Samenvatting
Summary/Overview QMAE (only) Panel Data
- Instelling
- Erasmus Universiteit Rotterdam (EUR)
Samenvatting/Overzicht QMAE Panel data . Inclusief alle belangrijke dia's, exercise lectures, eigen aantekeningen en assignments
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Voorbeeld 4 van de 46 pagina's
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Door: wyang1999 • 3 jaar geleden
Voorbeeld van de inhoud
Panel data QMAE 2019-2020Data formats
Cross-sectional Observe individuals (or firms or countries) only once at a given point of time.
data fe: Household economic surveys
Cross-section A single (random) sample from the population of interest
Repeated cross- Two or more (random) samples taken at different points of time from the
sections same population
Longitudinal/ Observe at least some individuals at different points in time. fe:
panel data Administrative data, stocks over a year
Adding time
Repeating the same unit (unit can be person, financial stock, city, country etc.)
Panel data A sample from the population taken at a given point of time and followed
over time
Contains repeated observations on the same unit. fe: stocks, a household
survey held every year
2 kinds of panel data:
Balanced: All individuals are observed in all time periods
Unbalanced: The number of observations varies across individuals
(attrition (loss of follow up), death, etc). We don’t see them in all
waves
Characterized by having two dimensions:
1. Individual unit dimension:
fe: person, country, school
2. Time dimension:
fe: days, months, years
Advantages:
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, Panel data models correct for time-invariant unobservable effects
Panel data may address reverse causality concerns if we exploit the
timing of events
Panel data can improve efficiency
- Dimensions of data are large (Add time as a dimension)
- Panel data models exploit variation across units and over time
- Follow up of same individuals increases efficiency with regards to
repeated cross-sectional data
Disadvantages:
Panel data sets are not always complete and therefore you can have
an unbalanced datasets
- Attrition: Units drop from the sample
o Loss of follow up
o Death
o Refusal to remain in the survey
- Item non-response: Individuals do not respond to certain answers
Challenge if incompleteness is due to endogenous reasons
- Missing at random or selected sample?
- Need to test (LATER!)
Panel effects: individuals may modify reporting across waves
- Learning effects
- Gaming
Panel conditioning: individuals change behaviour because they are in
the panel
- Learning effects
Representativeness of sample
- Composition may change over time
- Sample may become different to population
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,Panel Data Stata
Working with xtset unit_identifier time_variable
panel data: fe: xtset id wave
Descriptive xtdescribe – general patterns of panel data. Use it to produce descriptive
statistics patterns with panel information. Among other things, it provides information
on the amount of waves individuals have been followed. You can use this
information to discuss issues such as balance and attrition in the sample.
In this case we have an unbalanced dataset.
n = number of units, across time periods we have 16647 unique units. This
does NOT mean that we have each person in every wave.
T = max waves observed, 12
Distribution of T_i:
25% implies that for 75% of the sample we observed them for more
than 2 waves
95% implies that for 5% of the sample we observed them for 7 or
more waves
We see a lot of follow up for a long period of time.
Patterns:
1848 people were observed from wave 10 to 12. Meaning I observed
11% of the sample in the last 3 waves (wave 10, 11 and 12).
1302 people only observed in first wave not in other waves
xtsum – descriptive statistics on panel data set. Use this command to produce
summary statistics with panel information. Among other things, it produces
the within and between standard deviations
of the variables. Check which variables are time-invariant by checking
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, whether the within variation is zero.
Overall: estimates the average and standard deviation across the 52740
person-wave observations
Between: the variation of our variable across the units of our sample. Easier
said: individuals are systematically different from one another, for example
Virgil lives in Liverpool and Memphis lives in Lyon. calculates the average for a
person and then estimates the average and standard deviation across 16574
people.
Within: how much your value in that point of time varies in regards of
average of unit. How much does the values for that value vary with regards to
its own mean. Easier said: that individuals’ behaviour varies between
observations, for example Virgil moves from Liverpool to Barcelona. Is also
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