1.3 Structure of the data

• There are three basic data structures:

1. Cross-sectional data
2. Time-series data
3. Panel data

• Cross-sectional data refers to the multiple observation units during one point in time ($i=1,2,...,n$), e.g. income of $100$ households in one yer, say $2004$

• Time-series refers to a single unit of observation across time ($t=1,2,...,T$), e.g. income of a single household for $5$ years, say $2001$, $2002$, $2003$, $2004$, and $2005$

• Panel data include observations for the same set of cross-sectional units $i$ at different points in time $t$, e.g. income of the same $100$ households for $5$ years

• Collected data should be always organized in matrix form (observations are presented by rows and variables by columns)

• When collecting the data some issues can emerge:

1. Missing values (abbreviation NA or n/a represents “not applicable”)
2. Measurement errors (collected data may not always present true values)
3. Outliers (extreme values)

Example 1.5 Two tables are given below. Answer the following questions:

1. Which table presents cross-sectionla data, and which one presents time-series data?
2. In which year cross-sectional data are observed?
3. For which audit firm time-series data are observed?
4. Indicate whether quantitative data are continuous or discrete.

TABLE 1.1: Audit firms with respect to the number of employees and net margin (%)

Audit firm	Employees	Net margin
Deloitte	334800	23.4
PwC	284000	15.6
Erste & Young	398965	20.7
KPMG	227000	13.8

TABLE 1.2: Yearly observations with respect to the number of employees and net margin (%)

Year	Employees	Net margin
2016	204790	17.3
2017	223600	27.8
2018	287590	16.5
2019	334800	23.4