the basics and fundamentals of panel data - face.ufg.br filewhat is panel data and how the data is...

The Basics and Fundamentals of Panel Data

British Academy Grant n. NMGR2\100034

Prof: Marcos Severo

Personal presentation

Marcos Severo –The basics and fundamentals of panel data

https://www.face.ufg.br/admkt/

About me

Why are we here?

=

REGRESSION!

Y X

Mostly empirical

Models are

Derived from Regression


What is panel data and how the data is organized?

• Panel data or longitudinal data are repeated measurements at different points in time on the same individual unit, such as person, firm, state, or country

• Regression-based models can then capture both variation over units, similar to regression on cross-section data, and variation over time

• Panel-data methods are more complicated than cross-section-data methods: the standard erros of panel-data estimators need to be adjusted because eachadditional time period of data is not independent of previous periods

• There are many types of panel data and goals of panel-data analysis, leadingto different models and estimators for panel data

Cameron & Trivedi (2009)



• The repeated nature of the observations enables researchers to understand thecausal relationship and how the variables they are analyzing change over time

• In other words, panel data allows us to study dynamic relationships

• The theoretical econometric framework normally used to analyze panel data assumes we have a large number of units and a small number of time observations per unit. This framework excludes theoretical arguments fromtime series that assume we have an arbitrarily large number of time periods

Stata Corp (2015)



• With cross-sectional data, we can test only hypotheses about the variationbetween individuals, for example, differences in wages by educationalattainment

• In a panel data example/case, we have repeated measures of individuals fromdifferent time periods

• For example, consider the following example of dataset:

Stata Corp (2015)



Stata Corp (2015)

• In this case we have repeated measures ofindividuals, from 1977 to 1980.

• We can, for instance, study the income(wage) dynamics, or change (“thedifference”) in income, for the individuals in the sample

• We can also study “time-invariant” effects, such as the maximum level of educationattained by an individual, or unobservedindividual ability (if we arbitrarily “fix” something for each individual)


Basic panel-data estimation concepts

• The first important characteristic of panel data: repeated observations

• The other defining characteristic of panel data: unobserved time-invariantindividual heterogeneity

• For any given labor market dataset for example, we can think that individualshave an inherent propensity to exert effort of a high level of intelligence and thatboth of these characteristics (effort and high level of intelligence) remainconstant over time and affect individual wages

• It is important to note that individual heterogeneity is not a characteristic ofmodels for cross-sectional and univariate time-series data. In these cases, theunobserved random component is assumed to be common

Stata Corp (2015) Marcos Severo –The basics and fundamentals of panel data

Basic panel-data estimation concepts

• For the cross-sectional case, it is not possible to conceive the unobservedcomponent as changing or remaining constant over time

• The basic panel data characteristics are detailed in 6. There are TWO unobservedrandom components, αi and εit. αi is the individual heterogeneity, while εit isanother random unobserved component, which changes over time and can beunderstood as an extension of the random unobserved component in a cross-section.

Stata Corp (2015) Marcos Severo –The basics and fundamentals of panel data

What happens if I not consider the individual unobserved component?

Stata Corp (2015)

• What is the problem with this graph?


• A basic assumption of a regression model states that the variability of theunobserved component for any individual i and any value of the regressors isthe same, a constant σ

• In the wage example, we see this would mean that the variation of wagesaround their conditional mean is the same regardless of what the maximumlevel of education attained by the individual (grade) was

• If we take a look at the graph, we see this would mean that for any of thevalues of grade, the wages have the same variation around the blue line, theirconditional mean

• This is not validated by the graph, as wage variability seems to be differentfor individuals who have completed more than 10 years of educationcompared with those that did not

Stata Corp (2015)




Stata Corp (2015)

• What is the problem with this graph?

Positive deviation away from

the conditional mean (blue line)

after an individual completes more

than 10 years of education



• The problem in the real-word: overestimation or underestimation


• An advantage: we control for unobservables

• In most applications of least squares regression is that there aren´t anyomitted variables which are correlated with the included explanatoryvariables

• This is a powerful assumption, as omitted variables cause least squares to bebiased

• In cross section regressions, if you don´t observe a variable, you don´t havemuch choice but to omit it from the regression

Cannot correlate!

(Endogeneity)

Startz (1994)Takeaways: Detailing some advantages of using panels



• If we are estimating a model and we have something unobservable that varies across one dimension of the panel but not across the other, we can fix this effectusing a specific estimator

• If equation above is estimating countries over years, for example, theunobservable variable z varies across countries, but not time (is somethingthat does not change over time) (zi)

Something that varies across one dimension of the

panel (i), but not across the other (t)

Unobservables




• The trick of basic panel data aplplications in this case is to think of there beinga unique constant for each country

• If we call this constant αi (as varies across countries, but not time) and use thefollowing definition for the constant term αi = α + γzi we can rewriteequations!

Something that varies across one dimension of the

panel (i), but not across the other (t)




This:

Turns into this:

We have now a separate intercept for each individual, assembling

the unobservables related to one dimension (i),



What this framework has provided for marketing research?

Study Primary results

Baghestani (1991) Advertising has a long-run impact on sales if both variablesare (a) evolving and (b) in long-run equilibrium(cointegrated)

Chowdhury (1994) No long run equilibrium (cointegration) relationship isfound between UK aggregate advertising spending and avariety of macro-economic variables

Dekimpe et al. (1997) Persistence measures quantify marketing’s long-runeffectiveness. Image-oriented and price-oriented messageshave a differential short- along long-run effect

Jung and Seldon (1995) Aggregate US advertising spending is in long-runequilibrium with aggregate personal consumptionexpenditures

Srinivasan et al. (2000) Temporary, gradual and structural price changes have adifferent impact on market shares

Challenges of panel data models

1. Dealing with recent data richness- Data aggregation- Level of parameterization (“more data more variables”)

2. Broadening the scope of techniques to different managerial problems

3. Allowing for asymmetries in market response (as we are able to analyze theinfluence on different, and heterogeneous, individual observations)

4. Capturing changing dynamics

Pauwels et al.(2004)


THANK YOU! (OBRIGADO)


QUESTIONS?

the basics and fundamentals of panel data - face.ufg.br filewhat is panel data and how the data is...

Documents