Sesi 1_Introduction Econometrics.pdf

Ekonometrika Terapan
Week 1
Rus’an Nasrudin Introduction to applied econometrics Feb 6, 2020 1 / 23

What is econometrics
‘Econometrics may be deﬁned as the quantitative analysis
of actual economic phenomena based on the concurrent de-
velopment of theory and observation, related by appropriate
methods of inference.’
Paul Samuelson
Terdapat overlapping statistics vs econometrics, statistikal inferen fokus terhadap statistik inferensi (problem terkait hal
mendasar populasi, sampel yang merupakan bagian dari populasi, pengambilan random sampling yang akan menghasilkan
model serta hasil yang berbeda) sedangkan ekonometrics membahas tentang hubungan kausalitas dengan menggunakan
tools dan metode modelling (usaha untuk mengungkapkan data yang tidak diketahui (apa yang terjadi di semesta alam atau
keadaan kontras di dunia.
Perbedaan Ekonometrics vs data science adalah pendekatan prediksi
data suatu masalah,
Data science fokus ke pendekatan tipe pencocokan kurva untuk
prediksi. Jadi semua model yang cocok dengan data akan dimasukan.
seperti pengalaman sebelumnya yang akan digunakan untuk
meramalkan kemungkinan

The aims of econometrics
– Estimating economic relationships
– Testing economic theories
– Evaluating and implementing policy
– Forecasting
● For Example:
– What is the impact of price on quantity
demanded?
– What is the effect of education on wages?
– How do training programs impact
productivity?

What kind of appropriate inference
Economists mainly concerned with causal inference such as
causal relationship and causal impact.

What is causal inference
‘Causal inference is often accused of being a-theoretical, but
nothing could be further from the truth [Imbens, 2009, Deaton
and Cartwright, 2018]. Economic theory is required in order
to justify a credible claim of causal inference. And economic
theory also highlights why causal inference is necessarily a
thorny task.’
Cunningham

Why econometrics?
Economist always interested in examining relationships between
variable
For example, identifying price elasticity of demand
For what? Business entity makes planning, government makes
policy
To do so, what economists do? Collect data, run a regression and
do hypothesis testing, interpret the result and so on..
This is econometric task

Example: estimating price elasticity of demand
We start we the curiosity from theory: Marshallian demand
function
From the prescription we know that quantity demanded is a
function of price, price of other goods, income, etc..
We then collect data on these variables
Estimate the logaritmic form of quantity on logaritmic form of price
etc
We get the elasticity measure

The objectives of this course
Course objectives
This course intends to stimulate your interest in empirical work
using a modern approach of econometric
Modern? Yes it is
Was there any old econometrics? Knowledge is always a precious
one
Yet, econometricians, statisticians find that some refinement and
new knowledge emerges
We came into era when the econometric work is at the
enthusiasm to identify causality
Specifically, to make causality that is differ from correlation

What makes correlation differs from causation?
Let’s watch this interesting Ted Talk:
https://www.youtube.com/watch?v=8B271L3NtAw&t=15s
Tidak selamanya 2 variabel yang berkorelasi atau memiliki hubungan merupakan hubungan kausalitas atau sebab akibat;
contoh;
- permintaan/penjualan es krim yang semakin naik menyebabkan tenggelam (padahal disini terdapat faktor mendasar) yang tidak
tercermin dalam hubungan berupa cuaca panas. Ketika cuaca panas orang akan banyak berenang dan menyebabkan tenggelam
dalam waktu yang bersamaan orang juga banyak yang membeli es krim. Konklusi yang menyebut kebanyakan es krim akan
menyebabkan tenggelam bukan merupakan hubungan kausalitas dan merupakan logika berfikir yang salah.
- Married man live longer than simple man, marriage is make healthy for man and makes them live "seem" longer (waktunya
berjalan lebih lambat) -> Ekspektasi hiduplah yang membuat pernikahan dapat terjadi (dilihat dari latarbelakang pasangan pria
yang mapan, well educated dll)
- penelitian di th1999, anak kecil yang tidur dengan lampu menyala probabilitasnya tinggi punya pandangan yang pendek dalam
hidupnya di kemudian hari, (berfikiran pendek itu genetik);
contoh lainnya korelasi antara siswa anak-anak yang berprestasi di sekolah, memiliki nilai yang baik dan harga diri yang
tinggi, penelitian lain menyebutkan beberapa faktor yang membuat harga diri tinggi adalah percaya diri dan bangga terhadap
diri sendiri maka prestasi akan mengikuti, penelitian selanjutnya justru menghasilkan korelasi kebalikan bahwa nilai yang
baiklah yang menyebabkan harga dirinya tinggi.
Jika hendak menguji korelasi antara satu variabel dengan variabel lainnya. Ngga cukup punya korelasi aja, Korelasi mungkin
baik untuk memberikan petunjuk apa yang akan terjadi kedepannya. Namun ketika hendak membuat sebuah konklusi
variabel yang satu mempengaruhi variabel lainnya yang perlu kamu tahu adalah kenapa ini terjadi dan bagaimana ini
terjadi??

Course outline
Introduction to econometrics
Review of mathematical statistics and probability theory
Regression theory
Least square
Inference
Impact evaluation with OLS
Omitting variable bias and how to use control
Conditional independence assumption
Double Difference (DD) regression
DD application
Instrumental variable (IV) regression
IV Application
Review

What and why causation
Motivation
Economist have always been interested in eliciting impact of
something on something else
Knowing this impact is important to make some great decision
For example, as social planner I want to choose either give
income transfer unconditionally or conditionally to eligible citizens
In Indonesia, we have options: BLT or PKH
If the aim of the social assistance is to boost vital outcome such
as health and education, knowing the difference between the two
in terms of effectiveness, is important

Ceteris paribus
How do we complete this task?
In the language of economics, if we want to test a pure effect of X
on Y, we hold everything other than X to be constant
By this, we ensure that the induced effect on Y is must be coming
from X
We call this approach ceteris paribus, holding everything else
constant
Otherwise, we cannot separate which one is the effect of X and
which one is from other than X
In a real world of human beings with real activities, ceteris paribus
is almost imposible

Let’s come back to elasticity example
Consider this graphic from Philip Wright’s Appendix B [Wright,
1928] of Cunningham (2018)

Let’s come back to elasticity example
The price elasticity of demand is the solution to the following
equation
∂logQ
=
∂logP
in which we expect to hold supply fixed, the prices of other goods
fixed, income fixed, preference fixed, input cost fixed etc.
We need P that is truly independent, which is fulfiling ceteris
paribus notion

Form of Data Based on Its Structure
• Cross Sectional Data
• Time Series Data
• Pooled Cross Section
• Panel Data

Cross Section Data
o A cross-sectional dataset consists of a
sample of individuals, households, firms, …
taken at a given point in time.
o Cross-sectional datasets are often obtained
from random sampling from the underlying
population.
o If the sample has not been drawn randomly,
our methods may have to be adjusted. For
now, we assume random sampling unless I
say otherwise.

Cross Section Data
o Display here data wage1.dta from Stata

Time Series Data
●A time series data set consists of observations on
one or several variables over time.
●Unlike the arrangement of cross-sectional data, the
chronological ordering of observations in a time
series is important.
●A key feature of time series data that makes them
more difficult to analyze than cross-sectional data is
that observations are unlikely to be independent
over time.
●Special methodological problems arise when we
analyze time series data.

11
Data on Minimum Wage for Puerto
Rico. Avgmin is the average minimum
wage
Avgcov is the percentage of workers covered by the minimum wage
law. Unem is the unemployment rate.
GNP is the gross national product.

12
Pooled Cross Section Data
●Some datasets have both cross-sectional and
time series features.
●Example: household surveys from 1985 and 1990
which are combined to yield one dataset
containing observations from both years.
●May be a useful basis for analysis of change of
policy, for example, we often include time
(year) as an additional explanatory variable in
regressions based on pooled cross-sections.
susenas series
sakernas
DHS

14
Panel (Longitudinal) Data
●A panel dataset consists of a time series for each
cross-sectional member in the data set. Example:

Types of data that we use
Types of data based on the empirical set up
To sum up, when working with empirical task, we face three types of
data
Experimental data: this is ideal data to establish causality as we
generate X and ’isolate’ everything else other than X (we will
come back into this topic later)
Observational data: be careful with this type of data, it is
susceptible to endogeneity problem
Quasi-experiment or natural experiment data: it gives us chance
to get an exogenous X variable
Untuk data panel dan pool
Analisis PKH [karakteristik daerah yg dapat PKH]

Experiment and observational data
But wait, why not to follow the approach used by Physicians or
Medical researchers?
What? Yes it is. Let’s make an experiment and use human being
as the subject in the experiment and make sure that the ceteris
paribus holds
It seems possible.
Yes, that’s way many great development economists now use this
approach. It is called randomised control trial (RCT)
harus random karena
karakteristiknya akan sama (jika
sampelnya besar)

RCT inﬂuence on econometrics and the doubt about
endogeneity
With observational data, as we formally call it, such as household
survey (SUSENAS, IFLS, RISKESDAS):
Any variables extracted from respondent are not in a fulﬁlment of
ceteris paribus
Everything moves, within human being interest, maximisation of
bunch of things
We call them endogenous variables
Indeed, what we want is an exogenous variable
Up to this point, I hope it is clear enough that now RCT is a golden
standard in studying the econometrics of causality (the impact of
something on something)
Techniques that prone to bias (not only the effect of X) because
we use endogenous variable is called suffered from endogeneity
problem

Quasi-experiment and natural experimental data
Is that experiment in the lab or ﬁeld is the only avenue to do a
modern econometrics?
No. There are chances for observational data, as long as it closes
enough to make any variable of interest (the X) is exogenous
So, what is the requirement for X that comes from observational
data can be exogenous?
Let’s start with explaining litle bit what is regression
Untuk melihat
korelasi 2
variabel

Example of experimental data

Example of quasi-experimental data
yang dikasih askeskin
semua orang miskin
(tidak random)

Example of quasi-experimental data

Regression
In examining the relationship between Y and X, economist
usually employs some kind of regression and estimates
the following equation
Y = α + βX + ε
i
i i
β is the measure of the effect of X on Y, while εi
is anything that
we don’t know for the value of apart from explanation done by X.
At weaker notion, everything in ε is held constant is similar to have
situation of that X and ε is not related when we want to know effect
X on Y
We call X like this is a random X
And a random X could come from some quasi or natural
experiment events, for example X is a natural disaster or a policy
event that are totally sursprising and not anticipated by individuals
and so on.

Reading time
Let’s have a look on these articles, and talk about it in terms of
econometrics:
Banerjee, Abhijit, et al. ”Private outsourcing and competition:
Subsidized food distribution in Indonesia.” Journal of Political
Economy 127.1 (2019): 101-137.
Burke, Paul J., Tsendsuren Batsuuri, and Muhammad Halley
Yudhistira. ”Easing the trafﬁc: The effects of Indonesia’s fuel
subsidy reforms on toll-road travel.” Transportation Research Part
A: Policy and Practice 105 (2017): 167-180.
Sparrow, Robert, Asep Suryahadi, and Wenefrida Widyanti.
”Social health insurance for the poor: Targeting and impact of
Indonesia’s Askeskin programme.” Social science & medicine 96
(2013): 264-271.

Sesi 1_Introduction Econometrics.pdf

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Sesi 1_Introduction Econometrics.pdf