1. chapter1 MÔN KINH TẾ LƯỢNG ECONOMETRICS.pdf

CHƯƠNG 1 KINH TẾ LƯỢNG CLC

1. Introduction to Econometrics
Instructor: Le Hang My Hanh
[email protected]

2. Correlation
Hypothesis
Regression
Estimate
Data
Significant
Forecast
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2
3
4
5
6
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3. Course materials
1.Introduction to Econometrics, 3
rd
Edition, by
Addision-Wesley Series in Economics
2.Basic Econometrics by Gujarati, Fourth
Edition (Ch1-13)
3.Introductory Econometrics-A modern
approach by Jeffrey M. Wooldridge (Ch1-9)
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4. Assessment
•Performance: 10%
•Mid term test + project: 30%
•Final term test : 60%
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5. Instruction for your project
•Each group should write and present a short report ( 25-30 pages all included)
based on the data and introduction given during the course.
•The report should be organized as follows:
Abstract
1.Introduction
Give a brief statement about the purpose of the study.
2. Literature Review
- Summarize the main published work concerning your research question.
- It should be a synthesis and analysis of the relevant published work, linked at all times to
your research question.
3. Methodology and data
-An introduction of your model (dependent and independent variables)
-A description of the data must be provided here. You should discuss the data
sources and the definition of variables and report in a table summary statistics
such as minimum and maximum values, means, standard deviations for each
variable.
4. Results: Estimation results are provided in a table and discussed in this section.
5. Conclusion: you should summarize the results here.
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6. •U can use the proposal for the Research
method course and continue to complete it
•Deadline: 11:59 pm 20/3 for soft copy
hard copy: 15:15pm
[email protected] 6

7. Examples of empirical research
❑Thisthesisexaminestherelationshipbetweenthe
probabilityoffinancialdistressandsomespecificfinancial
ratiosinordertoidentifyinternalfactorscausingdistress
forfirms.(PhuKimYen,K49CLC)
•Findings:Sizehasnegativecoefficientswhichare
statisticallysignificantatsignificancelevelof1%inall
estimations.Thisfindingisconsistentwithpreviousstudy
ofOhlson(1980).Theauthorconcludesthatsizeaffectthe
probabilityoffinancialdistressofVietnameselistedfirms,
especiallythoseonHOSE.Inreality,large-capcompanies
oftenhavemorepowerinitstradingpositionwith
counterpartiesaswellasmoreapproachestofinancing
resources.Therefore,itiseasierforthemtoweather
unexpecteddownturns.
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8. Outline
•Chapter 1: Introduction to Econometrics
•Chapter 2: Simple Regression
•Chapter 3: Multiple Regression
•Chapter 4 : Statistical Inference
•Chapter 5: Diagnosing Model Problems
•Reading papers + Replicating empirical Research +
Presentation
8Le Hang My Hanh, FTU CS2

9. Some keywords
•Dependent variables, independent variables
•Regression, estimation, estimator, estimate
•Empirical research
•Significant, significance, significance level,
confidence interval
•Hypothesis, hypothesis testing
•Assumption
•Correlation, autocorrelation, multicollinearity,
heteroscedasticity, homoscedasticity
•Biased, unbiased
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10. Introduction to Econometrics
The Nature and Purpose of Econometrics
1.Whydo you need to learn Econometrics?
2.What is Econometrics? What will you
learn from the course?
3.Howdo you learn? Methodology of
Econometrics
4.Terminology and notation
5.Types of data
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Whydo you need to learn Econometrics? Economics suggests important relationships, often with policy
implications, but virtually never suggests quantitative
magnitudes of causal effects.
• What is the quantitative effect of reducing class size on
student achievement?
• How does another year of education change earnings?
• What is the price elasticity of cigarettes?
• What is the effect on output growth of a 1 percentage point
increase in interest rates by the Fed?
• What is the effect on housing prices of environmental
improvements?

12. What is Econometrics?
•Econometrics = “economic measurement”.
•“Econometrics may be defined as the social science in
which the tools of economic theory, mathematics, and
statistical inferenceare applied to the analysis of
economic phenomena” (Goldberger 1964).
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In this course you will: • Learn methods for estimating causal effects using
observational data
• Focus on applications – theory is used only as needed to
understand the “why”s of the methods;
• Learn to evaluate the regression analysis of others – this
means you will be able to read/understand empirical
economics papers in other econ courses;
• Get some hands-on experience with regression analysis in
your problem sets.

14. 1.2. Methodology of Econometrics
1. Statement of 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.

15. Example
1. Statement of Theory or Hypothesis
•Keynes states that on average, consumers increase
their consumption as their income increases, but not
as much as the increase in their income (MPC < 1).
•MPC= marginal propensity to consume

16. Example
2. Specification of the Mathematical Model of
Consumption (single-equation model)
Y = β
1+ β
2X 0 < β
2< 1 (1)
Y = consumption expenditure (dependent variable)
X = income (independent or explanatory variable)
β
1= the intercept
β
2= theslope coefficient
•The slope coefficient β
2measures the MPC.
MPC= marginal propensity to consume

17. Example
Geometrically,
•Geometrically,

18. Example
3. Specification of the Econometric Model of Consumption
•Other variables can affect consumption expenditure: size of family,
ages of the members in the family, family religion →the inexact
relationships between economic variables
•To allow for the inexactrelationships between economic variables,
(1) is modified as follows:
•Y = β
1+ β
2X + u (2)
•where u = the disturbance, or error, term, a random (stochastic)
variable that has well-defined probabilistic properties.
•u may well represent all those factors that affect consumption but
are not taken into account explicitly.

19. Example
•(2) is an example of a linear regression model, i.e., it hypothesizes
that Y is linearly related to X, but that the relationship between the
two isnot exact; it is subject to individual variation. The econometric
model of (2) can be depicted as shown in Figure 2.

20. Example
4. Obtaining Data
•Y = personal consumption expenditure (PCE)
•X = gross domestic product (GDP)

21. Example
5. Estimation of the Econometric Model
•Regression analysis is the main tool used to obtain the
estimates. We obtain the estimates
β
1
^
= −184.08 and β
2
^
= 0.7064
Yˆ = −184.08 + 0.7064X
i (3)
→An increase in real income of 1 dollar led, on average,
to an increase of about 70 cents in real consumption.

22. Example
The data are plotted in Figure I.3

23. Example
6. Hypothesis Testing
•Keynes expected the MPC to be positive but less than 1.
•In our example MPC= 0.70 →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.
•Such confirmation or refutation of economic theories on
the basis of sample evidence is based on a branch of
statistical theory known as statistical inference
(hypothesis testing).

24. Example
7. Forecasting or Prediction
•To illustrate, suppose we want to predict the mean
consumption expenditure for 2015. The GDP value for
2015 was 7269.8billion dollars consumption would be:
Yˆ2015 = −184.0779 + 0.7064 (7269.8) = 4951.3
8. Use of the Model for Control or Policy Purposes
•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?

25. Terminology and notation
Unless stated otherwise:
•The letter Y will denote the dependent variable
•The X’s will denote the independent variables, Xkbeing the
k
th
explanatory variable.
•The subscript ior t will denote the i
th
or the t
th
observation
or value.
•N will denote the total number of observations or values in
the population,
•n will denote the total number of observations in a sample.
•u or e will denote the random error or stochastic
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26. Terminology and notation
•In the literature the terms dependent variable
and explanatory variable are described
variously. A representative list is:
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27. 1.3. Types of data
•There are three types of data empirical
analysis: time series, cross-section, and panel
data.
•Time series data: a set of observations on the
values that a variable takes at different times.
It is collected at regular time intervals, such
as daily, weekly, monthly, quarterly, annually.
Ex: weekly stock return, monthly interest rate,
GDP growth, CPI and so on.
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28. 1.3. Types of data
•Cross-section data:data on one or more
variables collected at the same point in time.
Ex: the census of population conducted by
the Vietnam General Statistics Office every 10
years. Profits of listed firms in 2014.
•Panel data/ Pooled data: set of combination of
time series and cross-section.
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29. Example of panel data
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30. The accuracy of data
The results of research are only as good as
the quality of the data.
•If in given situations researchers find that the
results of the research are “unsatisfactory”,
the cause may be not that they use the wrong
model but that the quality of the data was
poor.
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31. Measurement Scales of Variables
•Four broad categories: ratio scale, interval scale,
ordinal scale and nominal scale.
•Ratio scale: GDP growth rate, interest rate, ROE.
Most economic variables belong to this category.
•Interval scale: the distance between two time
periods, say (2000-1995)
•Ordinal scale: income class (upper, middle,
lower), grading systems (A,B, C grades)
•Nominal scale: gender (male, female), marital
status (married, unmarried, divorced, separated)
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32. 1.4 Review of statistics
•Empericalproblem:Classsizeand
educationaloutput
–Policyquestion:Whatistheeffectontest
scores(orsomeotheroutcomemeasure)of
reducingclasssizebyonestudentperclass
–Wemustusedatatofindout(isthereanyway
toanswerthiswithoutdata?)
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33. Example: The California Test Score Data Set
AllK-6andK-8Californiaschooldistricts(n=420)
Variables:
•5
th
gradetestscores(Stanford-9achievementtest,
combinedmathandreading),districtaverage
•Student-teacherratio(STR)=no.ofstudentsinthe
districtdividedbyno.full-timeequivalentteachers
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35. Scatterplot of test score v. student –teacher ratio
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Do districts with smaller classes have higher test scores?

36. Weneedtogetsomenumericalevidenceonwhetherdistricts
withlowSTRshavehighertestscores–buthow?
1.CompareaveragetestscoresindistrictswithlowSTRs
tothosewithhighSTRs(“estimation”)
2.Testthe“null”hypothesisthatthemeantestscoresin
thetwotypesofdistrictsarethesame,againstthe“alternative”
hypothesisthattheydiffer(“hypothesistesting”)
3.Estimateanintervalforthedifferenceinthemeantest
scores,highv.lowSTRdistricts(‘confidenceinterval”)
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37. Initial data analysis: Compare districts with
small (STR<20 ) and large (STR>=20) class sizes
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38. a. Estimation
Isthisalargedifferenceinareal-worldsense?
•Standarddeviationacrossdistricts=19.1
•Differencebetween60
th
and75
th
percentilesof
testscoredistributionis667.6–659.4=8.2
•Thisisabigenoughdifferencetobeimportantfor
schoolreformdiscussions,forparents,orfora
schoolcommittee?
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39. b. Hypothesis testing
•Difference-in-means test: compute the t-
statistic:
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41. c. Confidence interval
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42. 1.5 Review of probability
a.Population, random variable, and distribution
b.Moments of a distribution (mean, variance,
standard deviation of a deviation, covariance,
correlation)
c.Conditional distributions and conditional
means
d.Distribution of a sample of data draw
randomly from a population: Y
1, …, Y
n
[email protected] 42

43. [email protected] 43

44. Population distribution of Y
•TheprobabilitiesofdifferentvaluesofYthatoccur
inthepopulation,forex.Pr(Y=650)(whenYis
discrete)
•Or:Theprobabilitiesofsetsofthesevalues,forex.
Pr(640<=Y<=660)(whenYiscontinuous)
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55. Distribution of Y
1,…, Ynunder simple random sampling
Becauseindividuals#1and#2areselectedatrandom,thevalueof
Y1hasnoinformationcontentforY2.Thus:
•Y1andY2areindependentlydistributed
•Y1andY2comefromthesamedistribution,thatis,Y1,Y2are
identicallydistributed
•Thatis,undersimplerandomsampling,Y1andY2are
independentlyandidenticallydistributed(i.i.d.).
•Moregenerally,undersimplerandomsampling,{Yi},i
=1,…,n,arei.i.d.
Thisframeworkallowsrigorousstatisticalinferencesabout
momentsofpopulationdistributionsusingasampleofdata
fromthatpopulation…
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56. Some database
•IMF data: http://www.imf.org/en/Data
•ADB data: http://www.adb.org/data/statistics
•WB data:
http://data.worldbank.org/vietnamese
•GSO: https://www.gso.gov.vn/
•https://www.quandl.com/collections/vietnam
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