Pooling Time-Series of Cross-Section Data

Abstract

In this chapter, we will consider pooling time-series of cross-sections. This may be a panel of households or firms or simply\rcountries or states followed over time. Two well known examples of panel data in the U.S. are the Panel Study of Income Dynamics\r(PSID) and the National Longitudinal Survey (NLS). The PSID began in 1968 with 4802 families, including an over-sampling of\rpoor households. Annual interviews were conducted and socioeconomic characteristics of each of the families and of roughly\r31000 individuals who have been in these or derivative families were recorded. The list of variables collected is over 5000.\rThe NLS, followed five distinct segments of the labor force. The original samples include 5020 older men, 5225 young men,\r5083 mature women, 5159 young women and 12686 youths. There was an over-sampling of blackds, hispanics, poor whites and military\rin the youths survey. The list of variables collected runs into the thousands. An inventory of national studies using panel\rdata is given at http://www.isr.umich.edu/src/psid/panelstudies.html. Pooling this data gives a richer source of variation\rwhich allows for more efficient estimation of the parameters. With additional, more informative data, one can get more reliable\restimates and test more sophisticated behavioral models with less restrictive assumptions. Another advantage of panel data\rsets are their ability to control for individual heterogeneity. Not controlling for these unobserved individual specific effects\rleads to bias in the resulting estimates. Panel data sets are also better able to identify and estimate effects that are simply\rnot detectable in pure cross-sections or pure time-series data. In particular, panel data sets are better able to study complex\rissues of dynamic behavior. For example, with a cross-section data set one can estimate the rate of unemployment at a particular\rpoint in time