Кафедра економічної кібернетики, Сумський державний університет Economic Cybernetics Department, Sumy State University https://ek.biem.sumdu.edu.ua/

1. INTRODUCTION TO
ECONOMETRIC MODEL

2. Econometrics ⇒ Economic measurements
ECONOMIC
MEASUREMENTS
Governments, central
banks and policy
organizations
(guide monetary policy,
fiscal policy, education
and training, health, and
transfer policies)
Businesses
(management and control - hiring,
production, inventory, investment, ...;
marketing - pricing, distributing, advertising;
accounting - budgeting revenues and
expenditures
Financial organizations
(asset management,
asset pricing, mergers
and acquisitions,
investment banking, and
insurance etc)

3. ORDER OF THE EMPIRICAL ANALYSIS
1. Economic model 2. Econometric model 3. Data Analysis
The economic theory proposes models
that explain behavior of one or more
variables as a function of some other
variables, which are determined
outside of the model.
 Formal model: Mathematical
equations describing the
relationship between the variables
 Informal model: based on the
theory and more intuitive aspects
Econometric models are
constructed from economic data
with the aid of the techniques of
statistical inference. These models
are usually based on economic
theories that assume optimizing
behavior on the part of economic
agents.
After determining an economic
model, and corresponding
econometric model to answer the
questions of interest, we analyze
the data, i.e., estimate the
unknown parameters

4. THE WAYS IN WHICH ECONOMETRIC MODELING USE
 Estimating economic parameters, such as elasticities, efficiency, using
econometric methods
 Predicting economic outcomes and other indicators, such as the tax
revenues in Ukraine for the next five years
 Testing economic hypotheses, such as the question of whether
newspaper advertising is better than store displays for increasing sales

5. Steps involved in
forming an
econometric model

6. How Are Data Generated
EXPERIMENTAL
DATA
NONEXPERIMENTAL DATA
One way to acquire information
about the unknown parameters of
economic relationships is to
conduct or observe the outcome
of an experiment.
conduct or observe information
from survey

7. Data may be collected at various levels of aggregation
 Micro data collected on individual economic decision-making units
such as individuals, households, and firms.
 Macro data resulting from a pooling or aggregating over individuals,
households, or firms at the local, state, or national levels.
Data may also represent a flow or a stock
 Flow - outcome measures over a period of time, such as the
consumption of gasoline during the last quarter of 2018.
 Stock - outcome measured at a particular point in time, such as the
asset value of the Wells Fargo Bank on July 1, 2019.

8. Data may be quantitative or qualitative
 Quantitative -outcomes such as prices or income that may be
expressed as numbers or some transformation of them, such as real
prices or per capita income.
 Qualitative - outcomes that are of an ‘‘either-or’’ situation. For
example, a consumer either did or did not make a purchase of a
particular good, or a person either is or is not married
Types of data
 Time Series Data
 Cross-Sectional Data
 Pooled Cross Sections
 Panel or Longitudinal Data

9. A time series data set consists of observations on a variable or several
variables over time.
Examples of time series data include stock prices, money supply, consumer price index,
gross domestic product, annual homicide rates, and automobile sales figures.
Key features of time series data:
 economic observations can rarely be assumed to be
independent across time.
 require special attention is the data frequency at
which the data are collected.
Figure 1 Real U.S. GDP, 1980–2008

10. A cross-sectional data set consists of a sample of individuals, households, firms,
cities, states, countries, or a variety of other units, taken at a given point in time.
Key feature of cross-sectional
series data
data have been obtained by random sampling from the underlying
population
Figure 2 A Cross-Sectional Data Set on Wages
and other individual characteristics
Figure 3 A Data Set on economic growth rates
and country characteristics

11. Pooled Cross Sections include both cross-sectional and time series
features.
A pooled cross section is analyzed much like a standard cross section, except that we often need to
account for secular differences in the variables across the time. In fact, in addition to increasing the
sample size, the point of a pooled cross-sectional analysis is often to see how a key relationship has
changed over time.
Figure 4 Pooled cross Sections: two years of
housing prices

12. A panel data (or longitudinal data) set consists of a time series for each
cross-sectional member in the data set
Figure 4 A two-year panel Data Set on city
crime Statistics
Key feature of panel data  panel data require replication of the same units
over time, panel data sets, especially those on
individuals, households, and firms, are more
difficult to obtain than pooled cross sections. That’s
why, observing the same units over time leads to
several advantages over cross-sectional data or
even pooled cross-sectional data.
 having multiple observations on the same units
allows us to control for certain unobserved
characteristics of individuals, firms, and so on
 allow us to study the importance of lags in
behavior or the result of decision making.

13. Econometric software packages for modelling economic data
Package software supplier
EViews QMS Software
GAUSS Aptech Systems
LIMDEP Econometric Software
MATLAB The MathWorks
RATS Estima
SAS SAS Institut
SPSS SPSS
Statistica StatSoft