Chapter 6

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MANAGERIAL ECONOMICS

MANAGERIAL ECONOMICS

12

12

thth

Edition

_Edition

By

By

Mark Hirschey

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Forecasting

Forecasting

Chapter 6

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Chapter 6

Chapter 6

OVERVIEW

OVERVIEW

 Forecasting Applications

 Qualitative Analysis

 Trend Analysis and Projection
 Business Cycle

 Exponential Smoothing
 Econometric Forecasting
 Judging Forecast Reliability

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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

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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.

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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

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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.

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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

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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

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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

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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

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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.

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