chapter one23.ppt-Microsoft Microelectronics

1/1/2016
Prepared by: Dessie M.
Department of Management
Econometrics for finance
By Dessie M. (MSc.)
Addis Ababa, Ethiopia
September 2023

4
Estimation and
testing techniques
Economic
theory
Mathematical
economics
Mathematical
statistics
Economic
statistics
provides theory or
imposes a logical
structure on the
question
expresses economic
theory using
mathematical form
data presentation
and description
Econometrics
.
.
1/9/2024
Prepared by: Dessie M..

 The principal objective of the course, “Econometrics for
Management”, is to provide a comprehensive introduction to
the art and science of econometrics.
 Economic theory, statistical and mathematical methods are
combined in the analysis of economic data, with a purpose of
giving empirical content to economic theories and verify or
refute them.
 Moreover, in the present world, research in economics,
finance, management, marketing, and related disciplines is
becoming increasingly quantitative.
 What is Econometrics?
1/9/2024 Prepared by: Dessie M..

CHAPTER ONE
INTRODUCTION
1.1 Definition and Scope of Econometrics
 Various economics courses suggest many relationships
among economic variables.
 For instance,
 In microeconomics, we learn demand and supply models
in which the quantities demanded and supplied of a good
depend on its price.

Cont…
 In macroeconomics, we study ‘investment function’ to
explain the amount of aggregate investment in the
economy as the rate of interest changes; and
 Consumption function’ that relates aggregate
consumption to the level of aggregate disposable
income.
 Each of such specifications involves a relationship
among economic variables.
 Hence, econometric tools are helpful in explaining the
relationships among variables.

Cont…
 We may be interested in questions such as:
 If one variable changes in a certain magnitude, by
how much wills another variable change?
 Given that we know the value of one variable; can
we forecast or predict the corresponding value of
another?
 The purpose of studying the relationships among economic
variables help us to understand the real economic world we live
in.
 Econometric models: help to understand the relation
between economic and business variables and to analyze the
possible effects of decisions.

Cont…
 The relationships between economic variables have to be
checked against data obtained from the real world.
 If empirical data verify the relationship proposed by
economic theory, we accept the theory as valid.
 If the theory is incompatible with the observed behavior,
we either reject the theory or in the light of the empirical
evidence of the data, modify the theory.

Cont…
 The field of knowledge which helps us to carry out
such an evaluation of economic theories in empirical
terms is called “econometrics”.
 Literally, econometrics means “economic
measurement”, but the scope of econometrics is much
broader as described by leading econometricians.
 Econometrics deals with the measurement of economic
relationships.

Cont…
 “Econometrics is the science which integrates;
 Economic theory,
 Economic statistics, and
 Mathematical economics
o That is to investigate the empirical support of the general
schematic law established by economic theory.

Cont…
 Econometric methods used to obtain numerical estimates
of the coefficients of the economic relationships.
 Measurement is an important aspect of econometrics.
 However, the scope of econometrics is much broader than
measurement.
 Econometrics is basically concerned with measuring of
economic relationships.

Cont…
 In short, econometrics may be considered as the
integration of;
 economics,
 mathematics, and
 statistics
o It provides numerical values for the parameters of
economic relationships and verifying the validity of
economic theories.

Cont…
A. Econometrics Vs. Mathematical Economics
 Mathematical economics states/formulates/expresses economic
theory in terms of mathematical terms/symbols.
 Of course, there is no essential difference between mathematical
economics and economic theory.
 Both state the same relationships, but while economic theory use
verbal exposition, mathematical economics uses mathematical
symbols.
 Both express economic relationships in an exact or deterministic
form.

Cont…
 Neither mathematical economics nor economic theory allows for
random elements which might affect the relationship and make it
stochastic.
 Furthermore, they both do not provide numerical values for the
coefficients of economic relationships.
 Whereas, Econometrics presupposes the economic
relationships to be expressed in mathematical forms, but does not
assume exact or deterministic relationship among variables.

Cont…
 Econometrics assumes random relationships among
economic variables.
 Econometric methods are designed to take into account
random disturbances.
 Furthermore, econometric methods provide numerical
values of the coefficients of economic relationships.

Cont…
B. Econometrics Vs. Statistics
 Econometrics differs from both mathematical statistics and
economic statistics.
 In economic statistics, the empirical data is
o Collected/gathered,
o Recorded,
o Tabulated
o Charted
o Used in describing the pattern in their development
over time.
o The economic statistics is a descriptive aspect of
economics

Cont…
 Statistical methods of measurement are not
appropriate for a number of economic relationships.
 This is due to the fact that the natures of relationships
among economic variables are stochastic or random.
 Econometric methods are adjusted so that they may
become appropriate for the measurement of economic
relationships which are stochastic.

Cont…
 Economic statistics is mainly a descriptive aspect of economics.
 It does not provide explanations of the development of the various
variables and
 It does not provide measurements of the coefficients of economic
relationships.
 Mathematical (or inferential) statistics deals with the method of
measurement which is developed on the basis of controlled
experiments.

1.2 Econometric models Vs. Economic
Models
 A model is the simplified representation of
actual/real phenomena.
 Modelling is an integral part of most scientific
inquiry.
 A model is a compromise between reality and
manageability.

A. Economic Model
 A deliberately simplified analytical framework is called an
economic model.
 Economic models consist of the following three basic structural
elements.
 A set of variables
 A list of fundamental relationships and
 A number of strategic coefficients
 Example; Wage rate =f(education, experience, training)
 Wage = b0 + b1 education + b2 experience + b3 training
 (it doesn't take into account other factors that
affect).

Cont.…
 Economic model: does not contain a random element.
 It postulates exact relationships between economic
variables
 For instance, economic theory postulates that the
demand for a commodity depends on its price, the
prices of other related commodities, consumers’
income and tastes.
 The exact relationship between demand and its determinant can
be written mathematically as: Q = b0 + b1 P + b2 P + b3 Y + b4 t
 This demand equation is exact.

B. Econometric Model
 From the above, we understand that many more other factors
may affect quantity demand.
 Hence, in econometrics, the influence of these ‘other’ factors are
taken into account.
 Econometrics model/relationships: contain a random element
which is ignored by mathematical economic models which
postulate exact relationships between economic variables.
 So, the demand function studied with the tools of econometrics
would be of the stochastic form:
 Q = b0 + b1 P + b2 P + b3 Y + b4 t + u where u stands for the
random factors or disturbance term which affect the quantity
demanded.

Cont.…
 Econometric model includes disturbance terms.
 Why does the disturbance term exist?
 There are several reasons;
 omission of explanatory variables,
 model misspecification,
 functional misspecification,
 measurement error, etc.
 An econometric model will either be linear or non-linear in
parameters and variables.
 Econometric models can be either static or dynamicMost of
econometrics deals with the specification of the error
 Econometric models may be used for hypothesis testing

1.3 Methodology of Econometrics
 Econometric research is concerned with
 the measurement of the parameters of economic
relationships and
 with the predication of the values of economic variables.
 The relationships of economic theory can be measured with
econometric techniques.
 In theory, some variables are postulated as causes of the
variation of other variables

Cont…
 Starting with the postulated theoretical relationships among
economic variables, econometric research generally proceeds
along the following lines/stages.
 Specification of the model;
 Estimation of the model;
 Evaluation of the estimates; and
 Evaluation of the forecasting power of the estimated
model

Cont…
A. Specification of the Model
 In this step the econometrician has to express the relationships
between economic variables in mathematical form.
 This step involves the determination of three important tasks:
a. The dependent and independent (explanatory) variables
b. The size and sign of the parameters of the function.
c. The mathematical form of the model (number of equations,
specific form of the equations, etc.)

CONT…
 The specification of the econometric model will be based on economic
theory and on any available information.
 Thus, specification of the econometric model presupposes knowledge of
economic theory.
 Specification of the model is the most important and the most difficult stage
of any econometric research.
 It is often the weakest point of most econometric applications.
 In this stage there exists enormous degree of likelihood of committing errors
or incorrectly specifying the model.

Cont.…
 Some of the common reasons for incorrect specification of the
econometric models are:
 The imperfections, looseness of statements in economic theories.
 The limitation of our knowledge of the factors which are operative
in any particular case.
 The formidable obstacles presented by data requirements in the
estimation of large models.

Cont…
 The most common errors of specification are:
 Omissions of some important variables from the function.
 The omissions of some equations (for example, in
simultaneous equations model).
 The mistaken mathematical form of the functions.

Cont.…
B. Estimation of the Model
 This is purely a technical stage which requires knowledge of the
various ;
 econometric methods,
 their assumptions and
 the economic implications for the estimates of the parameters.
 This stage includes the following activities.
 Gathering of the data on the variables included in the model.
 Examination of the aggregations problems involved in the
variables of the function.

Cont…
 Examination of the degree of correlation between
the explanatory variables
o i.e. examination of the problem of multicollinearity.
 Choice of appropriate economic techniques for estimation,
o i.e. to decide a specific econometric method to be
applied in estimation; such as, OLS, MLM, Logit, and
Probit.

Cont…
C. Evaluation of the Estimates
 This stage consists of deciding whether the estimates of the
parameters are theoretically meaningful and statistically
satisfactory.
 This stage enables the econometrician to evaluate the results of
calculations and determine the reliability of the results.
 Its Economic reliability is determined by economic theory and
refers to the size and sign of the parameters of economic
relationships.

Cont…
 Whereas, it’s statistical reliability is determined by the
estimates of the parameters of the model. That is by using
 correlation coefficient test,
 standard error test,
 t-test, F-test, and
 R2-test are some of the most commonly used statistical tests.
 The econometric criteria establish whether the estimates have the
desirable properties of un-biasedness, consistency etc.
 Econometric criteria aim at the detection of the violation or
validity of the assumptions of the various econometric
techniques.

Cont…
D. Evaluation of the Forecasting Power of the Model
 Forecasting is one of the aims of econometric research.
 It is possible that the model may be economically meaningful and
statistically and econometrically correct for the sample period for
which the model has been estimated;
 yet it may not be suitable for forecasting due to various factors
(reasons).
 Therefore, this stage involves the investigation of the stability of
the estimates and their sensitivity to changes in the size of the
sample.

Cont…
 Economic theory/model:
 People increase consumption as income increases.
 Mathematical model that is specification of mathematical model:
C = α + βY; 0 < β < 1.
 Econometric model that is specification of the econometric
model: C = α + βY + ɛ; 0 < β (= MPC) < 1.
 Data that is obtaining data
 Estimation of the model that is estimations of parameters of the
model: How? For now, ........................C = 184.08 + 0.8Y
 Interpret the results and use the model for policy or forecasting:

1.3 Desirable Properties of an Econometric Model
 An econometric model is a model whose parameters have been
estimated with some appropriate econometric technique.
 The ‘goodness’ of an econometric model is judged customarily
according to the following desirable properties.
 Theoretical plausibility:
 Explanatory ability
 Accuracy of the estimates of the parameters
 Forecasting ability
 Simplicity

1.4 Goals of Econometrics
 Three main goals/uses of Econometrics are identified
 Analysis/Testing: that is testing economic theory
 Policy-making/evaluation: that is obtaining numerical estimates of the
coefficients of economic relationships for policy simulations. And
evaluation of policies/programs.
 Forecasting/Prediction: that is using the numerical estimates of the
coefficients in order to forecast the future values of economic magnitudes.

1.5 Types of Data for Econometric Analysis
 The success of any econometric analysis ultimately depends on
the availability of the appropriate data.
 Data based on sources is classified in to primary and
secondary.
 Primary data: collected from the sample respondents directly
through informal discussions.
o The researcher himself collects the data from the (sample)
respondents informally, he gets the precise data actually
needed for the research project.
 Secondary data: the data and requisite information collected from
authentic published sources.
o Data collected from those which have already been collected and
analyzed by someone else.

Qualitative versus quantitative data
 Data, as a matter of definition, is quantitative. Thus facts, which
are already expressed as numbers.
 When the variables are qualitative in nature, then the data is
recorded in the form of the indicator function.
 The values of the variables do not reflect the magnitude of the
data.
 They reflect only the presence/absence of a
characteristic.
 For example, variables like religion, sex, taste, etc.
are qualitative variables.

Time series, cross-sectional and
Panel data
 There are three types of data.
 Cross-Section Data
 Time Series Data
 Panel/ Pooled Data
 Econometric methods depend on the nature
of the data used
 Use of inappropriate methods may lead to
misleading results

A. Cross-sectional Data
 These data give information on the variables concerning
individual agents (consumers or producers) at a given
point of time.
 Or it is a data on one/more variables collected at a point
in time.
 Each observation is a new individual, household, firm,
etc... with information at a point in time.

Cont…
 Cross-sectional units: individuals, households, firms, cities,
states data taken at a given point in time.
 Cross sectional data is usually a random sample of the underlying
population.
 Examples: Data on expenditures, income, hours of work,
household composition, assets, investments, employment, etc.
 The census of population conducted by CSA, survey of
consumer expenditure conducted by Addis Ababa University.
 Note that: due to heterogeneity, cross- sectional data have their
own problems.

Cont…

B. Time Series Data:
 Time Series Data: it is a set of observations on the values that a
variable takes at different times (e.g. money supply,
unemployment rate … over years).
 Such data may be collected at regular time intervals: daily,
weekly, monthly, quarterly, and annually and the like.
 Time series data consists of observations on a set of variables
over time.
 Example: Data on stock prices, unemployment rate, GDP and the
like. Data may be qualitative or quantitative

Cont…
 Qualitative data are sometimes called dummy variables
or categorical variable.
 These are variables that cannot be quantified.
 Example: male or female, married or unmarried, religion, and
the like.
 Quantitative data are data that can be quantified.
Example: income, prices, money and the like.

C. Pooled Data
 Pooled Data are repeated surveys of a single (cross-
section) sample in different periods of time.
 They record the behavior of the same set of individual
microeconomic units over time.
 There are elements of both time series and cross
sectional data.

Cont…
 The panel or longitudinal data also called micro panel
data is a special type of pooled data in which the same
cross-sectional unit (say, a family or a firm) is surveyed
over time.
 Cross-sectional observations collected over time, but the
units don’t have to be the same.
 Responses related to the same cross-sectional unit
(individual, firm, country) over time.

Cont…
 The Sources of data: a governmental agency, an
international agency, a private organization or an
individual may collect the data used in empirical
analysis.
 Example: Governmental in Ethiopia: MEDAC, MOF,
CSA, NBE; International agencies: International
Monetary Fund (IMF), World Bank (WB)

Sample, Population, Random Variable
 Sample: a small part or quantity intended to show what the
whole is like.
 It is defined as a smaller set of data that a researcher chooses or
selects from a larger population by using a pre-defined selection
method.
 Sampling is a technique of selecting individual members or a
subset of the population to make statistical inferences from them
and estimate characteristics of the whole population.

Cont…
 Population: is the pool of individuals from which a
statistical sample is drawn for a study.
 It is the total number of observations (i.e., individuals,
animals, items, data, etc.) contained in a given group or
context.
 A sample, in other words, is a portion, part, or fraction
of the whole group, and acts as a subset of the
population.

Cont…
 Random Variable: is a variable whose value is unknown or a
function that assigns values to each of an experiment's outcomes.
 Random variables are often used in econometric regression
analysis to determine statistical relationships among one another.
 A random variable (stochastic variable) is a type of variable in
statistics whose possible values depend on the outcomes of a
certain random phenomenon.

Main Idea
 Econometrics is the science which integrates economics,
mathematics, and statistics for the purpose of providing
numerical values for the parameters of economic relationships
and verifying economic theories.
 Thus, econometrics is special in that:
 It provide numerical values of the coefficients of economic
phenomena
 It assume that economic relationships are not exact.
 It allows for random elements which might affect the
relationship and make it stochastic (random)

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