Lecture 1: Introduction
Applied Econometrics
Le Anh Tuan, Ph.D.
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Course Information
►Lecturer info:
►Le Anh Tuan, Ph.D. in Finance
►Email: tuan.le@isb.edu.vn
►Page: https://sites.google.com/view/anhtuanle
►Research Interests: corporate finance, corporate
governance, labor economics, banking,
institutional quality, policy uncertainty shocks.
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Course Information
►Course requirements:
3
►Practical exercises
10%
►Mid-term exam (Sec. 7):
20%
►Research Project
►Proposal (Sec. 4)
►Final Project (Sec. 12)
20%
►Final Exam
50%
Course Information
►Recommended reading:
►Lecture notes and presentations.
►Wooldridge, J. M. Introductory econometrics: a
modern approach, 7e. Mason: South-Western,
2020.
►Gujarati, D. N. Basic econometrics, Boston:
McGraw-Hill, 2003.
►Virtually any other book on econometrics.
►Software:
►Stata
►Any others
4
Tentative schedule
►Session 1: Introduction (Chap. 1)
►Session 2: Simple Regression Model (Chap. 2)
►Session 3: Multiple Regression Model (Chap. 3)
►TA session
►Session 4: Testing Hypothesis 1 (Chap. 4)
►Proposal Presentation
►Session 5: Testing Hypothesis 2 (Chap. 4)
►TA session
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►Session 6: Further issues (Chap. 6)
►Session 7: Mid-term exam
Tentative schedule
►Session 8: Regression with Dummy Variables (Chap. 7)
►TA session
►Session 9: Heteroskedasticity (Chap. 8)
►Session 10: Time Series and Panel Data (Chap. 10, 14,
15)
►TA session
►Session 11: Final Research Project Presentation
►Session 12: Review and Final exam
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Session 1. Introduction
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Introduction
►What is Econometrics?
►Typical questions in econometrics.
►The Structure of Econometric Data
►Empirical Economic Analysis
►Introduction to Stata
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What is Econometrics?
► Economics vs. Econometrics
economics: focus on “how” and “why”
econometrics: focus on “how much” and “by how much”
► Example
Economist: “If the government increases corporate tax, firm
profit will reduce.”
Econometrician: “If the government increases corporate tax
by 1%, firm profit will reduce by 5%.”
► Econometrics = use of statistical methods to analyze
economic data
► provides “both a numerical answer to the question and
a measure how precise the answer is (Stock and
Watson, 2007, p. 7)”,
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What is Econometrics?
► Typical goals of econometric analysis
► Estimating relationships between economic variables
► Testing economic theories and hypotheses
► Forecasting economic variables
► Evaluating and implementing government and
business policy
10
Typical questions in econometrics.
►Three basic types of econometric questions:
►Descriptive
►Forecasting
►Causal (or structural)
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Descriptive questions
► Typical questions:
How much do men and women earn annually on average in the UK?
How long do recessions typically last?
How does medical insurance coverage vary with income?
How are amenities of a house reflected in the house’s price?
► all these question mean “how much ... on average or typically”
► Descriptive type of questions is the simplest one.
► Main trait: if we had enough data, we would know the answer for
sure
► Example: if you have a complete (and accurate) list of all UK
citizens’ income, you can answer the first question in the list above
► Challenges:
► Sampling: how to make conclusions based on a sample
rather than the whole population (→ random sampling &
statistical inference).
► summary statistics: how to summarize the (quantitative)
answer in a nice, brief and comprehensible way
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Forecasting Questions
► Typical questions:
What will the stock price of Vinamilk be on 14th October?
Will you pass this course?
How long will the Covid-19 last this year?
► We can never know the answers for sure in advance; however,
there might be very high stakes behind these questions
► Good prediction VNM stock price= VND 200,000
► Common traits:
► If we wait long enough, we’ll know the answer
► Inferences based on time-related data (i.e., time series)
► Highly visible applications of econometrics: forecasts of
macroeconomic indicators (interest rates, inflation, GDP etc.)
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Causal Questions
► Typical questions:
What is the effect of anti-corruption campaign on corruption
level?
How would board reforms influence...
firm transparency?
firm performance?
How much more money will you earn as a result of getting
bachelor degree?
► Note the cause and effect elements in the previous questions
► The presence of a causal link is suggested by economic
theory (or common sense), the goal of econometric analysis
is either to empirically verify or quantify this causal link.
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Causal Questions
► Main tool: Multiple regression model
► It allows to quantify the effect of a change in one variable on
another variable, holding other things constant (ceteris
paribus analysis).
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Causality & Ceteris paribus
► Causality: Definition of causal effect of x on y :
“How does variable y change if variable x is changed
but all other relevant factors are held constant”
► Ceteris paribus = “other things are held constant/fixed”
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Causality & Ceteris paribus
► Causal effect of CSR on firm performance
• “By how much will the profit of a firm increase if one
increases the amount of CSR investment”
• Implicit assumption: all other factors that influence firm profit
such as quality of firm size, firm leverage, tangible assets etc.
are held fixed.
► Experiment
• Choose several firms; randomly assign different amounts of
CSR investment to the different firms; compare the profits
► Economectrics analysis
• Using data to analyze
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The Structure of Economectric Data
► Economic data sets
• Cross-sectional data
• Time series data
• Pooled cross sections
• Panel/Longitudinal data
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Cross-sectional data
obs
wage
educ
Exp
Age
female
1
20.40
20
10
34
1
2
10.29
15
8
30
0
…
…
…
…
…
…
500
40.39
30
12
45
1
► 1 observation = information about 1 cross-sectional unit
► Cross-sectional units: individuals, households, firms, cities, states
► Data taken at a given point in time
► Typical assumption: units form a random sample from the whole
population → the notion of independence of the units’ values
► Possible violations:
• Censoring: wealthier families are less likely to disclose their wealth
• Small population: neighboring states influence one another, their
indicators are not independent.
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Time series data
Year
Stock
Price
Volumn
(x1000)
Return
(%)
High
Low
2000
15.00
1,000
4
23
8.00
2001
16.20
7,000
8
25
10.97
…
…
…
…
…
…
2022
50.33
24,000
12
100
20.22
► Observations on economic variables over time: stock prices, money
supply, CPI, GDP, inflation rates,…
► Frequencies: daily, weekly, monthly, quarterly, annually
► Ordering is important here!
• Behaviour of economic subject (and the resulting indicators) evolve in a
gradual manner in time
• Lags in economic behaviour (stock prices today affect next month’s
actions)
► Typically, observations cannot be considered independent across time →
require more complex econometric techniques.
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Pooled cross sections
►
►
►
►
obs
Year
wage
educ
Exp
Age
female
1
2010
20.40
20
10
34
1
…
…
…
…
…
…
…
500
2010
10.29
15
8
30
0
501
2020
15.02
12
4
40
1
…
…
…
…
…
…
…
1000
2020
40.39
30
12
45
1
Both cross-sectional and time-series features
Data collected in multiple (typically, two) points in time
Ordering is not crucial, year is recorded as an additional variable
Often used to evaluate the effect of a policy change
• collect data before and after the policy change and see how the
relationship between the variables changes
► Note: in the second time period, the cross-sectional units need be neither
distinct from nor identical to those in the first period.
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Panel/Longitudinal Data
Firm
1
Firm
2
Firm
Year
Size
Leverage
Ratio (%)
Tangible
Ratio (%)
Age
Cash
ratio
1
2020
4.44
15
25
30
10
1
2021
4.98
14
39
31
15
1
2022
5.02
23
41
32
21
2
2020
8.00
6
12
4
9
2
2021
7.53
4
11
5
11
2
2022
8.49
10
18
6
10
…
…
…
…
…
…
…
► Are a collection of cross-sectional data for at least two different
points/periods of time.
► Unlike with pooled cross sections, the same units are measured over time
• More difficult /costly to obtain the data
• Have several advantages over (pooled) cross sections (for
problem where panel data make sense)
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Panel/Longitudinal Data
► Use of double index: it where i = 1,...,n and t = 1,...,T.
► Typical problem: missing values - for some units and periods there are
no data.
► Maybe balanced panel data or unbalanced panel data.
► Example: firm performance of 500 Vietnamese listed firms from 2000
to 2019 where all Vietnamese firms were chosen for the sample 2000
and kept fixed for all subsequent years (T =20,n=500, N=10,000)
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Steps in Empirical Economic Analysis
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Steps in Empirical Economic Analysis
► Step 1: Formulate the question of interest.
► Step 2: Find a suitable economic model.
► Step 3: Turn it into an econometric model.
► Step 4: Obtain suitable data.
► Step 5: Use econometric methods to estimate the
econometric model.
► Step 6: If needed, use hypothesis tests to answer the
question from step 1.
► Step 7: Diagnostics of correct model specification.
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Steps in Empirical Economic Analysis
► Step 1: Formulate the question of interest.
?
Does political risk influence corporate cash
holdings in Vietnam?
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Steps in Empirical Economic Analysis
► Step 2: Find a suitable economic model
• formal relationships between economic variables
based on prior papers.
• y = f(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12)
y
x1
x2
x3
x4
x5
x6
x7
x8
x9
x10
x11, x12
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corporate cash holdings
political risk
firm size
firm age
firm leverage
sales growth
net working capital
tangible assets
profitability
capital expenditures
dividend policy
ownership structure
Steps in Empirical Economic Analysis
► Step 3: Turn it into an econometric model.
► Solve quantification issues
• How can we measure corporate cash holdings?
• How can we measure political risk?
• How can we measure control variables (e.g., size)?
üUsing the literature
ü Note that there exists a large variety of econometric models and
model choice depends very much on the research question, the
underlying economic theory, availability of data, and the structure
of the problem.
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Steps in Empirical Economic Analysis
► Step 3: Turn it into an econometric model.
► Build an economectric model:
Error terms: unobserved factors, measurement errors,
random nature of firm behavior.
► Firm control: firm size, firm age, firm leverage, sales
growth, net working capital, tangible assets, profitability,
capital expenditures, dividend policy, ownership
structure
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Steps in Empirical Economic Analysis
► Step 4: Obtain suitable data.
• Firm-level data: Orbis
• Country-level data: International Country Risk
Guide (ICRG).
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Steps in Empirical Economic Analysis
► Step 5: Use econometric methods to estimate the
econometric model.
► Step 6: If needed, use hypothesis tests to answer the
question from step 1.
► Step 7: Diagnostics of correct model specification.
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Steps in Empirical Economic Analysis
► Political risk is
positively associated
with corporate cash
holdings`
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Introduction to Stata
► Data input
• Manual data entry
• Copy Excel file and Paste
• Import data
► Obtain statistics value
► Data transformation
► Basic command
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