Chapter 6

 Used to forecast relatively stable activity.  Two-parameter Exponential Smoothing[r]

MANAGERIAL ECONOMICS

MANAGERIAL ECONOMICS

12

12thth Edition Edition

By

By

Mark Hirschey

Forecasting

Forecasting

Chapter 6

Chapter 6 Chapter 6 OVERVIEW OVERVIEW  Forecasting Applications

 Qualitative Analysis

 Trend Analysis and Projection  Business Cycle

 Exponential Smoothing  Econometric Forecasting  Judging Forecast Reliability

Chapter 6 Chapter 6

KEY CONCEPTS KEY CONCEPTS

 macroeconomic forecasting  microeconomic forecasting  qualitative analysis

 personal insight  panel consensus  delphi method  survey techniques  trend analysis  secular trend

 cyclical fluctuation  seasonality

 irregular or random influences  linear trend analysis

 growth trend analysis  business cycle

 economic indicators

 composite index  economic recession  economic expansion  exponential smoothing

 one-parameter (simple) exponential

smoothing

 two-parameter (Holt) exponential

smoothing

 three-parameter (Winters) exponential

smoothing

 econometric methods  identities

 behavioral equations  forecast reliability  test group

 forecast group

Forecasting Applications  Macroeconomic Applications

 Predictions of economic activity at the national or

international level, e.g., inflation or employment

 Microeconomic Applications

 Predictions of company and industry performance,

e.g., business profits

 Forecast Techniques

 Qualitative analysis

 Trend analysis and projection  Exponential smoothing

Qualitative Analysis

 Expert Opinion

 Informed personal insight is always useful  Panel consensus reconciles different views  Delphi method seeks informed consensus

 Survey Techniques

 Random samples give population profile  Stratified samples give detailed profiles of

Trend Analysis and Projection  Secular trends show fundamental patterns of

growth or decline

 Constant unit growth is linear

 Constant percentage growth is exponential

 Cyclical fluctuations show variation according to

macroeconomic conditions

 Cyclical normal goods have ε

I > 1, e.g., housing

 Seasonal variation due to weather or custom is

often important, e.g., summer demand for soda

Business Cycle

 The Business Cycle is a rhythmic pattern

of economic expansion and contraction.

 Economic indicators help forecast the

economy.

 Leading indicators, e.g., stock prices  Coincident indicators, e.g., production  Lagging indicators, e.g., unemployment

 Economic recessions are periods of

Exponential Smoothing

 One-parameter Exponential Smoothing

 Used to forecast relatively stable activity

 Two-parameter Exponential Smoothing

 Used to forecast relatively stable growth

 Three-parameter Exponential Smoothing

 Used to forecast irregular growth

 Practical Use of Exponential Smoothing

Econometric Forecasting

 Advantages of Econometric Methods

 Models can benefit from economic insight  Forecast error analysis can improve models

 Single Equation Models

 Show how Y depends on X variables

 Multiple-equation Systems

 Show how many Y variables depend on

Judging Forecast Reliability

 Tests of Predictive Capability

 Consistency between test and forecast

sample suggests predictive accuracy

 Correlation Analysis

 High correlation indicates predictive accuracy

 Sample Mean Forecast Error Analysis

 Low average forecast error points to

Choosing the Best Forecast Technique

 Data Requirements

 Scarce data mandates use of simple forecast

methods

 Complex methods require extensive data

 Time Horizon Problems

 Short-run versus long-run

 Role of Judgment

 Everybody forecasts