Ch. 5-demand-forecasting(2)

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Microeconomics-Salvatore

Transcript of Ch. 5-demand-forecasting(2)

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PowerPoint Slides Prepared by Robert F. Brooker, Ph.D. Copyright 2007 by Oxford University Press, Inc. (Adapted by OUP India, 2008) Slide 1

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Qualitative Forecasts

• Survey Techniques– Planned Plant and Equipment Spending– Expected Sales and Inventory Changes– Consumers’ Expenditure Plans

• Opinion Polls– Business Executives– Sales Force– Consumer Intentions

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Time-Series Analysis

• Secular Trend– Long-Run Increase or Decrease in Data

• Cyclical Fluctuations– Long-Run Cycles of Expansion and Contraction

• Seasonal Variation– Regularly Occurring Fluctuations

• Irregular or Random Influences

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Trend Projection

• Linear Trend:St = S0 + b tb = Growth per time period

• Constant Growth RateSt = S0 (1 + g)t

g = Growth rate• Estimation of Growth Rate

lnSt = lnS0 + t ln(1 + g)

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Seasonal Variation

Ratio to Trend Method

ActualTrend ForecastRatio =

SeasonalAdjustment =

Average of Ratios forEach Seasonal Period

AdjustedForecast =

TrendForecast

SeasonalAdjustment

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Seasonal Variation

Ratio to Trend Method:Example Calculation for Quarter 1

Trend Forecast for 1996.1 = 11.90 + (0.394)(17) = 18.60

Seasonally Adjusted Forecast for 1996.1 = (18.60)(0.8869) = 16.50

YearTrend

Forecast Actual Ratio1992.1 12.29 11.00 0.89501993.1 13.87 12.00 0.86521994.1 15.45 14.00 0.90611995.1 17.02 15.00 0.8813

Seasonal Adjustment = 0.8869

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Moving Average Forecasts

Forecast is the average of data from w periods prior to the forecast data point.

1

wt i

ti

AF

w

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Exponential SmoothingForecasts

1 (1 )t t tF wA w F

Forecast is the weighted average of of the forecast and the actual value from the prior period.

0 1w

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Root Mean Square Error

2( )t tA FRMSE

n

Measures the Accuracy of a Forecasting Method

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Barometric Methods

• National Bureau of Economic Research• Department of Commerce• Leading Indicators• Lagging Indicators• Coincident Indicators• Composite Index• Diffusion Index

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Econometric Models

Single Equation Model of the Demand for Cereal (Good X)

QX = a0 + a1PX + a2Y + a3N + a4PS + a5PC + a6A + e

QX = Quantity of X

PX = Price of Good X

Y = Consumer Income

N = Size of Population

PS = Price of Muffins

PC = Price of Milk

A = Advertising

e = Random Error

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Econometric Models

Multiple Equation Model of GNP

1 1 1t t tC a bGNP u

2 2 1 2t t tI a b u

t t t tGNP C I G

2 11 21

1 11 1 1t t

t

b Ga aGNP b

b b

Reduced Form Equation

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Input-Output Forecasting

Producing IndustrySupplying Industry A B C

Final Demand Total

A 20 60 30 90 200B 80 90 20 110 300C 40 30 10 20 100Value Added 60 120 40 220Total 200 300 100 220

Three-Sector Input-Output Flow Table

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Input-Output Forecasting

Direct Requirements Matrix

Producing IndustrySupplying Industry A B C

A 0.1 0.2 0.3B 0.4 0.3 0.2C 0.2 0.1 0.1

DirectRequirements

Input RequirementsColumn Total

=

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Input-Output Forecasting

Total Requirements Matrix

Producing IndustrySupplying Industry A B C

A 1.47 0.51 0.60B 0.96 1.81 0.72C 0.43 0.31 1.33

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Input-Output Forecasting

1.47 0.51 0.600.96 1.81 0.720.43 0.31 1.33

9011020

=200300100

Total Requirements

Matrix

Final Demand Vector

Total Demand Vector

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Input-Output Forecasting

Revised Input-Output Flow Table

Producing IndustrySupplying Industry A B C

Final Demand Total

A 22 62 31 100 215B 88 93 21 110 310C 43 31 10 20 104

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Chapter 5 Appendix

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