CHAPTER 1

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Transcript CHAPTER 1

BY
A. SHINTA
EMPIRICAL ECONOMETRIC
MODELLING
FRISCH 1933 in Hendry 1995 said that
ECONOMETRICS involves the “MUTUAL
PENETRATION OF QUANTITATIVE
ECONOMIC THEORY AND STATISTICAL
OBSERVATION”
so…
it will examine the CONCEPTS, MODELS,
PROCEDURES and TOOLS OF
ECONOMETRICS and INVESTIGATE THE
SYSTEMATIC APPLICATION OF
ECONOMETRIC METHODS TO THE
ANALYSIS OF ECONOMIC TIME SERIES
DATA
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All models are not born equal, and we seek those
which are useful in practise :
- for understanding economic behavior
- for testing economic theories
- for forecasting the future
- for analysing economic policy
Achieving these four objectives requires discovering
sustainable relationships between observed economic
magnitudes.
To find such relationships entails rejecting models
which lack desirable characteristics, so we must be
able to critically evaluate empirical models and test
their associated theories against the available
evidence
This group of activities constitutes econometric
modelling
EMPIRICAL ECONOMETRIC MODELS ARE
SYSTEMS OF QUANTITATIVE RELATIONSHIPS
LINKING OBSERVED DATA SERIES
There are 4 main roles in economics
1. Data summarize : there exist too many
variables of potential interest in economics for
us to investigate them all, so summarization is
essensial, and econometric models are one way
of doing so
2. Econometric models allow us to interpret
empirical evidence : facsts rarely speak
themselves
3. There are often several competing theoritical
explanations for economic phenomena;
econometric models play an important role in
evaluating the relative explanatory powers of
these theories
4. Econometric models are the primary vehicle for
the accumulation and consolidation of empirical
knowledge about how economies function
.
THE PROBLEMS OF ECONOMETRICS
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These are many and varied
The economy is a complicated, dynamic, non linier,
high – dimensional and evolving entity
It was caused :
- society, social change
- laws change
- tehcnological innovations
- time series data samples are :
* short
* higly agregated
* heterogenous
* non stationary
* time dependent and interdependent
AIMS OF THE COURSE :
ABILITY TO GOOD ECONOMETRICS :
1. REGRESSION METHODS
- ESTIMATION AND TESTING
- DIAGNOSTIC TESTING
- MODEL BUILDING
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2. TIME SERIES METHODS
- STATIONARY SERIES
- NON-STATIONARY SERIES
3. LIMITED DEPENDENT VARIABLES
4. PANEL DATA
REGRESSION METHODS
ESTIMATION the Magnitude of quantitative
relationships
 Evaluating and comparing alternative economic
models
 Example :
a. market research : finding the parameters of
demand or supply curves to compute own price,
cross price, income and supply elasticities
b. How does labor supply, employment and
investment respond to changes in taxes and other
policy variables
c. How will a carbon tax affect pollution levels?
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How will changes in penalties or conviction rates
affect crime rates
How will changing welfare rules affect income
distribution, employment, poverty and health care
So :
Econometric techniques are used in many fields besides
economics :
- Political science
- Psychology
- History
- Biology
- Medical reseacrh
- Law
- Financial analysis
The basic tool for econometrics is the linear
regression model.
Estimating a linear regression on two variables can
be visualized as fitting a line through data points
representing paired values of the independent
and dependent variables.
For example, consider Okun's law, which relates
GDP growth to the unemployment rate. This
relationship is represented in a linear regression
where the change in unemployment rate is a
function of an intercept, a given value of GNP
growth multiplied by a slope coefficient and an
error term, :
Linearity
 The dependent variable is assumed to be a linear
function of the variables specified in the model.
The specification must be linear in its
parameters. This does not mean that there must
be a linear relationship between the independent
and dependent variables. The independent
variables can take non-linear forms as long as
the parameters are linear. The equation qualifies
as linear while , does not.
 Data transformations can be used to convert an
equation into a linear form. For example, the
Cobb-Douglas equation—often used in
economics—is nonlinear:
 But it can be expressed in linear form by taking
the natural logarithm of both sides
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Expected error is zero
The expected value of the error term is assumed to be
zero. This assumption can be violated if the
measurement of the dependent variable is
consistently positive or negative. The missmeasurement will bias the estimation of the intercept
parameter, but the slope parameters will remain
unbiased]
The intercept may also be biased if there is a
logarithmic transformation. See the Cobb-Douglas
equation above. The multiplicative error term will not
have a mean of 0, so this assumption will be violated
This assumption can also be violated in limited
dependent variable models. In such cases, both the
intercept and slope parameters may be biased
Econometric theory uses statistical theory to
evaluate and develop econometric methods.
Econometricians try to find estimators that have
desirable statistical properties including
unbiasedness, efficiency, and consistency. An
estimator is unbiased if its expected value is the
true value of the parameter; It is consistent if it
converges to the true value as sample size gets
larger, and it is efficient if the estimator has
lower standard error than other unbiased
estimators for a given sample size. Ordinary least
squares (OLS) is often used for estimation since
it provides the BLUE or "best linear unbiased
estimator" (where "best" means most efficient,
unbiased estimator) given the Gauss-Markov
assumptions.
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Makasih……