applied straightforwardly to different types of data sets, it is essential to examine the
special features of some sets. In the next sections we describe the most important data
structures encountered in applied econometric work.
1. Cross-sectional data
A cross-sectional data set consists of a sample of individuals, households, firms,
countries, regions, cities or any other type of units at a specific point in time. In some
cases, the data across all units do not correspond to exactly the Sa!Jle time period.
Consider a survey that collects data from questionnaires applied to different families
that were surveyed during different days within a month. In this case, we can ignore
the minor time differences in collecting the data and the data collected will still be
viewed as a cross-sectional data set.
In econometrics, cross-sectional variables are usually denoted by the subscript i, with
i taking values from 1, 2, 3, ... , N; for N number of cross-sections. So, if for example Y
denotes the income data we have collected for N number of individuals, this variable,
in a cross-sectional framework, will be denoted by:
Y; fori= 1, 2, 3, ... , N
Cross-sectional data are widely used in economics and other social sciences. I
economics, the analysis of cross-sectional data is mainly associated with applied
microeconomics. Labour economics, state and local public finance, business
economics, demographic economics and health economics are some of tl;~e most
common fields included within microeconomics. Data on individuals, households,
firms, cities and regions at a given point in time are utilised in these cases in order to
test microeconomics hypotheses and evaluate economic policies.
2. Cross-sectional data
A cross-sectional data set consists of a sample of individuals, households, firms,
countries, regions, cities or any other type of units at a specific point in time. In some
cases, the data across all units do not correspond to exactly the Sa!Jle time period.
Consider a survey that collects data from questionnaires applied to different families
that were surveyed during different days within a month. In this case, we can ignore
the minor time differences in collecting the data and the data collected will still be
viewed as a cross-sectional data set.
In econometrics, cross-sectional variables are usually denoted by the subscript i, with
i taking values from 1, 2, 3, ... , N; for N number of cross-sections. So, if for example Y
denotes the income data we have collected for N number of individuals, this variable,
in a cross-sectional framework, will be denoted by:
Y; fori= 1, 2, 3, ... , N
Cross-sectional data are widely used in economics and other soci~l sciences. Ih
economics, the analysis of cross-sectional data is mainly associated with applied
microeconomics. Labour economics, state and local public finance, business
economics, demographic economics and health economics are some of tl;~e most
common fields included within microeconomics. Data on individuals, households,
firms, cities and regions at a given point in time are utilized in these cases in order to
test microeconomic hypotheses and evaluate economic policies
3 .Time series data
A time series data set consists of observations on one or several variables over time.
So, time series data are arranged in chronological order and can have different time
frequencies, such as biannual, annual, quarterly, monthly, weekly, daily and hourly.
Examples of time series data can include stock prices, gross domestic product (GDP),
money supply, ice-cream sale figures, among many others.
Time series data are denoted with the subscript t. So, for example, if Y denotes the
GDP of a country from 1990 to 2002 then we denote that as:
Yt fort=1,2,3, ... ,T (2.2)
where t = 1 for 1990 and t = T = 13 for 2002.
Because past events can influence future events and lags in behaviour are prevalent
in social sciences, time is a very important dimension in time series data sets.
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