MICROECONOMICS Part 1 Economic Concepts for Strategy Demand

1
MICROECONOMICSPart 1Economic Concepts for
Strategy - Demand

• Lecturer
• Ass.Prof. Polona Domadenik, PhD
• E-mail polona.domadenik_at_ef.uni-lj.si

2
Market Demand Analysis for Decision Making

• Outline of the lecture
• Market Demand
• The Elasticity Concept
• Direct vs. Indirect Methods of Demand Estimation
• Regression Analysis of
• Consumer Demand
• V. Economic Forecasting

3
Market Demand

• The demand for a good or service is defined as
• Quantities of a good or service that people are
  ready (willing and able) to buy at various prices
  within some given time period, other factors
  besides price held constant.

4
The demand curve slopes downward demonstrating
that consumers are willing to buy more at a
lower price as the product becomes relatively
cheaper and the consumers real income
increases.

• The inverse relationship between price and the
  quantity demanded of a good or service is called
  the Law of Demand.

The theory of individual behavior
5

• Changes in nonprice determinants result in
  changes in demand.
• Nonprice determinants of demand
• Tastes and preferences
• Income
• Prices of related products
• Future expectations
• Number of buyers
• This is shown as a shift in the demand curve.

6
The Economic Concept of Elasticity

• Elasticity The sensitivity of one variable to
  another or, more precisely, the percentage change
  in one variable relative to a percentage change
  in another.

7
The Price Elasticity of Demand

• The percentage change in quantity demanded
  caused by a 1 percent change in price.

8

• Determinants of Elasticity
• Ease of substitution
• Proportion of total expenditures
• Durability of product
• Possibility of postponing purchase
• Possibility of repair
• Second hand market
• Length of time period

9
The Price Elasticity of Demand and Revenue

• As price decreases
• revenue rises when demand is elastic
• falls when it is inelastic
• reaches it peak when elasticity of demand equals
  1.

10
The Cross-Price Elasticity of Demand

• The percentage change in quantity consumed of
  one product as a result of a 1 percent change in
  the price of a related product.

11

• The sign of cross-elasticity for substitutes is
  positive.
• The sign of cross-elasticity for complements is
  negative.

12
Income Elasticity

• The percentage change in quantity demanded
  caused by a 1 percent change in income.

13

• Categories of income elasticity
• Superior goods
• EM gt 1
• Normal goods
• 0 gt EM gt 1
• Inferior goods
• EM lt 1

14
Other Elasticity Measures

• Elasticity is encountered every time a change in
  some variable affects quantities.
• Advertising expenditure
• Interest rates
• Population size

15
Uses of Elasticities

• Pricing
• Managing cash flows
• Impact of changes in competitors prices
• Impact of economic booms and recessions
• Impact of advertising campaigns
• And lots more!

16
Demand Estimation

• WHY???
• to determine the relationship between price and
  quantity of a given good or service
• Obviously, the more closely the firm can estimate
  demand conditions for its product, the more
  likely it is to determine its profit maximizing
  rate of output and price, or wether to produce at
  all.

17

• Which factors determine the demand?
• 1. Controllable factors (price, advertising,
  distribution channels, etc)
• 2. Non-controllable factors (consumers income,
  consumers preferences, prices of substitutes and
  complements, expectations, etc)

18
Estimation Techniques

• Direct approach
• Obtaining direct data about consumer behaviour
• Indirect approach
• Using primary and secondary (existing) data

19
Direct Approach

• Direct communication with consumers or
  observation of their behaviour.
• Techniques
• Survey (interviews)
• Focus group
• Market experiments, etc.

20

• CASE small firm that would like to become the
  general representative for selling rollerblades
• POTENTIAL MARKET Younger consumers-aprox.
  100.000 people
• ??? What is the effect of price on potential
  selling quantity?

21
(No Transcript)
22
(No Transcript)
23
Possible pricing policies
24
Indirect Approach

• Uses secondary data and statistical procedures
• Data
• Time series
• Cross-sectional data

25
Regression Analysis

• A statistical technique for finding the best
  relationship between a dependent variable and
  selected independent variables.
• Simple regression one independent variable
• Multiple regression several independent
  variables

26

• Dependent variable
• depends on the value of other variables
• is of primary interest to researchers
• Independent variables
• used to explain the variation in the dependent
  variable

Linear regression model Qx?BxPxBsPsBcPc
BmPm Bx, Bs, Bc, Bm regression
coefficients ? -intercept
27
How to proceed?

• Specification of relevant demand factors
• Specification of measurement scales
• Data
• Selection of model
• Coefficient estimation
• Statistical evaluation
• Identifying the demand function
• Practical use of estimated demand function

28
Example Estimating Demand for Beer

• STEP 1 identifying relevant factors of short run
  demand for beer
• Intensivity of advertising (controllable)
• Weather (non-controllable)
• STEP 2 specification of measurement scales
• Quantity of beer consumed (N)
• Intensity of advertising (advertising outlays)
• Weather (average
  monthly temperature)

29

• STEP 3 Data

30
(No Transcript)
31
(No Transcript)
32
(No Transcript)
33

• STEP 4 selection of an appropriate
  regression model
• STEP 5 estimation of regression
  coefficients

34

• STEP 6 statistical evaluation
• Good fit
• Coefficients statistically significant
• STEP 7

35

• STEP 8 practical use
• Calculation of demand elasticity with respect to
  advertising
• December, 2nd year A5.000, T3
• July, 1st year A2.000, T31

EA(5.000,3) 0,381
EA(2.000,31) 0,098
36
Estimating Linear Regression Equation
37
The estimation of the regression equation
involves a search for the best linear
relationship between the dependent and the
independent variable.
38
Simple Regression Y a bX u Y
dependent variable X independent variable a
intercept b slope u random factor
39

• ORDINARY LEAST SQUARES (OLS)
• A statistical method designed to fit a line
  through a scatter of points is such a way that
  the sum of the squared deviations of the points
  from the line is minimized.
• OLS is blue.
• Many software packages perform OLS estimation.

40

• ORDINARY LEAST SQUARES (OLS)
• Let be
• where a are parameters of linear function
• Then, the necessary condition for minimum of F(a)
  is

iN number of observations
jK number of parameters
41

• How good is the regression model?
• A) Are all independent variables relevant?
• B) Is the model statistically significant?
• C) How well does regression line fit the data?

42

• How confident can a researcher be about the
  extent to which the regression equation for the
  sample truly represent the unknown regression
  equation for population???

Each random sample from the population generates
its own intercept and slope coefficients.
43
Evaluation the Quality of Regression Model

• To answer questions AB we use hypothesis testing
  (test of statistical significance)
• Hypothesis testing is a procedure based on sample
  evidence and probabilistic theory that help us to
  determine whether
• the hypothesis is the reasonable statement and
  should not be rejected
• the hypothesis is unreasonable statement and
  should be rejected

44
Testing Procedure

• 5-Step testing procedure
• 1) State a null and alternative hypothesis
• 2) Select a level of significance
• 3) Identify the test statistics
• 4) Formulate a decision rule
• 5)Take a sample ? decision

45
1) State a null and alternative
hypothesis Ho b 0 Ho b ?
0 or H1 b ? 0 H1 b ?? 0 In Ho we
put the statement we would like to reject.
46
2) Select a level of significance Level of
significance probability of rejecting the null
hypothesis when it is actually true Type 1
error rejecting null hypothesis when it is
actually true (?) Type 2 error accepting null
hypothesis when it is actually false (?)
47
3) Identify the test statistics A) testing for
statistical significance of the estimated
regression coefficients It can be demonstrated
mathematically that the standard deviation of
each samples estimate from the actual population
value has a t-distribution. Estimated
coefficients t-distribution with (n-k-1) degrees
of freedom N-number of observations K-number of
independent variables
48
B) testing for statistical significance of the
entire regression model H0(?1, ?2, ...,
?N0) F test OR
49

• 4) Formulate a decision rule
• Decision rule states condition of rejection or
  non-rejection
• Critical value dividing point between the region
  where the null hypothesis is rejected and the
  region where the null hypothesis is not rjected
• Significance level

50
5) Take a sample ? decision Compare the
value of resulting statistics with critical value
and make the decision
51

• Explanatory power of estimated regression
  equation

• Coefficient of determination (R2)
• A measure indicating the percentage of the
  variation in the dependent variable accounted for
  by variations in the independent variables.
• R2 is a measure of the goodness of fit of the
  regression model.

52
If R2 1 the total deviation in Y from its mean
is explained by the equation.
53

• If R2 0 the regression equation does not
  account for any of the variation of Y from its
  mean.

54

• The closer R2 is to unity, the greater the
  explanatory power of the regression equation.
• An R2 close to 0 indicates a regression equation
  will have very little explanatory power.
• As additional independent variables are added,
  the regression equation will explain more of the
  variation in the dependent variable. This leads
  to higher R2 measures.

55

• Adjusted coefficient of determination
• k number of independent variables
• n sample size

56
Estimating Demand for Beer

57
Additional Topics
Proxy variable an alternative variable used in
a regression when direct information in not
available Dummy variable a binary variable
created to represent a non-quantitative factor.
58
The relationship between the dependent and
independent variables may be nonlinear.
59
We could specify the regression model as
quadratic regression model. Y a b1x b2x2
60
We could also specify the regression model as
power function. Y axb or log Qd log a
b(logX)
61
Estimation of Non-Linear Regression Models

• Polynomial model
• Y a bX cX2 dX3
• We introduce new variables
• XX, X X2 in X X3
• Non-linear linear model
• Y a bX cX dX

62

• Multiplicative model
• Y aXbWcZd
• We take logarithms
• log(Y) log(a) b?log(X) c?log(W) d?log(Z)
• Introduce new variables
• Ylog(Y), Xlog(X), Wlog(W) in Zlog(Z)
• Non-linear linear model
• Y log(a) bX cW dZ

63
Problems with Linear Regression
1. Model Selection Solution test more models
and pick up the best one 2. Omission of the
relevant variable(s) Solution test the model
augmented with additional variables 3. Quality of
measurement 4. Multicolinearity Solution drop
variable(s) that cause multicolinearity
64
5. Identification problem
65
The Final Step
Check the residuals White Noise? No. Check the
model and procedures again.
66
Forecasting Demand

• WHY?
• to quess the future demand art science at
  the same time
• to set objectives and create plans
• forecasted demand is a foundation for
  operational, tactical and strategic decisions

67
Subjects of Forecasts

• Macro forecasts
• Gross domestic product
• Consumption expenditure
• Producer durable equipment expenditure
• Residential construction
• Industry forecasts
• Sales of an industry as a whole
• Sales of a particular product within an industry

68

• Firm-level forecasts
• Sales
• Costs and expenses
• Employment requirements
• Square feet of facilities utilized

69
Prerequisities of Good Forecast

• must be consistent with other parts of business
• should be based on adequate knowledge
• should take into consideration the economic and
  political environment

70
Forecast Techniques

• QUALITATIVE (not just an emergency exit)
• Expert opinion (e.g. Delphi)
• Opinion polls and marketing research
• Economic indicators
• QUANTITATIVE
• Projections
• Econometric models

71
Naive methods project past data without
explaining future trends. Causal (or
explanatory) forecasting attempts to explain the
functional relationships between the dependent
variable and the independent variables.
72
Choosing the right technique depends on various
factors.

• the item to be forecast
• the relation between value and cost
• the quantity of historical data available
• the time allowed to prepare the forecast

73
Time Series Analysis
Assumption behaviour in the future will be
similar to behavior in the past (BUT consider
environmental, political changes, govenmental
measures, etc) Forecasting of stock values is a
modern version of transforming lead into gold
74
Time Series Components

• We can think of time series as consisting of
  several components besides the basic level B
• Trend T (long-term moving of the average)
• Cyclical component C (regular pattern of sequence
  of points above and belove the trend line) . Ex
  cyclical movements in the economy
• Seasonal component S (regular pattern of
  variability in a shorter period of time)
• Irregular component R (caused by unanticipated
  and nonrecurring factors - unpredictable)

75
Forecasting methods

• LAST VALUE
• Forecast
• Can be a good estimate.
• LINEAR TREND
• Forecast

76
Linear Trend
77
?7,75
78
Moving Average Method
NOTE the larger I, the slower response
to changes, but more stable predictions.
79
Other Forecasting Methods

• Weighted moving average
• Exponential smoothing
• Decomposition (trend, seasonal effects, cyclical
  effects)
• ARIMA
• etc.

80
Econometric Models

• Regression analysis ? estimation of
  coefficients
• ASSUMPTION the relationship between variables
  doesnt change from past into future
• ? on the basis of independent variables the
  dependent variable is predicted

81
Forecasting Demand for Beer

• We have already estimated monthly demand function
  for beer
• Q 10.088,13 1.79 ? A 716,67 ? T
• For the month after we estimated
• average temperature T4
• advertising outlays A7.000
• therefore
• Q 10.088,13 1,79 ? 7.000 716,67 ? 4
  25.478

82
(No Transcript)
83
(No Transcript)