10B11PD311 Economics. Process of predicting a future event on the basis of past as well as present knowledge and experience Underlying basis of all business

10B11PD311 Economics


• Process of predicting a future event on the basis of past as well as present knowledge and experience

• Underlying basis of all business decisions– Production– Inventory– Personnel– Facilities

• To reduce risk and uncertainty

• To take business decisions

• For planning

Sales will be $200 Million!


Setting the objective Selection and classification of goods Selection of method Interpreting the results


Purpose of Short- term ForecastingScheduling of production Inventory managementPrice strategySales strategyFinancial requirements

Purpose of Long- term ForecastingPlanning a new ProjectFinancial requirementsManpower

Capital Goods (Producer goods)- goods which help in further production of goods Replacement Demand New Demand

Information required Growth possibilities of industry demanding such

goods Life expectancy The norm of consumption

NATURE OF PRODUCT

Capital GoodsDurable

Consumer goodsNon durable

consumer goods


Durable consumer goods - consumer goods which can be used repeatedlyReplacement demandNew demand

Information requiredLife expectancy tablesPurchasing power Number of households/firmsExistence and growth of cooperating facilities


Non durable consumer goods - goods which can be used once

Information requiredDisposable income of consumerPrice of the product Price of related productsDemography


Time horizon Stability Data Availability Cost Accuracy Ease of Application


Qualitative Forecasts & Sources of Data

Consumer Survey method

Expert Opinion Method

Market Experiments

Complete enumeration

Sample survey

Composite Opinion Method

Forecasting MethodsQualitative Methods Quantitative

Methods


Iterative group process

3 types of people Decision makers Staff Experts

Proposes to reduce ‘group-think’

Demerits Expensive Time consuming Biased opinion

ExpertsExperts

Staff Staff

Decision MakersDecision Makers

(Sales?)

(Sales will be 50!)(What will

sales be? survey)

(Sales will be 45, 50, 55)


• Ask customers about purchasing plans

• What consumers say, and what they actually do are often different

• Sometimes difficult to answer

How many hours will you use the Internet

next week?

How many hours will you use the Internet

next week?


Direct method of assessing information from primary sources

Simple method

Insufficient Information Lack of time Biased information Utility limited to very short period There may be sampling error, if

sample is not properly chosen


• Each salesperson projects their sales• Combined at district & national levels• Sales rep’s know customers’ wants• Tends to be overly optimistic


• Involves small group of high-level managers– Group estimates demand by working

together• Combines managerial experience with

statistical models• Relatively quick• ‘Group-think’

disadvantage


Simple Based on first-hand knowledge of

salesman

Biased opinion Restricted to short-term forecasting


Can help in determining the demand function

ExpensiveTime consumingRiskyDifficult to satisfy the condition of homogeneity


Quantitative Forecasting Methods Quantitative

Forecasting

LinearRegression

AssociativeModels

ExponentialSmoothing

MovingAverage

Time SeriesModels

TrendProjection


Time -Series data - values of a variable arranged chronologically by days, weeks, months, quarters or yearsPast Values plotted on y- axisTime plotted on x- axis

Time Series analysis- attempts to forecast future values of the time series by examining past observations of the data

Assumption - past pattern will continue unchanged in the future


Time Series Sales Data


Secular Trend - long run increase or decrease in data series

Cyclical fluctuations - changes that recur over years

Seasonal variation - regularly recurring fluctuation

Irregular or random influences - variations resulting from unique events


Persistent, overall upward or downward pattern

Due to population, technology etc Several years duration

Mo., Qtr., Yr.

Response


Repeating up & down movements Due to interactions of factors influencing

economy Usually 2-10 years duration

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponse

Cycle


Regular pattern of up & down fluctuations Due to weather, customs etc Occurs within 1 year

Mo., Qtr.

Response

Summer


Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen

eventsUnion strikeCyclone

Short duration & nonrepeating

Time

Valu

es o

f D

ep

en

den

t V

ari

ab

le Trend Projection – Graphic Curve Fitting

Random InfluencesCyclical Fluctuation

Secular Trend

Time

Valu

es o

f D

ep

en

den

t V

ari

ab

le

Actual observation

Trend Projection – Graphic Curve Fitting

Deviation

Deviation

Deviation

Deviation

Deviation

Deviation

Deviation

Time

Valu

es o

f D

ep

en

den

t V

ari

ab

le

Point on the line

Actual observation


Deviation

Deviation

Deviation

Deviation

Deviation

Deviation

Deviation

TimeValu

es o

f D

ep

en

den

t V

ari

ab

le

bxaY ˆ

Point on the line

Actual observation



Assumptions

Relationship is assumed to be linear

Relationship is assumed to hold only within or slightly outside data range

Deviations around least squares line are assumed to be random



Projecting the past trend by fitting a straight line to the data

Constant Rate of Change

St = So + bt Where:

St = value of time series to be forecasted for period t

So = estimated value of time series in the base period

b = absolute amount of growth per period t = time period for which series is to be forecasted




S = nSo + b t S*t = So t + b t2


St = So + bt


22 )( ttnd

dtSttSSo /*2

dSttSnb /*

Where:


Period Year Quarter (t) Sales (S) S * t t^21996 I 1 300 300 11996 II 2 305 610 41996 III 3 315 945 91996 IV 4 340 1360 161997 I 5 346 1730 251997 II 6 352 2112 361997 III 7 364 2548 491997 IV 8 390 3120 641998 I 9 397 3573 811998 II 10 404 4040 1001998 III 11 418 4598 1211998 IV 12 445 5340 144

n = 12 78 4376 30276 650

St = So + bt

S = nSo + b t S*t = So t + b t2

St = 281.39 + 12.81t



200

250

300

350

400

450

500

0 2 4 6 8 10 12 14

1996 1997 1998

Sales per Quarter

Quarter


Seasonal Variation

Ratio to Trend Method

ActualTrend Forecast

Ratio =

SeasonalAdjustment =

Average of Ratios forEach Seasonal Period

AdjustedForecast =

TrendForecast

SeasonalAdjustment


Forecasted IV Actual IV AdjustedYear quarter sales quarter sales Ratio quarter sales

1996 332.64 340 1.022 339.291997 383.88 390 1.016 391.561998 435.13 445 1.023 443.83

Seasonal Adjustment 1.02

(Average)

Seasonal Adjustment using Ratio-Trend method


Limited to short term predictions

Fluctuation in economic growth are not considered

Assumes that historical relationships will not change


Predicting values of a time series on the basis of some average of its past values

Used when time series exhibit irregular or random variation

Moving Averages

Exponential Smoothing


Moving Average


Moving Average


Moving Average


Quarter Actual Market 3 Quarter Moving A - F (A - F)^2 5 Quarter MovingA - F (A - F)^2Share (A) Average(F) Average(F)

1 202 223 234 24 21.67 2.33 5.445 18 23.00 -5.00 25.006 23 21.67 1.33 1.78 21.40 1.60 2.567 19 21.67 -2.67 7.11 22.00 -3.00 9.008 17 20.00 -3.00 9.00 21.40 -4.40 19.369 22 19.67 2.33 5.44 20.20 1.80 3.24

10 23 19.33 3.67 13.44 19.80 3.20 10.2411 18 20.67 -2.67 7.11 20.80 -2.80 7.8412 23 21.00 2.00 4.00 19.80 3.20 10.24

78.33 62.48

13 21.33 (Forecast) 20.60

Moving Average


n

FA tt2)( RMSE =

To decide on the better moving average forecast calculate the root-mean-square error(RMSE) of each forecast and use the moving average which results in the smallest RMSE


Quarter Actual Market 3 Quarter Moving A - F (A - F)^2 5 Quarter Moving A - F (A - F)^2Share (A) Average(F) Average(F)

1 202 223 234 24 21.67 2.33 5.445 18 23.00 -5.00 25.006 23 21.67 1.33 1.78 21.40 1.60 2.567 19 21.67 -2.67 7.11 22.00 -3.00 9.008 17 20.00 -3.00 9.00 21.40 -4.40 19.369 22 19.67 2.33 5.44 20.20 1.80 3.24

10 23 19.33 3.67 13.44 19.80 3.20 10.2411 18 20.67 -2.67 7.11 20.80 -2.80 7.8412 23 21.00 2.00 4.00 19.80 3.20 10.24

78.33 62.48

13 21.33 20.60 (Forecast)

RMSE 2.95 2.99

Moving Average


Gives equal weightage to all observations in computing the average.


Forecast for next period (ie, t + 1) is a weighted average of the actual and forecasted values of the time series in period t

1 (1 )t t tF wA w F

0 1w


Exponential Forecasting


Exponential Forecasting1 (1 )t t tF wA w F

Mean of A


Quarter Actual Market Forecast with A - F (A - F)^2 Forecast with A - F (A - F)^2Share (A) w= 0.3 w= 0.5

1 20 21.0 -1.0 1.0 21.0 -1.0 1.02 22 20.7 1.3 1.7 20.5 1.5 2.33 23 21.1 1.9 3.6 21.3 1.8 3.14 24 21.7 2.3 5.5 22.1 1.9 3.55 18 22.4 -4.4 19.0 23.1 -5.1 25.66 23 21.1 1.9 3.8 20.5 2.5 6.17 19 21.6 -2.6 7.0 21.8 -2.8 7.68 17 20.8 -3.8 14.8 20.4 -3.4 11.49 22 19.7 2.3 5.3 18.7 3.3 10.9

10 23 20.4 2.6 6.8 20.3 2.7 7.011 18 21.2 -3.2 10.0 21.7 -3.7 13.512 23 20.2 2.8 7.7 19.8 3.2 10.0

252 86.3 102.1

13 21.1 21.4

RMSE = 2.68 2.92

Exponential Forecasting


Gives greater weight to recent data It is easy to update the forecasts No need to re-estimate the equations When time trend is positive, forecasts

are likely to be too low When time time trend is negative,

forecasts are likely to be too high


A time series that is correlated with another time series is called an indicator

Coincident indicators two series change at the same time

Leading indicators one series consistently occurs prior to changes in another series


Coincident indicator

Business Cycle Time

Ind

icato

r le

vel

Value

Economic Indicators

Leading indicator A

B

D

C


Must be accurate Provide adequate lead time Lead time should be constant Logical explanation why it is a leading

indicator Cost and time necessary for data

collection


Indices represent a single time series made up of a number of individual leading indicators.Purpose is to smooth out the random

fluctuations in each individual series.

Construction of an Index


Composite index- weighted average of individual indicators in each group. Good indicators are given more weightage. Index is interpreted in terms of

percentage change from period to period.

Diffusion Index- gives the percentage of the leading indicators that increase from one time period to the next.


Month Leading Leading LeadingIndicator I Indicator II Indicator II

1 400 30 1002 425 29 1103 460 33 135

The 1 month represents the base periodAll series to be given equal weightConstruct a composite & diffusion index


Composite Index:

[ 25/400 + (-1)/30 + 10/100] / 3 = 4.31

[ 60/400 + 3/30 +35/100] / 3 = 20

Month Diffusion Index Composite Index 1 - 100.00 2 66.7 104.31 3 100.00 120.00

Month Leading Leading LeadingIndicator I Indicator II Indicator II

1 400 30 1002 425 29 1103 460 33 135


Forecast turning points in the business cycles

Prediction record not perfect

Variability in lead time

Difficult to identify accurate indicators

Provides only qualitative forecast of turning

point

Documents

10B11PD311 Economics. Process of predicting a future event on the basis of past as well as present knowledge and experience Underlying basis of all business