This document discusses econometrics and its applications. It defines econometrics as using statistical methods to estimate economic relationships and test economic theories. Econometrics allows estimating relationships between economic variables, testing hypotheses, and forecasting. It helps explain qualitative economic data quantitatively and evaluate government policies. Common econometric methods discussed include simple and multiple linear regression, estimation theory, and time series analysis. The document also notes some limitations of econometrics, such as not proving causation and possible issues with data interpretation.

1. Khusnur Jahan Shapna
Student ID# 182102
Master of Development Studies
Development Studies Discipline
Khulna University
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1. Introduction
2. Understanding econometrics
3. Importance of studying econometrics
4. Approaches of econometrics
5. Elements of econometric analysis
6. Econometric techniques
7. Examples
8. Limitations
9. References

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The Application of
Statistical tool set evaluation
Econometrics
Econometrics
Mathematical
Tools
Economic
Theory
Statistical
Methods Regression Analysis
Relationship of interest.
A regression is the
relationship between one
or several independent
variables and the
expected value of a
corresponding dependent
variable.Testing and forecasting of
hypothesis

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y=f(X1, X2,……….Xn)
For example, Here,
y= dependent variable
X= independent variables
f= function
Marks=f (Study Hour)
Study Hour
Marks = Study Hour
0
20
40
60
80
100
120
0 10 20 30 40 50
Marks

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Why we do econometrics?
It makes estimating relationships between economic variables
very easy
It helps testing economic theory and hypothesis
Its great for forecasting economic variables
It helps evaluating or implementing government or business
policy
It helps explaining qualitative data into quantitative one

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Theoretical
Applied
It use to
translate
qualitative
economic
statement into
quantitative
ones
Mathematics
Theoretica
l Statistics
Numerical
Methods

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Econometrics deals with mainly 3 types of economic
data, i.e.
Observation over timeTime Series
• i.e. GDP measures for 10 years in one specific country such as
Canada. So Canada’s GDP for 10 years.
Observation in certain set of individuals over a specific timeCross Sectional
• Canada’s and UK’s GDP in 2005
Observation over time and over individualsPanel data
• Annual GDP over a certain set of countries between a set of years.
So, The annual GDP of Canada and UK between 2000 and 2005.

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The more common methods of econometrics is,
1. Simple/Multiple linear regression
2. Estimation theory
3. Linear programming
4. Frequency distribution
5. Probability distribution
6. Correlation and regression
7. Time series analysis
8. Simulation analysis

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Simple/Multiple linear regression
0
20
40
60
80
100
120
0 10 20 30 40 50
Marks
Study Hour
Or
Y=β0 + β1X + e
Here,
β0 = Intercept
β1 = m(Slop)
e = error term
b
m
e
Function is,
Y=b + mX + e

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Simple/Multiple linear regression can develop a
relationship on the basis of past trends
For example,
Imagine, Rayhan has an income of $ 50,000. Spending pattern of his income
is 50% for the HH expense of his gross income earned during the period. So it
can be hypothesized that, as Rayhan increases his income, his spending will
also increased.
Then equation would be,
Expenses = β0 + β1X + e
= 10000+50% (50000)
= 35000
So, Rayhan will spend 35000 on the
basis oh his earned income.
Here we can understand that,
1. Strength of the relationship
between income and
consumption
2. Statistically significance of
that relationship

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1. Econometrics is sometime criticized for relying too heavily on
the interpretation of raw data without linking it to establish
economic theory or looking for casual mechanism.
2. It tests hypothesis but neglect concerns of errors
3. In some cases, economic variables cannot be experimentally
manipulated as treatments randomly assigned to subjects
4. It is crucial that, the findings revealed in the data are able to
adequately explained by a theory
5. Regression analysis also does not prove causation and just
because two data sets show an association, it may be spurious
6. Econometrics is largely concerned with correlation, but
correlation does not equal causation.

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Gujarati, D. N. (2009). Basic econometrics. Tata McGraw-Hill Education.
Hendry, D. F. (1997). The econometrics of macroeconomic forecasting. The
Economic Journal, 107(444), 1330-1357.
Wang, T. (2006). Introduction to econometrics. Tsinghua University Press,
Beijing.
Seddighi, H. (2013). Introductory econometrics: a practical approach.
Routledge.
Barreto, H., & Howland, F. M. (2006). Introductory econometrics. Cambridge,
New York.
Vogelvang, B. (2005). Econometrics: theory and applications with Eviews.
Pearson Education.

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