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Overview of Forecasting

Overview of Forecasting. Two Approaches to Forecasting. Using Survey Data (QMETH520). Model Based. Using Past Data (QMETH530). Forecasting Methods. Judgmental (NB: Ch. 11). Past Data. Time Series Variables observed in equal time space Frequency

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Overview of Forecasting

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  1. Overview of Forecasting

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

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

  4. Steps for Statistical Forecasting • Determine the variable(s) • Collect data • Frequency • Range • Develop a forecasting model (DGP) • Determine the forecast horizon • 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) L L e e 0 0

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

  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

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

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