data Relatively simple compared to other forecastig methods Cos: Cos: Pros ad Cos of Smoothig Pros: Requires a limited amout of data Relatively simple compared to other forecastig methods Cos: Its forecasts lag behid actual data Pros ad Cos of Smoothig Pros: Requires a limited amout of data Relatively simple compared to other forecastig methods Cos: Its forecasts lag behid actual data No adjustmet for tred or seasoality (See Uemploymet ad Gapsales) Holts Expoetial Smoothig Witers Expoetial Smoothig Used for data exhibitig some tred over time Is just as simple to apply as simple smoothig Adjusts for both tred ad seasoality Is just as simple to apply as simple smoothig Ivolves the use of 3 smoothig parameters, simple smoothig parameter, tred smoothig parameter, ad seasoality smoothig parameter 43 (See Uemploymet ad Gapsales) See Pg (See Uemploymet ad Gapsales) See Pg 215 7

8 Ecoometric Forecastig Advatages of Ecoometric Methods Models ca beefit from ecoomic isight Forecast error isight ca improve models Sigle Equatio Models Show how Y depeds o X variables Multiple-equatio Systems Show how may Y variables deped o X variables Judgig Forecast Reliability Tests of Predictive Capability Cosistecy betwee test ad forecast sample suggest predictive accuracy Correlatio Aalysis High correlatio suggests predictive accuracy Sample Mea Forecast Error Aalysis Low average forecast error suggests predictive accuracy Ecoometric Forecastig Large Scale Macroecoomic Models Smaller Scale Idustry Models Idividual Product Demad Models Large Scale Macroecoomic Model (with oly 4 equatios) Behavorial Equatio (Cosumptio): C = a+b (GNP) Behavorial Equatios (I ad G): I = 400 G = 500 See Page 218 Idetity: GNP = C + I + G See See page page Multiple Multiple Equatio Equatio Systems Systems Large Scale Macroecoomic Model (with oly 4 equatios) 49 Substitute cosumptio equatio ito idetity: GNP = a + b(gnp ) + I 0 + G 0 Solve for GNP: 1 GNP = 1 b a + I 0 + G 0 ( ) Substitute regressio estimates ito model: 1 GNP = ( ) See See page page Multiple Multiple Equatio Equatio Systems Systems 50 Evaluatig the Model 1 GNP = ( ) GNP = 2, Whe ew values of Ivestmet ad govermet expeditures become available, the model may be evaluated agai New parameters are determied frequetly (See the Fairmodel at See See page page Multiple Multiple Equatio Equatio Systems Systems 8

9 Choosig the Best Forecast Techique Data Requiremets Scarce data madates use of simple forecast methods Complex methods require extesive data Time Horizo Problems Short-ru versus log-ru Role of Judgmet Everybody forecasts Better forecasts are useful Appropriate Forecast Techique Varies Varies Over Over Life Life Cycle Cycle of of Product Curve Curve fittig fittig Techiques, Techiques, Bass Bass Model Model Regressio Figure Regressio Models i Forecastig cotiued Accoutig for Seasoality Extesios of Multiple Regressio Forecastig Domestic Car Sales Forecastig Mickey Mouse (Case Chapter 7) 71 9