Slide Chap 5

8/12/2019 Slide Chap 5

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Slide 1

Chapter 5

DR.NORAZNIN ABU BAKAR


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Slide 2

Business and Economic Forecasting

Demand Forecasting is a criticalmanagerial activity which comes in two

forms:

Quantitative Forecasting+2.1047%Gives the precise amountor percentage

Qualitative Forecasting Gives the expected direction

Up, down, or about the same


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Slide 3

What Went Wrong WithSUVs at Ford Motor Co?

Chrysler introduced the Minivan

in the 1980s

Ford expanded its capacity to produce theExplorer, its popular SUV

Explorers price was raised substantially in 1995

at same time competitors expanded theirofferings of SUVs.

Must consider response of rivals in pricing

decisions


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Slide 4

Significance of Forecasting

Both public and private enterprises operate under

conditions of uncertainty.

Management wishes to limit this uncertainty by

predicting changes in cost, price, sales, and

interest rates.

Accurate forecasting can help develop strategies

to promote profitable trends and to avoid

unprofitable ones. A forecast is a prediction concerning the future.

Good forecasting will reduce, but not eliminate, the

uncertainty that all managers feel.


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Slide 5

H ierarchy of Forecasts The selection of forecasting techniques depends in

part on the level of economic aggregation involved. The hierarchy of forecasting is:

National conomy(GDP, interest rates,inf lation, etc.)

sectors of the economy(durable goods)

industry forecasts(all automobile manufacturers)>firm forecasts(Ford Motor Company)

Product forecasts(The Ford Focus)


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Slide 6

Forecasting Criter iaThe choice of a particular forecasting method

depends on several criteria:

1.costsof the forecasting method compared withits gains

2.complexityof the relationships amongvariables

3.timeper iod involved4.accuracyneeded in forecast5.lead timebetween receiving information and

the decision to be made


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Accuracy of Forecasting

The accuracyof a forecasting model is measuredby how close the actual variable, Y, ends up to the

forecasting variable, Y.

Forecast erroris the difference. (Y - Y)

Models differ in accuracy, which is often based on

the square root of the average squared forecast

error over a series of N forecasts and actual figures

Called a root mean square error, RMSE.

RMSE = { (Y - Y)2

/ N }

^

^

^


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Quantitative Forecasting

Deterministic TimeSeries Looks For Patterns

Ordered by Time

No Underlying Structure

Econometric Models

Explains relationships Supply & Demand

Regression Models

Like technical

security analysis

Like fundamentalsecurity analysis


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Time Series

Examine Patterns in the Past

TIMETo

X

X

X

Dependent Variable

Secular TrendCyclical Variation

Forecasted Amounts

The data may offer secular trends, cyclical variations, seasonal

variations, and random fluctuations.


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Elementary Time Series Models

for Economic Forecasting

1. Naive Forecast

Yt+1

= Yt

Method best whenthere is no trend, onlyrandom error

Graphs of sales over

time with and withouttrends

When trending down,the Nave predicts toohigh

NO Trend

Trend

^

time

time


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2. Nave forecast with adjustments

for secular trendsYt+1 = Yt + (Yt - Yt-1 )

This equation begins with last periods

forecast, Yt. Plus an adjustment for the change in the

amount between periods Ytand Yt-1.

When the forecast is trending up, thisadjustment works better than the purenave forecast method #1.

^


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3. Linear & 4. Constant rate of growth

Used when trend has a

constant AMOUNT ofchange

Yt = a + bT, where

Yt are the actual

observations and

Tis a numerical time

variable

Used when trend is a

constantPERCENTAGE rate

Log Yt = a + bT,

whereb is the

continuouslycompounded growth

rate

Linear Trend Growth Uses a Semi-log Regression


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More on Constant Rate of Growth Model

a proof

Suppose: Yt= Y0( 1 + G)twhere g is the

annual growth rate

Take the natural log of both sides: Ln Yt= Ln Y0 + t Ln (1 + G)

but Ln ( 1 + G ) g, the equivalentcontinuously compounded growth rate

SO: Ln Yt= Ln Y0 + t g

Ln Yt= a + b twherebis the growth rate

^

^


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Numerical Examples: 6 observations

MTB > Print c1-c3.Sales Time Ln-sales

100.0 1 4.60517109.8 2 4.69866

121.6 3 4.80074

133.7 4 4.89560

146.2 5 4.98498

164.3 6 5.10169

Using this sales

data, estimate

sales in period 7

using a linear anda semi-log

functional

form


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The regression equation is

Sales = 85.0 + 12.7 Time

Predictor Coef Stdev t-ratio p

Constant 84.987 2.417 35.16 0.000

Time 12.6514 0.6207 20.38 0.000

s = 2.596 R-sq = 99.0% R-sq(adj) = 98.8%

The regression equation is

Ln-sales = 4.50 + 0.0982 Time

Predictor Coef Stdev t-ratio pConstant 4.50416 0.00642 701.35 0.000

Time 0.098183 0.001649 59.54 0.000

s = 0.006899 R-sq = 99.9% R-sq(adj) = 99.9%


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Slide 16

Forecasted Sales @ Time = 7 Linear Model Sales = 85.0 + 12.7 Time

Sales = 85.0 + 12.7( 7)

Sales = 173.9

Semi-Log Model Ln-sales = 4.50 + 0.0982

Time

Ln-sales = 4.50 +

0.0982( 7 )

Ln-sales = 5.1874

To anti-log:

e5.1874 = 179.0

linear


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Slide 17

Sales Time Ln-sales

100.0 1 4.60517

109.8 2 4.69866

121.6 3 4.80074133.7 4 4.89560

146.2 5 4.98498

164.3 6 5.10169

179.0 7 semi-log

173.9 7 linear

Which prediction

do you prefer?

Semi-logis

exponential

7


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Slide 18

5. Declining Rate of Growth Trend

A number of marketing penetration modelsuse a slight modification of the constant rate

of growth model In this form, the inverse of time is used

Ln Yt = b1b2( 1/t ) This form is good for patterns

like the one to the right

It grows, but at continuouslya declining rate time

Y


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Slide 19

6. Seasonal Adjustments: The Ratio to Trend Method

Take ratios of the actual to

the forecasted values for past

years.

Find the average ratio. Thisis the seasonal adjustment

Adjust by this percentage by

multiply your forecast by the

seasonal adjustment

If average ratio is 1.02, adjust

forecast upward 2%

12 quarters of data

I II III IV I II III IV I II III IV

Quarters designated with roman numerals.


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Slide 20

Let D = 1, if 4th quarter and 0otherwise Run a new regression:

Yt= a + bT + cD the c coefficient gives the amount of the adjustment for the

fourth quarter. It is an I ntercept Shi f ter.

With 4 quarters, there can be as many as three dummy variables;

with 12 months, there can be as many as 11 dummy variables

EXAMPLE: Sales = 300 + 10T + 18D12 Observationsfrom the first quarter of 2002 to 2004-IV.

Forecast all of 2005.

Sales(2005-I) = 430; Sales(2005-II) = 440; Sales(2005-III) = 450;

Sales(2005-IV) = 478

7.Seasonal Adjustments: Dummy Variables


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Slide 21

Soothing Techniques

8. Moving Averages

A smoothing forecast

method for data thatjumps around

Best when there is no

trend

3-Period Moving Ave.

Yt+1 = [Yt+ Yt-1+ Yt-2]/3

*

*

*

*

*Forecast

Line

TIME

Dependent Variable


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Slide 22

Smoothing Techniques

9. First-Order Exponential Smoothing

A hybrid of the Naive

and Moving Average

methods

Yt+1 = wYt+(1-w)Yt A weighted average of

past actual and pastforecast.

Each forecast is a function

of all past observations

Can show that forecast is

based on geometricallydeclining weights.

Yt+1 = w.Yt+(1-w)wYt-1+(1-w)2wYt-1 + Find lowest RMSE to pick

the best alpha.

^ ^

^

^


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Slide 23

First-Order Exponential

Smoothing Example for w= .50Actual Sales Forecast

100 100 initial seed required

120 .5(100) + .5(100) = 100

115

130

?

1

2

3

4

5


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Slide 24




120 .5(100) + .5(100) = 100

115 .5(120) + .5(100) = 110

130

?

1

2

3

4

5


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Slide 25




120 .5(100) + .5(100) = 100

115 .5(120) + .5(100) = 110

130 .5(115) + .5(110) = 112.50

? .5(130) + .5(112.50) = 121.25

Period 5

ForecastMSE = {(120-100)2+ (110-115)2+ (130-112.5)2}/3 = 243.75

RMSE = 243.75 = 15.61

1

2

3

4

5


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Slide 26

Direction of sales can be indicated by other variables.

TIME

Index of Capital Goods

peakPEAK Motor Control Sales

4 Months

Example: Index of Capital Goods is a leading indicator

There are also lagging indicators and coincident indicators

Qualitative Forecasting

10. Barometric Techniques


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Slide 27

LEADING INDICATORS*

M2 money supply (-12.4) S&P 500 stock prices (-11.1)

Building permits (-14.4)

Initial unemployment claims

(-12.9) Contracts and orders for

plant and equipment (-7.4)

COINCIDENT

INDICATORSNonagricultural payrolls

(+.8)

Index of industrial

production (-1.1)

Personal income less

transfer payment (-.4)

LAGGING INDICATORS Prime rate (+2.0)

Change in labor cost per unit

of output (+6.4)*Survey of Current Business, 1994See pages 206-207 in textbook

Time given in month s from change


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Slide 28

Handling Multiple Indicators

Diffusion Index: Wall Street With LouisRuykeyserhas eleven analysts. If 4 are

negative about stocks and 7 are positive, the

Diffusion Index is 7/11, or 63.3%.above 50% is a positive diffusion indexomposite Index: One indicator rises

4% and another rises 6%. Therefore, theComposite Index is a 5%increase.

used for quantitative forecasting


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Slide 29


11.Surveys and Opinion Polling Techniques

Sample bias-- telephone, magazine

Biased questions--

advocacy surveys

Ambiguous questions

Respondents may lie on

questionnaires

New Products have no

historical data -- Surveys

can assess interest in new

ideas.

Survey Research Center

of U. of Mich. does repeat

surveys of households on

Big Ticket items (Autos)

Common Survey Problems


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Slide 30


12. Expert OpinionThe average forecast from several experts

is a Consensus Forecast.

Mean Median

Mode

Truncated Mean Proportion positive or negative


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Slide 31

EXAMPLES:

IBES, First Call, and Zacks Investment --

earnings forecasts of stock analysts of companies

Conference Board macroeconomic predictions

Livingston Surveys--macroeconomic forecasts

of 50-60 economists

Individual economists tend to be lessaccurate over time than the consensus

forecast.


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Slide 32

13. Econometric Models Specify the variables in the model

Estimate the parameters

single equation or perhaps several stage methods

Qd = a + bP + cI + dPs+ ePc But forecasts require estimates for future prices,

future income, etc.

Often combine econometric models with time seriesestimates of the independent variable.

Garbage in Garbage out


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Slide 33

example

Qd = 400 - .5P + 2Y + .2Ps

anticipate pricing the good at P = $20

Income (Y) is growing over time, the estimate

is: Ln Yt= 2.4 + .03T, and next period is T

= 17. Y = e2.910= 18.357

The prices of substitutes are likely to be

P = $18. Find Qdby substituting in predictions for P, Y,

and Ps

Hence Qd = 430.31


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Slide 34

14. Stochastic Time Series A little more advanced methods incorporate into time

series the fact that economic data tends to drift

yt= a+ byt-1+ et In this series, if ais zero and bis 1, this is essentially

the nave model. When ais zero, the pattern is called arandom walk.

When ais positive, the data drift. The Durbin-Watson

statistic will generally show the presence ofautocorrelation, or AR(1), integrated of order one.

One solution to variables that drift, is to use first

differences.


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Cointegrated Time Series

Some econometric work includes several stochastic

variable, each which exhibits random walk with drift

Suppose price data (P) has positive drift

Suppose GDP data (Y) has positive drift

Suppose the sales is a function of P & Y

Salest= a + bPt+ cYt

It is likely that P and Y are cointegrated in that they

exhibit comovement with one another. They are notindependent.

The simplest solution is to change the form into first

differences as in: DSalest= a + bPt+ cYt

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