1. Econometrics Analysis
Week 1
Introduction
Econometrics
Analysis

2. 2. Mathematical Expression:
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 C Y
…. …. ….
Find the mean, variance,
standard deviation,
correlation, etc.
4. Econometric - Regression model
Ct = 1 + 2 Yt + ut => C/Y = 2
=> estimating the relationship

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

4. Economic Relationships:
Stock Market Index
capital gains tax
crime rate
unemployment
Exchange
Rate
Properties Market
rent
control
laws
Interest rate
money supply
government
budget
trade
deficit
Wage
inflation

5. } Economic Decisions To use information effectively:
economic theory
economic data }
economic
decisions
To use information effectively:
*Econometrics* helps us combine
economic theory and economic data .

6. The Consumption Function
Consumption, C, is some function of income, i :
c = f(i)
For applied econometric analysis
this consumption function must be
specified more precisely.

7. demand supply qd = f( p, pc, ps, i ) qs = f( p, pc, pf, ps )
demand, qd, for an individual commodity:
qd = f( p, pc, ps, i )
p = own price; pc = price of complements;
ps = price of substitutes; i = income
demand
supply, qs, of an individual commodity:
qs = f( p, pc, pf, ps )
p = own price; pc = price of complement products;
ps = price of substitutes; pf = price of factor inputs
supply

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?

9. Answering the How Much? question
Need to estimate parameters
that are both:
1. unknown
and
2. unobservable

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.

11. The Statistical Model c = f(i) + u Actual vs. Predicted Consumption:
Actual = systematic part + random error
Systematic part provides prediction, f(i),
but actual will miss by random error, u.
Consumption, c, is function, f, of income, i, with error, u:
c = f(i) + u

12. The Consumption Function
c = f(i) + u
The Consumption Function
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

13. f(X) = 1 + 2 educ + 3 experi + 4 training
W = f(X) + u
The Wage Function
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

14. The Econometric Model y = 1 + 2 X2 + 3 X3+ 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.

15. Terminology and Notation
Y = 1 + 2 X + u
Left hand-side
Variable:
Dependent
Explained
Predictand
Regressand
Response
Endogenous
Right hand-side
Variable:
Explanatory
Independent
Predictor
Regressor
Stimulus or control
Exogenous

16. Controlled (experimental) vs. Uncontrolled (observational)
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 consump-
tion, Y: price, X1, and income, X2, vary at the same time.

17. Econometric model economic model economic variables and parameters.
statistical model
sampling process with its parameters.
data
observed values of the variables.

18. Time series data

19. Cross-section data and Pool (Panel) data

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

21. Economic Empirical Study
Economic Theory; Past Experience, studies
Formulating a model: Cause - effect
C = f(Inc) ==>
Ct = 1 + 2Inct + ut
Gathering data:
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