Overview of Forecasting

Two Approaches to Forecasting

ForecastingMethods

Model Based

Judgmental(NB: Ch. 11)

Using Survey Data

(QMETH520)

UsingPast Data(QMETH530)

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

• 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 Web

• Private

Forecast

• Horizon – h step ahead– Short run h small– Long run h large

• Statement– Point (unbiased and small se)– Interval (confidence level)– Density

Loss Functions

• L(e=y – pred_y)

0 0

L L

ee

ExampleVariable: Japanese Yen per US DollarFrequency: MonthlyData Range:1980: 1 – 2000: 3Forecast Horizon: 2000: 4 - 2002: 7

0

40

80

120

160

200

240

280

84 86 88 90 92 94 96 98 00 02

EXJPUSEXJPUSF

LLUL

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

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 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

• Irregular– Equal Variance– Unequal Variance

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

• 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 management– production planning, etc.

• Marketing– pricing decisions– advertisement expenditure decisions

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 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

Irregular Random / GARCH

Statistical Thinking for Management

Represent many others Data

Information abouta few customers,

incidents

Identify the relevantProcess

World

Statistics not used

Statistical methods needed