1. Overview of Forecasting

2. Two Approaches to Forecasting Forecasting Methods Model Based Judgmental (NB: Ch. 11) Using Survey Data (QMETH520) Using Past Data (QMETH530)

3. Past Data Time Series –Variables observed in equal time space –Frequency Daily, Weekly, Monthly, Quarterly, Yearly, etc.

4. Steps for Statistical Forecasting 1.Determine the variable(s) 2.Collect data Frequency Range 3.Develop a forecasting model (DGP) 4.Determine the forecast horizon 5.Determine the forecast statement

5. Data Sources Public –Links to several data sources available on the Courses Web Private

6. Forecast Horizon – h step ahead –Short run h small –Long run h large Statement –Point (unbiased and small se) –Interval (confidence level) –Density

7. Loss Functions L(e=y – pred_y) 00 LL ee

8. Example Variable: Japanese Yen per US Dollar Frequency: Monthly Data Range:1980: 1 – 2000: 3 Forecast Horizon: 2000: 4 - 2002: 7

9. Forecasting Model Statistical (scientific) forecast uses a “model” for determining the forecast statement. Model = Data Generating Process (DGP)

10. Standard Forecasting Models See the list in the syllabus

11. Modeling Process We do not reinvent a new wheel We “match data” with a “standard model” Data Standard Forecasting Models

12. Importance of Coverage Merit in learning a variety of forecasting models –Rather than mastering a one particular model For time series data –To cope with different types of “dynamics” Survey data –To cope with different types of “variables”

13. Variety of Dynamics Data = Trend + Season + Cycle + Irregular Irregular –Equal Variance –Unequal Variance

14. Implications of Using Standard Models Democratization of forecasting technology Transparency of forecasting process Identify the weaknesses of modeling –Imperfect model –Not enough observations –Contaminated data

15. Role of Software Graphical display of data –Guiding the choice of models Data Analysis: Matching Process –Fitting standard models supported in the software –Testing the adequacy of the models after fitting Forecast –Computing forecasts

16. Forecasting in Action Operations Planning and Control –Inventory management –sales force management –production planning, etc. Marketing –pricing decisions –advertisement expenditure decisions

17. Forecasting in Action - cont. Economics –macroeconomics variables –business cycles Business and Government Budgeting –revenue forecasting –expenditure forecasting Demography –population –immigration, emigration –incidence rate

18. Forecasting in Action - cont. Human Resource Management –employee performance Risk Management –credit scoring Financial Speculation –stock returns –interest rates –exchange rates

19. Models ComponentsForecasting Model Trend Fixed vs. Variable Season Fixed vs. Variables Cycle ARMA Irregular Random / GARCH

20. Statistical Thinking for Management Represent many others Data Information about a few customers, incidents Identify the relevant Process World Statistics not used Statistical methods needed