DRA/KV

Decision and Risk Analysis

Business Forecasting and Regression Analysis

Kiriakos VlahosSpring 2000

DRA/KVSession overview

• Why do we need forecasting?• Overview of forecasting techniques• The components of time series

– Trend– Seasonality– Cycles– Randomness

• Trend curves• Causal forecasting and regression

analysis• Judgemental forecasting• Scenario planning

DRA/KV

All forecasts are wrong

Those who claim to forecast the future

are all lying even if, by chance,

they are later proved right

DRA/KVForecasting is ...

Forecasting is like trying to drive a car

blindfolded and following directions

given by a person who is looking

out of the back window

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Forecasting in business

Forecasting in business is like sex in

society, we have to have it, we cannot

get along without it, everyone is doing

it, one way or another, but nobody is

sure he is doing it the best way.

G W PlosslLast Frontiers for Profits

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Forecasting in organisations

• Marketing

– Sales, prices, social and economic

trends• Production

– Demand, costs, employment and machinery requirements

• Finance– Costs, sales, capital expenditure,

economic climate• R&D

– Technological developments, new products

• Top management– Total sales, costs, pricing, economic

trends, competitors’ positioning

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Formal vrs. informal forecasting

• Forecasting is a very common activity• The majority of forecasting is informal

• Why do we need formal forecasting?– Coping with complexity– Coping with growth– Coping with change– Need for auditability and justification

Formal forecasting provides a vehicle for communication about the forecast and a basis for systematic improvement.

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Characteristics of forecasting problems

• Time horizon– short-term– long-term

• Data patterns– Seasonality– Trend– Cycles– Randomness

• Cost• Complexity• Accuracy

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Data patterns - Trend

Medium to long term movements

Upward or downward

e.g.

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

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Data patterns - cycles

Long-term irregular movements, e.g.

Government debt since the American revolution

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Data patterns -Seasonality

Regular periodic oscillations.

They can be monthly, quarterly, etc. e.g.

Jan-84 Jan-85 Jan-86 Jan-87 Jan-88

Turnover (£m)

Additive or multiplicative

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Data patterns - Random oscillations

Unsystematic, oscillations around a constant mean.

No trend cycle or seasonality

85

90

95

100

105

110

115

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Classification of forecasting methods

Trend C urves

Seasonal D ecom position

Exponentia l Sm oothing

Tim e Series

R egression

Econom etric forecasting

Track ing signals

C ausal Forecasting

Scenario p lanning

Q uantita tive

Ind ividual

C om m ittees

D elphi

Market surveys

R ole p laying

G roup

Judgem enta l

Forecasting m ethods

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Regression overview

• Why understanding relationships is important

• Visual tools for analysing relationships• Correlation

– Interpretation – Pitfalls

• Regression– Building models– Interpreting and evaluating models– Assessing model validity– Data transformations– Use of dummy variables

DRA/KV

Why analysing relationships is

important

• Development of theory in the social sciences and empirical testing

• Finance e.g.– How are stock prices affected by

market movements?– What is the impact of mergers on

stockholder value?• Marketing e.g.

– How effective are different types of advertising?

– Do promotions simply shift sales without affecting overall volume?

• Economics e.g.– How do interest rates affect

consumer behaviour?– How do exchange rates influence

imports and exports?

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Sales vrs advertising

Advertising (£000)

Sal

es (

unit

s)

DRA/KVEstimating betas

The slope of this line is called the beta of the stock and is an estimate of its market risk.

DRA/KVScatter plots

• What are they?

A graphical tool for examining the relationship between variables

• What are they good for?

For determining• Whether variables are related• the direction of the relationship• the type of relationship• the strength of the relationship

DRA/KVCorrelation

• What is it?

A measure of the strength of linear relationships between variables

• How to calculate?

a) Calculate standard deviations sx, sy

b) Calculate the correlation using the formula

• Possible values

From -1 to 1

yx

iii

xy ssN

yyxxr

)1(

))((

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Interpreting the correlation

DRA/KVCorrelation Pitfalls

• Correlation measures only linear relationships

• Existence of a relationship does not imply causality

• Even if there exists a causal relationship, the direction may not be obvious

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Correlation and Causality

Many nations see improving communications as vital to boost overall economy. A 1% increment in telephone density yields an increment of about 0.1% in per-capita GNP, according to a 1983 OECD-ITU study.

AT&T advertisement in Fortune Dec 97

DRA/KVFerric Processing

What are the factors influencing production costs?

Production costs

Capacity Plant age

Plantlocation

Other plantfeatures

Predicting production cost is important for the negotiation of 5-year contracts with steel companies

?

? ?

?

DRA/KVVisual inspection

10

15

20

25

30

0 0.5 1 1.5 2 2.5 3 3.5

capacity (000 tons/month)

cost

/ton

($)

a) Construct scatter plot

b) Calculate correlation (excel function CORREL)

The correlation between cost and capacity is -0.84

c) Candidate modelCost = a + b Capacity

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Simple Linear Regression

10

15

20

25

30

0 0.5 1 1.5 2 2.5 3 3.5

capacity (000 tons/month)

cost

/ton

($)

Simple regression estimates a linear equation which corresponds to straight line that passes through the data

Regression model

Cost = 25.2 - 4.4 Capacity

Dependent variable

Constant orintercept

Coefficientor slope

Independentor explanatoryvariable

DRA/KVLeast squares

10

15

20

25

30

0 0.5 1 1.5 2 2.5 3 3.5

capacity (000 tons/month)

cost

/ton

($)

Residuals

• Residuals are the vertical distances of the points from the regression line

• In least squares regression

– The sum of squared residuals is minimised

– The mean of residuals is zero

– residuals are assumed to be randomly distributed around the mean according to the normal distribution

DRA/KVExcel output

Regression StatisticsMultiple R 0.84R Square 0.70Adjusted R Square 0.66Standard Error 2.33Observations 10

ANOVAdf SS MS F Significance F

Regression 1 100.65 100.65 18.47 0.00Residual 8 43.59 5.45Total 9 144.23

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 25.19 1.86 13.55 0.00 20.91 29.48Capacity -4.40 1.02 -4.30 0.00 -6.77 -2.04

Read equation

Observe adjusted R2

Observe statisticssb

s

The standard error s is simply the st. deviation of the residuals (a measure of variability)

R2 is the most widely measure of goodness of fit.

It can be interpreted as the proportion of the variance of the dependent variable explained by the model. Use the adjusted R2 ,which accounts for the no. of observations.

variancevariabledependent

varianceresidual11

2

22

ys

sR

DRA/KVHypothesis testing

Does a relationship between capacity and cost really exist? If we draw a different sample, would we still see the same relationship?

Or in stats jargon

Is the slope significantly different from zero?

x

y b=0

b=0 implies no relationship between x and y

Hypothesis testingTest whether b=0

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t-values and p-values

0 b

p-value

t-value * sb

sb is the st. deviation of the slope estimate b

t-value = b/sb

p-value is the probability of getting an estimate of slope at least as large as b.

Equivalent tests (5% significance level)

|T-value| > 2

p-value < 0.05

Distribution of estimate of slope if b=0

DRA/KVChecking residuals

Residuals should be random. Any systematic pattern indicates that our model is incomplete.

Autocorrelated residuals

Heteroscedasticity

Problematic patterns

DRA/KVFerric - Residuals

Line fit Plot

10

15

20

25

30

0 1 2 3 4

Capacity

Co

st/

ton

Actual Predicted

Residual Plot

-4

-3

-2

-1

0

1

2

3

4

5

0 1 2 3 4

Capacity

Re

sid

ua

ls

Are residuals random?Can you see any pattern?

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Combining theory and judgement

The relationship appears to be non linear.

We can fit non-linear relationships by introducing suitable transformations, e.g.

x

y y=aebx

x

Ln(y)Ln(y)=ln(a)+bx

What transformation is appropriate for the Ferric data?

Use judgement e.g.

Total Cost (TC) = Fixed Cost + Variable Cost

TC = FC + Unit Cost (UC)* Quantity(Q)

TC/Q = FC/Q + UC e.g.

Average Cost = b/Q + a

This suggests that average costs are inversely proportionate to capacity

DRA/KV

Transforming the data

Regression StatisticsMultiple R 0.97R Square 0.95Adjusted R Square 0.94Standard Error 0.98Observations 10

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 11.75 0.60 19.53 0.00 10.36 13.131/Capacity 7.93 0.67 11.88 0.00 6.39 9.46

10

15

20

25

30

0 0.5 1 1.5 2 2.5 3 3.5

capacity (000 tons/month)

cost

/ton

($)

Line Fit Plot

10

15

20

25

30

0.00 0.50 1.00 1.50 2.00 2.50

1/Capacity

Cos

t/to

n

Actual

Predicted

DRA/KVModel comparison

• High adusted R2

• All coefficients significant– t-values or p-values

• Low standard error• No pattern in residuals• Is model supported by theory?• Does the model make sense?

Criteria First model Transformed modelHigh adjusted R2 66% 94%All coefficients significant Yes YesLow residual st. dev. (s) 2.33 0.98No pattern in residuals No YesEquation makes sense Yes (?) Yes

The transformed model is better:

Cost = 11.75 + 7.93 * (1/Capacity)

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Forecasting &confidence intervals

• If capacity is 2 what is the forecast for cost?– Cost = 11.75 + 7.93 (1/2) = 15.71

• Approximate 95% confidence interval:

15.71 2 * s

where s=0.98 is the standard error

• The greater the number of observations the better the approximation

• More accurate intervals can be calculated using statistical packages

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Confidence intervals

Plot of Fitted Model

1/CAPACITY

CO

ST

0 0.5 1 1.5 2 2.5 314

17

20

23

26

29

Statgraphics gives two sets of intervals.

• Outer bands are prediction intervals for an individual plant

• Inner bands are confidence intervals for the average cost from all plants. The can be viewed as the confidence intervals for the regression line.

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Is plant age important?

Multiple regression

Cost = a + b(1/Capacity)+ cYear + e

Regression StatisticsMultiple R 0.98R Square 0.96Adjusted R Square 0.95Standard Error 0.90Observations 10

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 542.01 326.41 1.66 0.14 -229.83 1313.84Year -0.27 0.16 -1.62 0.15 -0.66 0.121/Capacity 7.03 0.82 8.58 0.00 5.09 8.97

Cost/ton Year 1/CapacityCost/ton 1Year -0.74237 11/Capacity 0.9728 -0.67071 1

Correlation matrix

Regression analysis

Is this a good model?

DRA/KVMulticollinearity

87878685

8585

84

83

81

81

10

15

20

25

30

0 1 2 3 4

capacity (000 tons/month)

cost

/ton

($)

Multicollinearity appears when explanatory variables are highly correlated.

Effects:

• Including Year adds little information, hence fit does not improve much

• Parameter estimates become unreliable

Remedial action:

• Remove one of the correlated variables

Moral:

• Check for correlations between explanatory variables

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Other inappropriate models

Influential observations and outliers

Clustering of data

DRA/KVDummy variables

Bond purchases and national incomeYear B Y W1933 2.6 2.4 01934 3.0 2.8 01935 3.6 3.1 01936 3.7 3.4 01937 3.8 3.9 01938 4.1 4.0 01939 4.4 4.2 01940 7.1 5.1 11941 8.0 6.3 11942 8.9 8.1 11943 9.7 8.8 11944 10.2 9.6 11945 10.1 9.7 11946 7.9 9.6 01947 8.7 10.4 01948 9.1 12.0 01949 10.1 12.9 0

War

ye

ars

Regression equation: B = 1.29+.68Y+2.3W

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Regression checklist

• Visually inspect the data (scatter plots)

• Calculate correlations

• Develop and fit sensible model(s)

• Assess and compare the model(s)

– Significance of variables (t-values, p-values)

– adjusted R2

– standard error (s)

– residual plots

• autocorrelation

• heteroscedasticity

• Normality

• Outliers, influencial observations

– Does the model make sense?

• If you are satisfied use the model for

– developing business insights

– forecasting

DRA/KVTrend curves

• Also known as growth/decay curves• Most common curves

– Linear– Quadratic– Exponential– Logarithmic– S-curves

Fitting trend curvesTransform the original data so that a linear equation of the form y=a+bx arises. Then apply regression analysis.

Example:

tbaY

abY

t

tt

)log()log()log(

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Credit card turnover

1978 1981 1984 19870

2

4

6

8

10

12

14

16

£bn

Actual Predicted

Visa turnoverExponential Growth curve

How would you use such curve for forecasting?What role does judgement play in trend projection?

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Other trend curves (S curves)

Simple modifiedexponential

Logistic curveGompertz curve

Logarithmicparabola

0

b

abcY tt

01

1

cbe

Yctt

0

2

c

aeY ctbtt

0,0

cb

aeYctbe

t

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Trend and seasonality

Time Sales q1 q2 q3 q41 37.2 0 0 0 12 15.7 1 0 0 03 11 0 1 0 04 26.6 0 0 1 05 28.9 0 0 0 16 12 1 0 0 07 6.6 0 1 0 08 20.9 0 0 1 09 23.5 0 0 0 1

Quarterly data

Sales

0

510

1520

25

3035

40

0 10 20 30 40Quarters

$m

Regression with seasonal dummy variables

Sales = a + b Time + c q2 + d q3 + e q4

Include q1 in the model?

DRA/KV

Multiple regression with seasonal

dummiesRegression Statistics

Multiple R 0.95R Square 0.90Adjusted R Square 0.88Standard Error 2.96Observations 36.00

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%Intercept 14.78 1.31 11.30 0.00 12.11 17.44q2 -3.75 1.40 -2.69 0.01 -6.60 -0.91q3 8.53 1.40 6.10 0.00 5.68 11.38q4 15.66 1.40 11.23 0.00 12.82 18.51Time -0.25 0.05 -5.17 0.00 -0.34 -0.15

Equation: ?Interpretation: ?

Time Line Fit Plot

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Time

Sal

es

Sales

Predicted Sales

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Econometric modelling

Regression Analysis

Sales = f(GNP, price, advertising)

Econometric Modelling

Sales = f(GNP, price, advertising)

advertising = f(salest-1)

production cost = f(sales, labour cost, materials

cost)

price = f(production cost, price of substitutes)

exogenous - endogenous variables

Simultaneous parameter estimation

DRA/KVThe CEF model

• The CEF model of the UK economy– Agents

• Individuals• Banks• Other financial institutions• Government• Overseas agents

– Markets• Market for goods and services• Market for labour• Market for capital goods

• Agents interact in each market influencing supply and demand, which in turn determine price and quantities.– 500 equations (!)

DRA/KV

Judgemental forecasting

• Individual– Subjective probability assessment

• Group forecasting– Sales force method– Executive committees– Expert panels– Delphi method

• (Feedback, reassessment)

• Problems in judgemental forecasting– Bias– Anchoring– Conservatism/Optimism– Overconfidence

• Combining forecasts

DRA/KVForecasting change

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Crude price oil forecasts

The dangers of straight line forecasts

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Energy forecasting in West Germany

Energy Consumption - forecasts vrs. actual data

From Diefenbacher and Johnson“The politics of Energy Forecasting”

Persistence of mental models!

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Airline industry forecasts

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Forecasting & Planning

• Traditional view of forecasting– The past explains the future– Passive or adaptive attitude towards

the future

• Modern view– Active and creative approaches to

forecasting– Making things happen

DRA/KVScenario planning

“It is impossible to forecast the future and it may be dangerous to do so”

Use of scenarios in planning

Develop a small number of internally consistent and credible views of how the world will look in the future, that present testing conditions for the business.

The future will of course be different from all of these views/scenarios, but if the company is prepared to cope with any of them, it will be able to cope with the real world.

DRA/KVScenarios in Shell

Oil shock scenario:Shell analyse the impact

of a $15/bbl price on cashflows and investment plans

Oil shock scenario:Shell analyse the impact

of a $15/bbl price on cashflows and investment plans

Re-evaluation of up-streamplans and cash-flow positionof the operating companies

Re-evaluation of up-streamplans and cash-flow positionof the operating companies

Oil price fallsfrom $28/bbl to $10/bbl

Oil price fallsfrom $28/bbl to $10/bbl

Scenariodesign

StrategicPlan

Event

Early1985

Early1986

Result

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Advantages of Scenario Planning

• Challenge preconceived ideas and single point forecasts

• Explore a wide range of uncertainties

• Encourage an active and creative attitude to the future

• Provide a background for specific project evaluation

• Provide a vehicle for communication between the different parts of the organisation

DRA/KV

Forecasting - Summary

• All forecasts are wrong!• Never trust single point forecasts• Data patterns

– Trend, seasonality, cyclicality, randomness

• Time-series forecasting– Trend curves

• Causal forecasting– Regression

• Judgemental forecasting• Scenario planning

DRA/KV

Preparation for Regression workshop

• Read the note on Regression Analysis

• Work on the “Tutorial on Regression

Analysis using Excel”

• Practice on creating descriptive

statistics and histograms in Excel

(ExcelStats.xls)

• Select your workshop partner

• In preparation for the exam work on

regression exercises