1.1
Week 1
Introduction
Econometrics
Analysis
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1.2
1. Economics Theory
“ Keynes postulated a positive relationship between consumption
and incomes”, i.e., people’s income
2. Mathematical Expression:
Consumption = f(Income) ==> C = f(Y)
MPC = dC/dY = f’(Y) > 0 ;assume 0 < MPC < 1
3. Statistics:
Year
1980
1981
….
2000
C
Y
2447.1 3776.3
2476.9 3841.1
….
….
4651.8 5991.7
Find the mean, variance,
standard deviation,
correlation, etc.
4. Econometric - Regression model
Ct = 1 + 2 Yt + ut
=> C/Y = 2
=> estimating the relationship
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1.3
The Role of Econometrics
Provide measurement and quantitative
analysis of actual economic
phenomena
or economic relationship based on
1. Economic theory
2. Economic data
3. Methods of model constructed
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1.4
Economic Relationships:
money supply
Stock Market Index
government
budget
Interest rate
inflation
Wage
Exchange
Rate
trade
deficit
Properties Market
unemployment
capital gains tax
crime rate
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rent
control
laws
Economic Decisions
To use information effectively:
economic theory
economic data
}
economic
decisions
*Econometrics* helps us combine
economic theory and economic data .
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1.5
The Consumption Function
1.6
Consumption, C, is some function of income, i :
c = f(i)
For applied econometric analysis
this consumption function must be
specified more precisely.
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1.7
demand, qd, for an individual commodity:
qd = f( p, pc, ps, i )
demand
p = own price; pc = price of complements;
ps = price of substitutes; i = income
supply, qs, of an individual commodity:
qs = f( p, pc, pf, ps )
supply
p = own price; pc = price of complement products;
ps = price of substitutes; pf = price of factor inputs
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1.8
How much ?
Listing the variables in an economic relationship is not enough.
For effective policy we must know the amount of change
needed for a policy instrument to bring about the desired
effect:
• By how much should the Federal Reserve
raise interest rates to prevent inflation?
• By how much can the price of football tickets
be increased and still fill the stadium?
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1.9
Answering the How Much? question
Need to estimate parameters
that are both:
1. unknown
and
2. unobservable
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1.10
The Statistical Model
Average or systematic behavior
over many individuals or many firms.
Not a single individual or single firm.
Economists are concerned with the
unemployment rate and not whether
a particular individual gets a job.
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1.11
The Statistical Model
Actual vs. Predicted Consumption:
Actual = systematic part + random error
Consumption, c, is function, f, of income, i, with error, u:
c = f(i) + u
Systematic part provides prediction, f(i),
but actual will miss by random error, u.
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The Consumption Function
c = f(i) + u
Need to define f(i) in some way.
To make consumption, c,
a linear function of income, i :
f(i) = 1 + 2i
The statistical model then becomes:
c = 1 + 2i + u
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1.12
The Wage Function
1.13
W = f(X) + u
Where X can represent a group of variables such
“education”, “experience”, and “training”, etc.
f(X) = 1 + 2 educ + 3 experi + 4 training
The statistical estimation model then becomes:
W = 1 + 2 educ + 3 experi + 4 training + u
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1.14
The Econometric Model
y =  1 +  2 X 2 +  3 X 3+ u
• Dependent variable, y, is focus of study
(predict or explain changes in dependent variable).
• Explanatory variables, X1 and X2, help us explain
observed changes in the dependent variable.
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1.15
Terminology and Notation
Y =
1 + 2 X + u
Left hand-side
Variable:
Right hand-side
Variable:
Dependent
Explained
Predictand
Regressand
Response
Endogenous
Explanatory
Independent
Predictor
Regressor
Stimulus or control
Exogenous
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1.16
Statistical Models
Controlled (experimental)
vs.
Uncontrolled (observational)
Controlled experiment (“pure” science) explaining mass, Y :
pressure, X1, held constant when varying temperature, X2,
and vice versa.
Uncontrolled experiment (econometrics) explaining consumption, Y: price, X1, and income, X2, vary at the same time.
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1.17
Econometric model
• economic model
economic variables and parameters.
• statistical model
sampling process with its parameters.
• data
observed values of the variables.
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Time series data
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1.18
Cross-section data and Pool (Panel) data
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1.19
1.20
The Practice of Econometrics
• Statement of theory or hypothesis
• Specification of the mathematical model of the
theory
• Specification of the econometric model of the
theory
• Obtaining data for the analysis.
• Estimation with statistical properties.
• Hypothesis testing
• Analyze and evaluate implications of the results
• Forecasting or prediction
• Using the model for control or policy purpose
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1.21
Economic Empirical Study
Economic Theory; Past Experience, studies
Formulating a model: Cause - effect
Gathering data:
C = f(Inc) ==>
Ct = 1 + 2Inct + ut
Statistics monthly, quarterly, yearly data
Estimating the model:
Simple OLS method or other advances
Testing the hypothesis:
H0: 2>0,
positive relationship or not If not true
Interpreting the results:
Forecasting
Policy implication and decisions
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