The document provides an introduction to econometrics methodology. It outlines the typical steps as: 1) stating an economic theory or hypothesis, 2) specifying a mathematical model, 3) specifying an econometric model, 4) collecting data, 5) estimating model parameters, 6) hypothesis testing, 7) forecasting, and 8) using the model for policy purposes. It then gives an example applying these steps to Keynes' consumption theory and the relationship between consumption and income.

1. INTRODUCTION TO ECONOMETRICS

2. nternational Journal of Science
Engineering and Management
nternational Journal of Science
Engineering and Management

4. Outcomes of lecture
• Develop basic understanding of econometrics
• Analyze the methodology used in economic model

5. METHODOLOGY OF ECONOMETRICS
Statement of Economic theory
Specification of the Mathematical model
Specification of the Econometric model
Obtaining Data
Estimation of econometric model
Hypothesis testing
Forecasting or prediction
Use of the model for policy purposes

6. METHODOLOGY OF ECONOMETRICS
• Broadly speaking, traditional econometric methodology
proceeds along the following lines:
1. Statement of Economic theory or hypothesis.
2. Specification of the mathematical model of the theory
3. Specification of the statistical, or econometric, model
4. Collecting the data
5. Estimation of the parameters of the econometric model
6. Hypothesis testing
7. Forecasting or prediction
8. Using the model for control or policy purposes.
• To illustrate the preceding steps, let us consider the well-
known Keynesian theory of consumption.

7. 1. Statement of Economic Theory or Hypothesis
• Keynes states that on average, consumers increase their
•
consumption i n c r e a s e as their income increases, but not as
much as the increase in their income. (MPC < 1).
MPC= Rate of change consumption by change in income.
2. Specification of the Mathematical Model of Consumption
(single-equation model)
Y = a + β1X 0 < β1 < 1
Y = consumption expenditure and (dependent variable)
X = income, (independent, or explanatory variable)
a = the intercept
β1 = the slope coefficient
• The slope coefficient β1 measures the MPC.

8. • Geometrically,

9. 3. Specification of the Econometric Model of Consumption
• The relationships between economic variables are generally
inexact. In addition to income, other variables affect consumption
expenditure. For example, size of family, ages of the members in the
family, family religion, etc., are likely to exert some influence on
consumption.
• To allow for the inexact relationships between economic variables,
(I.3.1) is modified as follows:
• Y = a1 + β1X + u
• where u, known as the disturbance, or error, term, is a random
(stochastic) variable. The disturbance term u may well represent all
those factors that affect consumption but are not taken into
account explicitly.

10. • it hypothesizes that Y is linearly related to X, but that the relationship
between the two is not exact; it is subject to individual variation. The
econometric model of can be depicted as shown in Figure I.2.

11. MCQ
• Consider the following simple regression model of
house prices: house_price = b0+ b1*land_size + u.
What is b1 ?
• (a) land_size.
• (b) the distance to the city.
• (c) slope parameter.
• (d) intercept parameter.

12. MCQ
• In the equation, y=β0+β1x1+β2x2+u
β0 is a(n) _____.
• (a) independent variable
• (b) dependent variable
• (c) slope parameter
• (d) intercept parameter

13. 4. Obtaining Data
• To obtain the numerical values of a and β1, we need data. Look at
Table I.1, which relate to the personal consumption expenditure
(PCE) and the gross domestic product (GDP). The data are in “real”
terms.

14. The data are plotted in Figure I.3

16. 5. Estimation of the Econometric Model
• Regression analysis is the main tool used to obtain the estimates.
Using this technique and the data given in Table I.1, we obtain the
following estimates of a and β1, namely, −184.08 and 0.7064. Thus,
the estimated consumption function is:
• Yˆ = −184.08 + 0.7064Xi
• The estimated regression line is shown in Figure I.3. The regression
line fits the data quite well. The slope coefficient (i.e., the MPC) was
about 0.70, an increase in real income of 1 dollar led, on average, to
an increase of about 70 cents in real consumption.

17. 6. Hypothesis Testing
• That is to find out whether the estimates obtained in, Eq. (I.3.3) are
in accord with the expectations of the theory that is being tested.
Keynes expected the MPC to be positive but less than 1.
• In our example we found the MPC to be about 0.70.
• But before we accept this finding as confirmation of Keynesian
consumption theory, we must enquire whether this estimate is
sufficiently below unity.
• In other words, is 0.70 statistically less than 1? If it is, it may
support Keynes’ theory.

18. 7. Forecasting or Prediction
• To illustrate, suppose we want to predict the mean consumption
expenditure for 1997. The GDP value for 1997 was 7269.8 billion
dollars consumption would be:
Yˆ1997 = −184.0779 + 0.7064 (7269.8) = 4951.3
• Now suppose the government decides to propose a reduction in the
income tax. What will be the effect of such a policy on income and
thereby on consumption expenditure and ultimately on
employment?

19. 8. Use of models for control or policy
purpose-
As the estimated model is used to make
policies by appropriate mix of fiscal and
monetary the government can manipulate the
control variable X to produce a desired level of
the target variable Y.

20. Economic Theory
Mathematic Model Econometric Model Data Collection
Estimation
Hypothesis Testing
Forecasting
Application
in control or
policy
studies

21. Scope of Econometrics
• Developing statistical methods for the estimation of
economic relationships,
• Testing economic theories and hypothesis,
• Evaluating and applying economic policies,
• Forecasting,
• Collecting and analyzing non-experimental or
observational data

22. Goals of Econometrics
• The three main aims econometrics are as follows:
1. Formulation and specification of econometric
models
2. Estimation and testing of models
3. Use of models

23. Division of Econometrics
• Two types of Econometrics:
1. Theoretical Econometrics- statistical methods
2. Applied Econometrics- empirical analysis

24. RELATIONSHIPS
 Statistical versus deterministic- In statistical
relationships among variables we essentially deal
with random variables, in deterministic relationships
it also deals with variables
 Regression versus causation- there is no
statistical reason to assume that rainfall doesn’t
depend on crop yield. commonsense says that
inverse relationship in itself cannot logically imply
causation.

25.  Regression versus correlation- the strength of
association between two variables is correlation ,it
is measured by correlation coefficient.
 To estimate or predict the average value of one
variable on the basis of the fixed variable.
 In regression analysis there is an symmetry in the
way the dependent and explanatory variables are
treated. in correlation, variables are symmetrically.

26.  Explained variable
 Predictand
 Regressand
 Response
 Endogenous
 Outcome
 Controlled variable
 Independent variable
 Predictor
 Regressor
 Stimulus
 Exogenous
 Covariate
 Control variable
Dependent variable Explanatory variable

27. TYPES OF DATA IN ECONOMETRICS
 Time series data-
 consists of observations on a variable or several
variables over time.
 Chronological ordering
 Frequency of time series data: hour, day, week, month,
year
 Time length between observations is generally equal
 Examples of time series data include stock prices,
money supply, consumer price index, gross domestic
product, annual homicide rates, and automobile sales
figures.

29. Cross sectional Data
• Data collected at the same point of time
• consists of a sample of individuals, households, firms,
cities, states, countries, or a variety of other units,
taken at a given point in time
• Significant feature: random sampling from a target
population
• Generally obtained through official records of individual
units, surveys, questionnaires (data collection
instrument that contains a series of questions designed
for a specific purpose)
• For example, household income, consumption and
employment surveys conducted by the Turkish
Statistical Institute (TUIK/TURKSTAT)

31. Pooled data
 it is combined ,data are elements of both time series
and cross section data
 consists of cross-sectional data sets that are observed in
different time periods and combined together
 At each time period (e.g., year) a different random sample is
chosen from population
 Individual units are not the same
 For example if we choose a random sample 400 firms in
2002 and choose another sample in 2010 and combine
these cross-sectional data sets we obtain a pooled cross-
section data set.
 Cross-sectional observations are pooled together over time.

33. Panel Data (longitudinal data)
• Also known as Micropanel data.
• it is a special type of pooled data inwhich the
same cross sectional unit is surveyed over time
• consists of a time series for each cross-sectional
member in the data set.
• The same cross-sectional units (firms, households, etc.)
are followed over time.
• For example: wage, education, and employment history
for a set of individuals followed over a ten-year period.
• Another example: cross-country data set for a 20 year
period containing life expectancy, income inequality,
real GDP per capita and other country characteristics

35. • Simple regression model

36. Significance of error term
• Vagueness of theory
• Unavailability of data
• Randomness in human behavior
• Poor proxy variable
• Principle of parsimony (extreme unwillingness)
• Wrong functional form