Panel data

In statistics and econometrics, the term panel data refers to multi-dimensional data. Panel data contains observations on multiple phenomena observed over multiple time periods for the same firms or individuals. In biostatistics, the term longitudinal data is often used instead, wherein a subject constitutes a panel.

Time series and cross-sectional data are special cases of panel data that are in one-dimension only.

Example

balanced panel:		unbalanced panel:
$\begin{matrix} \mathrm{person} & \mathrm{year} & \mathrm{income} & \mathrm{age} & \mathrm{sex}\\ 1 & 2003 & 1500 & 27 & 1 \\ 1 & 2004 & 1700 & 28 & 1 \\ 1 & 2005 & 2000 & 29 & 1 \\ 2 & 2003 & 2100 & 41 & 2 \\ 2 & 2004 & 2100 & 42 & 2 \\ 2 & 2005 & 2200 & 43 & 2 \end{matrix}$		$\begin{matrix} \mathrm{person} & \mathrm{year} & \mathrm{income} & \mathrm{age} & \mathrm{sex}\\ 1 & 2003 & 1500 & 27 & 1 \\ 1 & 2004 & 1700 & 28 & 1 \\ 2 & 2003 & 2100 & 41 & 2 \\ 2 & 2004 & 2100 & 42 & 2 \\ 2 & 2005 & 2200 & 43 & 2 \\ 3 & 2004 & 3000 & 35 & 1 \end{matrix}$

In the example above, two data sets with a two-dimensional panel structure are shown. Individual characteristics (income, age, sex. educ) are collected for different persons and different years. In the left data set two persons (1, 2) are observed over three years (2003, 2004, 2005). Because each person is observed every year, the left-hand data set is called a balanced panel, whereas the data set on the right hand is called an unbalanced panel, since Person 1 is not observed in year 2005 and Person 3 is not observed in 2003 or 2005.

Analysis of panel data

A panel has the form

$X_{it}, \; i = 1, \dots, N \; t = 1, \dots, T,$
where i is the individual dimension and t is the time dimension. A general panel data regression model is written as y_it = α + β'X_it + u_it. Different assumptions can be made on the precise structure of this general model. Two important models are the fixed effects model and the random effects model. The fixed effects model is denoted as

y_it = α + β'X_it + u_it,

u_it = μ_i + ν_it.

μ_i are individual-specific, time-invariant effects (for example in a panel of countries this could include geography, climate etc.) and because we assume they are fixed over time, this is called the fixed-effects model. The random effects model assumes in addition that

$\mu_i \sim \text{i.i.d.} N(0, \sigma^2_{\mu})$

and

$\nu_{it} \sim \text{i.i.d.} N(0, \sigma^2_{\nu}),$

that is, the two error components are independent from each other.

Data sets which have a panel design

• German Socio-Economic Panel (SOEP)
• Household, Income and Labour Dynamics in Australia Survey (HILDA)
• British Household Panel Survey (BHPS)
• Survey of Family Income and Employment (SoFIE)
• Survey of Income and Program Participation (SIPP)
• Lifelong Labour Market Database (LLMDB)
• Panel Study of Income Dynamics (PSID)
• Korean Labor and Income Panel Study (KLIPS)
• Chinese Family Panel Studies (CFPS)
• German Family Panel (pairfam)
• National Longitudinal Surveys (NLSY)

Data sets which have a multi-dimensional panel design

Main article: Multidimensional panel data

References

Arellano, Manuel. Panel Data Econometrics, Oxford University Press 2003.

Hsiao, Cheng, 2003. Analysis of Panel Data, Cambridge University Press.

Davies, A. and Lahiri, K., 2000. "Re-examining the Rational Expectations Hypothesis Using Panel Data on Multi-Period Forecasts," Analysis of Panels and Limited Dependent Variable Models, Cambridge University Press.

Davies, A. and Lahiri, K., 1995. "A New Framework for Testing Rationality and Measuring Aggregate Shocks Using Panel Data," Journal of Econometrics 68: 205-227.

Frees, E., 2004. Longitudinal and Panel Data, Cambridge University Press.

See also

• Panel analysis

External links

• PSID
• KLIPS
• pairfam