Transcript Demand Estimation Chapter 5 © 2009, 2006 South-Western, a part of Cengage Learning.

Demand Estimation
Chapter 5
© 2009, 2006 South-Western, a
part of Cengage Learning
Chapter 5
OVERVIEW
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Interview and Experimental Methods
Simple Demand Curve Estimation
Simple Market Demand Curve Estimation
Identification Problem
Regression Analysis
Measuring Regression Model
Significance
Measures of Individual Variable
2006 South-Western, a
Significance © 2009,
part of Cengage Learning
Chapter 5
KEY CONCEPTS
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market demand curve
simultaneous relation
identification problem
consumer interview
market experiments
regression analysis
deterministic relation
statistical relation
time series
cross section
scatter diagram
linear model
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multiplicative model
simple regression model
multiple regression model
standard error of the estimate
(SEE)
correlation coefficient
coefficient of determination
degrees of freedom
corrected coefficient of
determination
F statistic
t statistic
two-tail t tests
one-tail t tests
© 2009, 2006 South-Western, a
part of Cengage Learning
Interview and Experimental
Methods
 Consumer
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Interviews can solicit useful information when
market data is scarce.
Consumer opinions can differ from behavior.
 Market
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Interviews
Experiments
Controlled experiments can generate useful
insight.
Experiments can be expensive.
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part of Cengage Learning
Another possibility is for the firm to introduce new products, or increase quality
or an existing product. The firm can analyze the change in demand.
All firms experiment in one way or another. They run advertising campaigns
and monitor sales, they increase/decrease prices and monitor sales.
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part of Cengage Learning
Simple Demand Curve Estimation
 Simple
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The best estimation method balances
marginal costs and marginal benefits.
Simple linear relations are often useful for
demand estimation.
 Using
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Linear Demand Curves
Simple Linear Demand Curves
Straight-line relations can give useful
approximations.
© 2009, 2006 South-Western, a
part of Cengage Learning
Simple Market Demand Curve
Estimation
 Market
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Demand Curve
Shows total quantity customers are willing to
buy at various prices under current market
conditions.
 Graphing
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the Market Demand Curve
Market demand is the sum of individual
demand quantities, Q1 + Q2 = Q1+2.
Add quantities, not prices!
© 2009, 2006 South-Western, a
part of Cengage Learning
© 2009, 2006 South-Western, a
part of Cengage Learning
Identification Problem
 Changing
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Demand relations are dynamic.
 Interplay
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of Demand and Supply
Economic conditions affect demand and
supply.
 Shifts
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Nature of Demand Relations
in Demand and Supply
Curve shifts can be estimated.
 Simultaneous
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Relations
Quantity and price are jointly determined.
© 2009, 2006 South-Western, a
part of Cengage Learning
© 2009, 2006 South-Western, a
part of Cengage Learning
Regression Analysis
 What
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Is a Statistical Relation?
A statistical relation exists when averages are
related.
A deterministic relation is true by definition.
 Specifying
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Dependent variable Y is caused by X.
X variables are independently determined
from Y.
 Least
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the Regression Model
Squares Method
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part of Cengage Learning
Minimize sum of squared residuals.
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part of Cengage Learning
Regression Analysis
What variables affect demand? I, Ad, PX, PY, POP, Taste + Pref, r
Can they be measured? Is the data too costly?
Specifying the Form of the Equation
Linear
QX = a1 + a2PX + a3PY + a4I
The computer will estimate a1, a2, a3, a4 using OLS, using either
(1) cross sectional – demand for all types of soda variables such as Psoda,
Pjuice, I, age of the POP, season etc. at a given point in time.
(2) time series analysis – P90 Q90  P09 Q09
It, At, POPt
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part of Cengage Learning
© 2009, 2006 South-Western, a
part of Cengage Learning
Measuring Regression Model
Significance
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Standard Error of the Estimate (SEE) reflects
degree of scatter about the regression line.
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part of Cengage Learning
© 2009, 2006 South-Western, a
part of Cengage Learning
F statistic
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Tells if R2 is statistically significant.
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part of Cengage Learning
Goodness of Fit
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Correlation shows degree of concurrence.
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r = 1 means perfect correlation.
r = 0 means no correlation.
 Coefficient
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R2 = 100% means perfect fit.
R2 = 0% means no relation.
 Corrected
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of determination, R2.
coefficient of determination
Adjusts R2 downward for small samples.
© 2009, 2006 South-Western, a
part of Cengage Learning
Judging Variable Significance
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t statistics compare sample characteristics to the
standard deviation of that characteristic.
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Two-tail t Tests
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t > 2 implies a strong effect of X on Y (95% conf.).
t > 3 implies a very strong effect of X on Y (99% conf.)
Tests of effect.
One-Tail t Tests
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Tests of magnitude or direction.
© 2009, 2006 South-Western, a
part of Cengage Learning
Multiplicative Model
QX = APXB PYC IR AE POPF
The marginal effect on demand of any of the independent variables depends on
the value (levels) of the other variables. Proof is in the book. A Company’s
advertising may be more effective when I is high.
But more importantly the exponents are equal to the elasticity of each variable.
Proof
(dQx/dI) (I/Qx) = eI
(dQx/dI) (I/Qx) = APXB PYC IR AE POPF
Notice no matter what the value is of QXI the elasticity = d; this is called
constant elasticity of demand.
Log Q = Log A + B Log PX + C Log PY + D Log I + C Log A + F Log POP
Computer Packages Transform Data Into Logs
Elasticity varies using the
© 2009, 2006 South-Western, a
partform…
of Cengage Learning
linear
Case Study
 Mrs.
Smyth’s Gourmet Pies
 Page 195
© 2009, 2006 South-Western, a
part of Cengage Learning
© 2009, 2006 South-Western, a
part of Cengage Learning