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Unformatted text preview: Panel data 1 Panel data Cross-sectional data typically collect information on N units (individuals, &rms, countries, etc.) at a given moment in time. Time-series data collect information on a single unit (typically, a country) over many ( T ) time periods. So far, we have concentrated our attention on cross-sectional data and ignored time-series data. Both types of data move along a single dimension: individuals (cross- sectional data) or time (time-series data). Panel data (or longitudinal data) move instead along two (or more) dimen- sions. The most frequent case is one where N units are observed over T time periods. The total number of observations is therefore NT . Three dimensions of data are also possible: for example, we could have information on N multina- tional &rms followed over T time periods and over the C countries where they operate. The total number of observations in this case is NTC . Two-dimension panels do not necessarily extend over units and time periods. We could have two-dimension panels extending over units and sub-units, such as when we have data on siblings within families, or branches within &rms, or counties within states. Panels can be balanced (when the units are followed for an equal number of periods) or unbalanced (when the units are followed for a di/erent number of periods). Panels can be continuous (when there is no refreshing, i.e., no entry of new units -an example is the Panel Study of Income Dynamics) or rotating (when units leave and are replaced by new units -an example is the Consumer Expenditure Survey). 1.1 Advantages of panel data & Panel data allow us to control for unobserved heterogeneity. For example, in the classical Mundlaks example, we can control directly for managerial ability and inputs use, so as to eliminate the omitted variable bias. In general, any unobserved heterogeneity component that remains &xed over time can be handled thus reducing considerably the omitted variable bias problem. & They o/er obvious statistical advantages. They may help us reduce the problem of collinearity among variables, and may give us more precise estimates due to the e ciency gain brought by more data. & They can help us addressing questions of dynamics as discussed in class. 1 1.2 Disadvantages of panel data & Panel data su/er from attrition, i.e., the fact that people who are initially part of the data set fail to be interviewed in subsequent waves. For ex- ample, people may move and the survey may be unable to track them down. The attrition of units would not be problematic if attrition were completely at random. However, this is unlikely to be the case. If we are studying the dynamics of poverty, for example, we may be worried that the people who move (and therefore disappear from the survey) are the poorest, for example due to lack of job opportunities in the local labor market or, to the extreme, because they become homeless. Since the poor disappears, we will have the impression that things are improving simply...
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- Spring '11