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DEMAND THEORY DEMAND THEORY DR. MOHAMMAD ABDUL MUKHYI, SE.,MM

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DEMAND THEORY. DR. MOHAMMAD ABDUL MUKHYI, SE.,MM. DEMAND FOR A COMMODITY. Permintaan adalah sejumlah barang yang diminta oleh konsumen pada tingkat harga tertentu. - PowerPoint PPT Presentation

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Page 1: DEMAND THEORY

DEMAND THEORYDEMAND THEORYDEMAND THEORYDEMAND THEORY

DR. MOHAMMAD ABDUL MUKHYI, SE.,MM

Page 2: DEMAND THEORY

DEMAND FOR A COMMODITYDEMAND FOR A COMMODITY

Permintaan adalah sejumlah barang yang diminta oleh konsumen pada tingkat harga tertentu.

Teori Permintaan adalah menghubungkan antara tingkat harga dengan tingkat kuantitas barang yang diminta pada periode waktu tertentu.

Fungsi Permintaan: QdX = ƒ(Px, Py, Pz, I, T, Tech, ….)

Page 3: DEMAND THEORY

Hypothetical Industry Demand Curves for New Domestic Automobiles at Interest Rates of 6%, 8%, and 10%

Hypothetical Industry Demand Curves for New Domestic Automobiles at Interest Rates of 6%, 8%, and 10%

Page 4: DEMAND THEORY

P

0 Q1

P P

Q2 Qx0 0

2 2 2

1 1 1

3 2 52 1 3

d1 d2 d3

Individual 1 Individual 2 Pasar

Page 5: DEMAND THEORY

Permintaan Kentang di IndonesiaPermintaan Kentang di Indonesia

Permintaan kentang untuk periode 1980-2008:QdS = 7.609 – 1.606PS + 59N + 947I + 479PW +

271t.QdS = quantitas kentang yang dijual per tahun per

1.000 Kg.PS = harga kentang per kgN = rata-rata bergeral jumlah penduduk per 1 milyar.I = pendapatan disposibel per kapita penduduk.PW = harga ubi per kg yang diterima petani.T = trend waktu (t = 1 untuk tahun 1980 dan t = 2

untuk tahun 2008).

N = 150,73I = 1,76 PW = 2,94 dan t = 1Bagaimana bentuk fungsi permintaan kentang?

Page 6: DEMAND THEORY

Elastisitas Harga PermintaanElastisitas Harga Permintaan

Elastisitas Titik :

Elastisitas Busur :

Q

P x

P

Q

P/P

Q/Qd

hargaperubahan %

diminta yangjumlah perubahan %d

p

p

E

E

2/)QQ(

2/)PP( x

P

Q d

Q/n

P/n x

P

Q d

21

21p

p

E

atau

E

Page 7: DEMAND THEORY

Elastisitas KumulatifElastisitas Kumulatif : :

Q

P x

P

Q d

Q/n

P/n x

P/N

Q/N d

p

p

E

atau

E

Elastisitas Silang :

Qx

Py x

Py

Qx

Py/Py)(

Qx/Qx)(c

Y barang hargaperubahan %

diminta yang X barangjumlah perubahan %c

xy

xy

E

atau

E

Page 8: DEMAND THEORY

Elastisitas Pendapatan :

Qd

Y x

Y

Qd

Qd

Y x

Y

Qdy

pendapatanperubahan %

diminta yang barangperubahan % y

E

atau

E

Elastisitas Harga, Total Revenue, Marginal Revenue :

TR = P . Q

MR = ΔTR / ΔQ

pE

1 1P MR

Page 9: DEMAND THEORY

Q = 600 – 100PDiminta :a. Buat fungsi pendapatan.b. Hitung nilai pendapatan marginal.c. Bila P = 4 dan EP = -2 hitung MR

Jawab:a. Q = 600 – 100P P = 6 – Q/100b. TR = P.Q TR = (6 – Q/100).Q = 6Q –

Q2/100MR = 6 – Q/50MR optimal = 00 = 6 – Q/50 Q = 300

Page 10: DEMAND THEORY

0100200

300400500600700

800900

1000

0 200 400 600 800

output

TR

($)

TR

output

0100200300400500

600700800900

1000

0 200 400 600 800

TR

($)

D

MR = 6 – Q/50

TR = 6Q – Q2/100

Q = 600 – 100P

Page 11: DEMAND THEORY

22

114

2

114

MR

Qx = 1,5 – 3,0Px + 0,8I + 2,0Py – 0,6Ps + 1,2A

Qx = penjualan kopi merek XPx = harga kopi merek XI = pendapatan disposibel per kapita per tahunPy = harga kopi pesaingPs = harga gula per kiloA = pengeluaran iklan untuk kopi merek X

Jika Px = 2; I = 2,5; Py = 1,8, Ps = 0.50 dan A = 1 berapa Q?

Qx = 1,5 – 3,0(2) + 0,8(2,5) + 2,0(1,8) – 0,6(0,50) + 1,2(1) = 2

Page 12: DEMAND THEORY

6,02

12,1E

15,02

50,06,0E

8,12

8,12E

12

2,50,8 E

32

23E

A

XS

XY

I

P

Tingkat Elastisitas :

Page 13: DEMAND THEORY

SupplySupply

Penawaran adalah sejumlah barang yang ditawarkan oleh produsen ke konsumen pada tingkat harga tertentu.

Teori Penawaran adalah menghubungkan antara tingkat harga dengan tingkat kuantitas barang yang ditawarkan pada periode waktu tertentu.

Fungsi Penawaran: QdX = ƒ(Px, Py, Pz, I, T, Tech, ….)

Page 14: DEMAND THEORY

Hypothetical Industry Supply Curve for New Domestic Automobiles

Hypothetical Industry Supply Curve for New Domestic Automobiles

Page 15: DEMAND THEORY

Hypothetical Industry Supply Curves for New Domestic Automobiles at Interest Rates of 6%, 8%, and 10%

Hypothetical Industry Supply Curves for New Domestic Automobiles at Interest Rates of 6%, 8%, and 10%

Page 16: DEMAND THEORY

Surplus, Shortage, and Market EquilibriumSurplus, Shortage, and Market Equilibrium

Page 17: DEMAND THEORY

Comparative Statics of Changing DemandComparative Statics of Changing Demand

Page 18: DEMAND THEORY

Comparative Statics of Changing SupplyComparative Statics of Changing Supply

Page 19: DEMAND THEORY

Comparative Statics of Changing Demand

and Changing Supply Conditions

Comparative Statics of Changing Demand

and Changing Supply Conditions

Page 20: DEMAND THEORY

Demand and Supply Curves

Demand and Supply Curves

Page 21: DEMAND THEORY

ObjectivesObjectives

• Understand how regression analysis and other techniques are used to estimate demand relationships

• Interpret the results of regression models– economic interpretation– statistical interpretation and tests

• Describe special econometric problems of demand estimation

Page 22: DEMAND THEORY

Approaches to Demand EstimationApproaches to Demand Estimation

• 1. Surveys, simulated markets, clinicsStated PreferenceRevealed Preference

• 2. Direct Market Experimentation

• 3. Regression Analysis

Page 23: DEMAND THEORY

A. Difficulties with Direct Market Experiments

(1) expensive and risky

(2) never a completely controlled experiment

(3) infeasible to try a large number of variations

(4) brief duration of experiment

Page 24: DEMAND THEORY

(1) Specify variables: Quantity Demanded, Advertising, Income, Price, Other prices, Quality, Previous period demand, ...

(2) Obtain data: Cross sectional v. Time series

(3) Specify functional form of equation

Linear Yt = + X1t + X2t + ut

Multiplicative Yt = X1t X2t

et

ln Yt = ln ln X1t + ln X2t + ut

(4) Estimate parameters

(5) Interpret results: economic and statistical

Page 25: DEMAND THEORY

Violating the assumptions of regression including

(1) Multicollinearity- highly correlated independent variables

(2) Heteroscedasticity- errors do not have the same variance

(3) Serial correlation- error in period t is correlated with error in period t + k

(4) Identification problems - data from interaction of supply and demand do not trace out demand relationship

Page 26: DEMAND THEORY

Transit ExampleTransit Example

• Y P T I H• YEAR Riders Price Pop. Income Parking Rate• 1966 1200 15 1800 2900 50• 1967 1190 15 1790 3100 50• 1968 1195 15 1780 3200 60• 1969 1110 25 1778 3250 60• 1970 1105 25 1750 3275 60• 1971 1115 25 1740 3290 70• 1972 1130 25 1725 4100 75• 1973 1095 30 1725 4300 75• 1974 1090 30 1720 4400 75• 1975 1087 30 1705 4600 80• 1976 1080 30 1710 4815 80• 1977 1020 40 1700 5285 80• 1978 1010 40 1695 5665 85

Page 27: DEMAND THEORY

• Y P T I H• YEAR Riders Price Pop. Income Parking Rate• 1979 1010 40 1695 5800 100• 1980 1005 40 1690 5900 105• 1981 995 40 1630 5915 105• 1982 930 75 1640 6325 105• 1983 915 75 1635 6500 110• 1984 920 75 1630 6612 125• 1985 940 75 1620 6883 130• 1986 950 75 1615 7005 150• 1987 910 100 1605 7234 155• 1988 930 100 1590 7500 165• 1989 933 100 1595 7600 175• 1990 940 100 1590 7800 175• 1991 948 100 1600 8000 190• 1992 955 100 1610 8100 200

Page 28: DEMAND THEORY

Linear Transit DemandLinear Transit DemandDependent Variable: RIDERSMethod: Least SquaresDate: 03/31/02 Time: 18:22Sample: 1966 1992Included observations: 27

Variable Coefficient Std. Error t-Statistic Prob.

C 85.43924 492.8046 0.173373 0.8639PRICE -1.617484 0.495976 -3.26122 0.0036POPULATION 0.643769 0.262358 2.453782 0.0225INCOME -0.047475 0.012311 -3.85616 0.0009PARKING 1.943791 0.349156 5.567113 0

R-squared 0.960015 Mean dependent var1026.222Adjusted R-squared 0.952745 S.D. dependent var 94.25756S.E. of regression 20.48984 Akaike info criterion 9.043312Sum squared resid 9236.342 Schwarz criterion 9.283282Log likelihood -117.0847 F-statistic 132.0525Durbin-Watson stat 1.384853 Prob(F-statistic) 0

Riders = 85.4 – 1.62 price …Pr Elas = -1.62(100/955) in 1992

Page 29: DEMAND THEORY

Multiplicative Transit DemandMultiplicative Transit DemandDependent Variable: LRIDERSMethod: Least SquaresDate: 03/31/02 Time: 18:26Sample: 1966 1992Included observations: 27

Variable Coefficient Std. Error t-Statistic Prob.

C 3.24892 3.26874 0.993937 0.3311LPRICE -0.13716 0.021873 -6.27052 0LPOPULATION 0.613645 0.409148 1.49981 0.1479LINCOME -0.13077 0.039913 -3.27646 0.0034LPARKING 0.166443 0.032361 5.143338 0

R-squared 0.973859 Mean dependent var6.929651Adjusted R-squared 0.969107 S.D. dependent var 0.09061S.E. of regression 0.015926 Akaike info criterion -5.27614Sum squared resid 0.00558 Schwarz criterion -5.03617Log likelihood 76.22788 F-statistic 204.9006Durbin-Watson stat 0.93017 Prob(F-statistic) 0

Ln Riders = exp(3.25)P-.14 …

Page 30: DEMAND THEORY

Ch 3: DEMAND ESTIMATIONCh 3: DEMAND ESTIMATION

In planning and in making policy decisions, managers must have some idea about the characteristics of the demand for their product(s) in order to attain the objectives of the firm or even to enable the firm to survive.

Page 31: DEMAND THEORY

Demand information about customer sensitivity toDemand information about customer sensitivity to

modifications in priceadvertisingpackagingproduct innovationseconomic conditions etc.

are needed for product-development strategy

• For competitive strategy details about customer reactions to changes in competitor prices and the quality of competing products play a significant role

Page 32: DEMAND THEORY

What Do Customers Want?What Do Customers Want?

• How would you try to find out customer behavior?

• How can actual demand curves be estimated?

Page 33: DEMAND THEORY

From Theory to PracticeFrom Theory to Practice

D: Qx = f(px, Y, ps, pc, , N)

(px=price of good x, Y=income, ps=price of substitute, pc=price of complement, =preferences, N=number of consumers)

• What is the true quantitative relationship between demand and the factors that affect it?

• How can demand functions be estimated?• How can managers interpret and use these

estimations?

Page 34: DEMAND THEORY

Most common methods used are:Most common methods used are:

a) consumer interviews or surveys to estimate the demand for new products to test customers reactions to changes in the

price or advertising to test commitment for established products

b) market studies and experiments to test new or improved products in controlled

settingsc) regression analysis

uses historical data to estimate demand functions

Page 35: DEMAND THEORY

Consumer Interviews (Surveys)Consumer Interviews (Surveys)

• Ask potential buyers how much of the commodity they would buy at different prices (or with alternative values for the non-price determinants of demand)

face to face approachtelephone interviews

Page 36: DEMAND THEORY

Consumer Interviews cont’dConsumer Interviews cont’d

• Problems:– Selection of a representative sample

• what is a good sample?

– Response bias• how truthful can they be?

– Inability or unwillingness of the respondent to answer accurately

Page 37: DEMAND THEORY

Market Studies and ExperimentsMarket Studies and Experiments

• More expensive and difficult technique for estimating demand and demand elasticity is the controlled market study or experiment– Displaying the products in several different

stores, generally in areas with different characteristics, over a period of time

• for instance, changing the price, holding everything else constant

Page 38: DEMAND THEORY

Market Studies and Experiments cont’dMarket Studies and Experiments cont’d

• Experiments in laboratory or field– a compromise between market studies and

surveys– volunteers are paid to stimulate buying

conditions

Page 39: DEMAND THEORY

Market Studies and Experiments cont’dMarket Studies and Experiments cont’d

• Problems in conducting market studies and experiments:

a) expensive

b) availability of subjects

c) do subjects relate to the problem, do they take them seriously?

BUT: today information on market behavior also collected by membership and award cards

Page 40: DEMAND THEORY

Regression Analysis and Demand EstimationRegression Analysis and Demand Estimation

• A frequently used statistical technique in demand estimation

• Estimates the quantitative relationship between the dependent variable and independent variable(s)quantity demanded being the dependent variable if only one independent variable (predictor) used:

simple regression if several independent variables used: multiple

regression

Page 41: DEMAND THEORY

A Linear Regression ModelA Linear Regression Model

• In practice the dependence of one variable on another might take any number of forms, but an assumption of linear dependency will often provide an adequate approximation to the true relationship

Page 42: DEMAND THEORY

Think of a demand function of general form:Think of a demand function of general form:

Qi = + 1Y - 2 pi + 3ps - 4pc + 5Z + ε

whereQi = quantity demanded of good iY = incomepi = price of good ips = price of substitute(s)pc = price of complement(s)Z = other relevant determinant(s) of demandε = error term

Values of and i ?

Page 43: DEMAND THEORY

and i have to be estimated from historical data and i have to be estimated from historical data

• Data used in regression analysiscross-sectional data provide information on

variables for a given period of time

time series data give information about variables over a number of periods of time

• New technologies are currently dramatically changing the possibilities of data collection

Page 44: DEMAND THEORY

Simple Linear Regression ModelSimple Linear Regression Model

In the simplest case, the dependent variable Y is assumed to have the following relationship with the independent variable X:

Y = + X + εwhere

Y = dependent variableX = independent variable = intercept = slopeε = random factor

Page 45: DEMAND THEORY

Estimating the Regression EquationEstimating the Regression Equation

• Finding a line that “best fits” the data– The line that best fits a collection of X,Y data

points, is the line minimizing the sum of the squared distances from the points to the line as measured in the vertical direction

– This line is known as a regression line, and the equation is called a regression equation

Estimated Regression Line:

XY ˆ

Page 46: DEMAND THEORY

Observed Combinations of Output and Labor inputObserved Combinations of Output and Labor input

Skatter Plot

0

100

200

300

400

500

600

0 100 200 300 400 500 600 700 800

L

Q

Q

YY ˆ

Page 47: DEMAND THEORY

Regression with ExcelRegression with Excel

SUMMARY OUTPUT

Regression StatisticsMultiple R 0,959701R Square 0,921026Adjusted R Square0,917265Standard Error47,64577Observations 23

ANOVAdf SS MS F Significance F

Regression 1 555973,1 555973,1 244,9092 4,74E-13Residual 21 47672,52 2270,12Total 22 603645,7

CoefficientsStandard Errort Stat P-value Lower 95%Upper 95%Lower 95,0%Upper 95,0%Intercept -75,6948 31,64911 -2,39169 0,026208 -141,513 -9,87686 -141,513 -9,87686X Variable 11,377832 0,088043 15,64957 4,74E-13 1,194737 1,560927 1,194737 1,560927

Evaluate statistical significance of regression coefficients using t-test and statistical significance of R2 using F-test

Page 48: DEMAND THEORY

Statistical analysis is testing hypothesesStatistical analysis is testing hypotheses

• Statistics is based on testing hypotheses• ”null” hypothesis = ”no effect”• Assume a distribution for the data, calculate

the test statistic, and check the probability of getting a larger test statistic value

X

Z

Z For the normal distribution:

p

Page 49: DEMAND THEORY

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

i = estimated coefficient

• H0: i = 0

• SEβ: standard error of the estimated coefficient

• Rule of 2: if absolute value of t is greater than 2, estimated coefficient is significant at the 5% level (= p-value < 0.05)

• If coefficient passes t-test, the variable has an impact on demand

iSE

t i

Page 50: DEMAND THEORY

Sum of SquaresSum of Squares

Page 51: DEMAND THEORY

Sum of Squares cont’dSum of Squares cont’d

TSS = (Yi - Y)2

(total variability of the dependent variable about its mean Y)

RSS = (Ŷi - Y)2

(variability in Y explained by the sample regression)

ESS = (Yi - Ŷi)2

(variability in Yi unexplained by the dependent variable x)

This regression line gives the minimum ESS among all possible straight lines.

Page 52: DEMAND THEORY

The Coefficient of DeterminationThe Coefficient of Determination

• Coefficient of determination R2 measures how well the line fits the scatter plot (Goodness of Fit)

R2 is always between 0 and 1 If it’s near 1 it means that the regression line is a

good fit to the dataAnother interpretation: the percentage of variance

”accounted for”

TSSESS

1TSSRSS

R2

Page 53: DEMAND THEORY

F-testF-test

• The null hyphotesis in the F-test is

H0: 1= 0, 2= 0, 3= 0, …• F-test tells you whether the model as a whole explains

variation in the dependent variable• No rule of thumb, because the values of the F-

distribution vary a lot depending on the degrees of freedom (# of variables vs. # of observations)– Look at p-value (”significance F”)

Page 54: DEMAND THEORY

Special Cases:Special Cases:

• Proxy variables– to present some other “real” variable, such as taste

or preference, which is difficult to measure

• Dummy variables (X1= 0; X2= 1)

– for qualitative variable, such as gender or location

• Linear vs. non-linear relationship– quadratic terms or logarithms can be used

Y = a + bX1 + cX12

QD=aIb logQD= loga + blogI

Page 55: DEMAND THEORY

Example: Specifying the Regression Equation for Pizza DemandExample: Specifying the Regression Equation for Pizza Demand

We want to estimate the demand for pizza among college students in USA

What variables would most likely affect their demand for pizza?

What kind of data to collect?

Page 56: DEMAND THEORY

Data: Suppose we have obtained cross-sectional data on randomly selected 30 college campuses (through a survey)Data: Suppose we have obtained cross-sectional data on randomly selected 30 college campuses (through a survey)

The following information is available:average number of slices consumed per month by

studentsaverage price of a slice of pizza sold around the

campusprice of its complementary product (soft drink) tuition fee (as proxy for income) location of the campus (dummy variable is

included to find out whether the demand for pizza is affected by the number of available substitutes); 1 urban, 0 for non-urban area

Page 57: DEMAND THEORY

Linear additive regression line:Linear additive regression line:

Y = a + b1pp + b2 ps + b3T + b4L

where Y = quantity of pizza demandeda = the intercept

Pp = price of pizza

Ps = price of soft drinkT = tuition feeL = location

bi = coefficients of the X variables measuring the impact of the variables on the demand for pizza

Page 58: DEMAND THEORY

Estimating and Interpreting the Regression CoefficientsEstimating and Interpreting the Regression Coefficients

Y = 26.27- 0.088pp - 0.076ps + 0.138T- 0.544 L (0.018) (0.018)* (0.020)* (0.087) (0.884)

R2 = 0.717adjusted R2 = 0.67F = 15.8

Numbers in parentheses are standard errors of coefficients.

*significant at the 0.01 level

Page 59: DEMAND THEORY

Problems in the Use of Regression Analysis:Problems in the Use of Regression Analysis:

• identification problem

• multicollinearity

(correlation of coefficients)

• autocorrelation

(Durbin-Watson test)

• normality assumption fails

(outside the scope of this course)

Page 60: DEMAND THEORY

Identification ProblemIdentification Problem

• Can arise when all effects on Y are not accounted for by the predictors

Q

P

Q

P S

D3

D2

D1

Can demand be upward sloping?!

OR…?

D?!

Page 61: DEMAND THEORY

MulticollinearityMulticollinearity

• A significant problem in multiple regression which occurs when there is a very high correlation between some of the predictor variables.

Page 62: DEMAND THEORY

Resulting problem:Resulting problem:

Regression coefficients may be very misleading or meaningless because…

– their values are sensitive to small changes in the data or to adding additional observations

– they may even be opposite in sign from what ”makes sense”

– their t-value (and the standard error) may change a lot depending upon which other predictors are in the model

Page 63: DEMAND THEORY

Multicollinearity cont’dMulticollinearity cont’d

Solution:

Don’t use two predictors which are very highly correlated (however, x and x2 are O.K.)

Not a major problem if we are only trying to fit the data and make predictions and we are not interested in interpreting the numerical values of the individual regression coefficients.

Page 64: DEMAND THEORY

Multicollinearity cont’dMulticollinearity cont’d

• One way to detect the presence of multicollinearity is to examine the correlation matrix of the predictor variables. If a pair of these have a high correlation they both should not be in the regression equation – delete one.

Y X1 X2 X3

Y 1.00 -.45 .81 .86

X1 -.45 1.00 -.82 -.59

X2 .81 -.82 1.00 .91

X3 .86 -.59 .91 1.00

Correlation Matrix

Page 65: DEMAND THEORY

AutocorrelationAutocorrelation

• Correlation between consecutive observations• Usually encountered with time series data

– E.g. seasonal variation in demand

Creates a problem with t-tests: insignificant variables may appear significant

time

D

Page 66: DEMAND THEORY

A test for Autocorrelated Errors:DURBIN-WATSON TESTA test for Autocorrelated Errors:DURBIN-WATSON TEST

• A statistical test for the presence of autocorrelation

• Fit the time series with a regression model and then determine the residuals:

ttt yy ˆ

n

tt

n

ttt

d

1

2

2

21)(

Page 67: DEMAND THEORY

The Interpretation of d:The Interpretation of d:

The Durbin-Watson value d will always be 0 d 4

40 2

No correlation

Strong negative correlation

Strong positive correlation

Page 68: DEMAND THEORY

Multiple Regression ProcedureMultiple Regression Procedure

1. Determine the appropriate predictors and the form of the regression model

– Linear relationship– No multicollinearity– Variables ”make sense”

2. Estimate the unknown and coefficients3. Check the “goodness” of the model (R2, global F-test,

individual t-test for each coefficient)4. Use the fitted model for predictions (and determine

their accuracy)

Page 69: DEMAND THEORY

Additional Comments:Additional Comments:

• OCCAM’S RAZOR. We want a model that does a good job of fitting the data using a minimum number of predictors. A high R2 is not the only goal; variables used should be ”meaningful”

• Don’t use more predictors in a regression model than 5% to 10% of n

• Correlation is not causality!

Page 70: DEMAND THEORY

FORECASTINGFORECASTING

• Expert opinion –based methods– Delphi method

• Data-based methods– Time series analysis

• History can predict the future?– Regression analysis

• Forecast the values of the Xi’s to get Y

• Assumes the relationship between Xi’s and Y does not change