chapter fivecopyright 2009 pearson education, inc. publishing as prentice hall. 1 chapter 5 demand...

Chapter Five Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall.

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Chapter 5

Demand Estimationand Forecasting


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Overview

Regression analysisHazards with use of regression analysisSubjects of forecastsPrerequisites of a good forecastForecasting techniques


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Learning objectives

understand importance of forecasting in business

describe six different forecasting techniques

know how to specify and interpret a regression


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Learning objectives

recognize limitations of consumer data

use seasonal and smoothing methods


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Data collection Data for studies pertaining to countries,

regions, or industries are readily available

Data for analysis of specific product categories may be more difficult to obtain buy from data providers (e.g. ACNielsen,

IRI) perform a consumer survey focus groups technology: point-of-sale, bar codes


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Regression analysis Regression analysis: a procedure

commonly used by economists to estimate consumer demand with available data

Two types of regression: cross-sectional: analyze several

variables for a single period of time time series data: analyze a single

variable over multiple periods of time


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Regression analysis Regression equation: linear, additive

eg: Y = a + b1X1 + b2X2 + b3X3 + b4X4

Y: dependent variablea: constant value, y-interceptXn: independent variables, used to explain Y

bn: regression coefficients (measure impact of

independent variables)


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Regression analysis Interpreting the regression results: coefficients:

negative coefficient shows that as the independent variable (Xn) changes, the variable (Y) changes in the opposite direction

positive coefficient shows that as the independent variable (Xn) changes, the dependent variable (Y) changes in the same direction

magnitude of regression coefficients is a measure of elasticity of each variable


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Regression analysis Statistical evaluation of regression results:

t-test: test of statistical significance of each estimated coefficient

b = estimated coefficient SEb = standard error of estimated coefficient

b̂SE

b̂ t


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Regression analysis Statistical evaluation of regression results:

‘rule of 2’: if absolute value of t is greater than 2, estimated coefficient is significant at the 5% level

if coefficient passes t-test, the variable has a true impact on demand


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Regression analysis Statistical evaluation of regression results

R2 (coefficient of determination): percentage of variation in the variable (Y) accounted for by variation in all explanatory variables (Xn)

R2 value ranges from 0.0 to 1.0 the closer to 1.0, the greater the

explanatory power of the regression


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Regression analysis Statistical evaluation of regression results

F-test: measures statistical significance of the entire regression as a whole (not each coefficient)


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Regression results

Steps for analyzing regression results

check coefficient signs and magnitudes

compute implied elasticities

determine statistical significance


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Regression results

Example: estimating demand for pizza

demand for pizza affected by 1. price of pizza 2. price of complement (soda) managers can expect price decreases to

lead to lower revenue tuition and location are not significant


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Regression problems

Identification problem: the estimation of demand may produce biased results due to simultaneous shifting of supply and demand curves

solution: use advanced correction

techniques, such as two-stage least squares and indirect least squares


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Regression problems

Multicollinearity problem: two or more independent variables are highly correlated, thus it is difficult to separate the effect each has on the dependent variable

solution: a standard remedy is to drop one of the closely related independent variables from the regression


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Forecasting Examples: common subjects of business

forecasts:• gross domestic product (GDP)• components of GDP

eg consumption expenditure, producer durable equipment expenditure, residential construction

• industry forecasts eg sales of products across an industry

• sales of a specific product


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Forecasting

A good forecast should:

be consistent with other parts of the business be based on knowledge of the relevant past consider the economic and political

environment as well as changes be timely


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Forecasting techniques

Factors in choosing the right forecasting technique:

item to be forecast interaction of the situation with the forecasting

methodology amount of historical data available time allowed to prepare forecast


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Approaches to forecasting

qualitative forecasting is based on judgments expressed by individuals or group

quantitative forecasting utilizes significant amounts of data and equations


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Approaches to forecasting

naïve forecasting projects past data without explaining future trends

causal (or explanatory) forecasting attempts to explain the functional relationships between the dependent variable and the independent variables


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Forecasting techniques Six forecasting techniques

expert opinion opinion polls and market research surveys of spending plans economic indicators projections econometric models


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Forecasting techniques Market research: is closely related to

opinion polling and will indicate not only why the consumer is (or is not) buying, but also

who the consumer is how he or she is using the product characteristics the consumer thinks are

most important in the purchasing decision


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Forecasting techniques Surveys of spending plans: yields

information about ‘macro-type’ data relating to the economy, especially:

consumer intentions Examples: Survey of Consumers (University of

Michigan); Consumer Confidence Survey (Conference Board)

inventories and sales expectations


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Economic indicators: a barometric method of forecasting designed to alert business to changes in conditions

leading, coincident, and lagging indicators

composite index: one indicator alone may not be very reliable, but a mix of leading indicators may be effective


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Forecasting techniques Leading indicators predict future economic

activity

average hours, manufacturing initial claims for unemployment

insurance manufacturers’ new orders for consumer

goods and materials vendor performance, slower deliveries

diffusion index manufacturers’ new orders, nondefense

capital goods


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Forecasting techniques Leading indicators predict future economic

activity

building permits, new private housing units

stock prices, 500 common stocks money supply, M2 interest rate spread, 10-year Treasury

bonds minus federal funds index of consumer expectations


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Coincident indicators identify trends in current economic activity

employees on nonagricultural payrolls personal income less transfer payments industrial production manufacturing and trade sales


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Lagging indicators confirm swings in past economic activity

average duration of unemployment, weeks

ratio, manufacturing and trade inventories to sales

change in labor cost per unit of output, manufacturing (%)


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Forecasting techniques Economic indicators: drawbacks

leading indicator index has forecast a recession when none ensued

a change in the index does not indicate the precise size of the decline or increase

the data are subject to revision in the ensuing months


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Trend projections: a form of naïve forecasting that projects trends from past data without taking into consideration reasons for the change

compound growth rate visual time series projections least squares time series projection


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Forecasting techniques Compound growth rate: forecasting by

projecting the average growth rate of the past into the future

provides a relatively simple and timely forecast

appropriate when the variable to be predicted increases at a constant %


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Forecasting techniques General compound growth rate formula:

E = B(1+i)n

E = final value n = years in the series B = beginning value i = constant growth rate


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Visual time series projections: plotting observations on a graph and viewing the shape of the data and any trends


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Time series analysis: a naïve method of forecasting from past data by using least squares statistical methods to identify trends, cycles, seasonality and irregular movements


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Forecasting techniques Time series analysis:

Advantages: easy to calculate does not require much judgment or

analytical skill describes the best possible fit for past

data usually reasonably reliable in the short

run


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Forecasting techniques Time series data can be represented as:

Yt = f(Tt, Ct, St, Rt)

Yt = actual value of the data at time t Tt = trend component at t Ct = cyclical component at t St = seasonal component at t Rt = random component at t


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Forecasting techniques Econometric models: causal or

explanatory models of forecasting regression analysis multiple equation systems

endogenous variables: dependent variables that may influence other dependent variables

exogenous variables: from outside the system, truly independent variables


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Forecasting techniques Example: econometric model

Suits (1958) forecast demand for new automobiles

∆R = a0 + a1 ∆Y + a2 ∆P/M + a3 ∆S + a4 ∆X

R = retail salesY = real disposable incomeP = real retail price of carsM = average credit termsS = existing stockX= dummy variable


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Global application

Example: forecasting exchange rates

GDP interest rates inflation rates balance of payments

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