Overview of Forecasting Two Approaches to Forecasting Using

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Overview of Forecasting

Overview of Forecasting

Two Approaches to Forecasting Using Survey Data (QMETH 520) Model Based Using Past Data

Two Approaches to Forecasting Using Survey Data (QMETH 520) Model Based Using Past Data Forecasting Methods (QMETH 530) Judgmental (NB: Ch. 11)

Past Data • Time Series – Variables observed in equal time space – Frequency

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

Steps for Statistical Forecasting 1. Determine the variable(s) 2. Collect data • Frequency •

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

Data Sources • Public – Links to several data sources available on the Courses

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

Forecast • Horizon – h step ahead – Short run – Long run h

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

Loss Functions • L(e=y – pred_y) L 0 L e 0 e

Loss Functions • L(e=y – pred_y) L 0 L e 0 e

Example Variable: Japanese Yen per US Dollar Frequency: Monthly Data Range: 1980: 1 –

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

Forecasting Model • Statistical (scientific) forecast uses a “model” for determining the forecast statement.

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

Standard Forecasting Models • See the list in the syllabus

Standard Forecasting Models • See the list in the syllabus

Modeling Process • We do not reinvent a new wheel • We “match data”

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

Importance of Coverage • Merit in learning a variety of forecasting models – Rather

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”

Variety of Dynamics • Data = Trend + Season + Cycle + Irregular •

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

Implications of Using Standard Models • Democratization of forecasting technology • Transparency of forecasting

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

Role of Software • Graphical display of data – Guiding the choice of models

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

Forecasting in Action • Operations Planning and Control – Inventory management – sales force

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

Forecasting in Action - cont. • Economics – macroeconomics variables – business cycles •

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

Forecasting in Action - cont. • Human Resource Management – employee performance • Risk

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

Models Components Forecasting Model Trend Fixed vs. Variable Season Fixed vs. Variables Cycle ARMA

Models Components Forecasting Model Trend Fixed vs. Variable Season Fixed vs. Variables Cycle ARMA Irregular Random / GARCH

Statistical Thinking for Management World Data Information about a few customers, incidents Statistics not

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

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