chapter fivecopyright 2009 pearson education, inc. publishing as prentice hall. 1 chapter 5 demand...
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Chapter Five Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall.
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Chapter 5
Demand Estimationand Forecasting
Chapter Five Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall.
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Overview
Regression analysisHazards with use of regression analysisSubjects of forecastsPrerequisites of a good forecastForecasting techniques
Chapter Five Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall.
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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
Chapter Five Copyright 2009 Pearson Education, Inc. Publishing as Prentice Hall.
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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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Forecasting techniques
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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Forecasting techniques
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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Forecasting techniques
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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Forecasting techniques
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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Forecasting techniques
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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Forecasting techniques
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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Forecasting techniques
Visual time series projections: plotting observations on a graph and viewing the shape of the data and any trends
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Forecasting techniques
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