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- W2324897957 abstract "Use of panel data regression methods has become increasingly popular as the availability of longitudinal data sets has grown. Panel data contain repeated time series observations (T) for a large number (N) of cross sectional units (e.g., individuals, households, or firms). An important advantage of using such data is that they allow researchers to control for unobservable heterogeneity, that is, systematic differences across cross sectional units. Regressions using aggregated time series and pure cross section data are likely to be contaminated by these effects, and statistical inferences obtained by ignoring these effects could be seriously biased. When panel data are available, error components models can be used to control for these individual differences. Such a model typically assumes that the stochastic error term has two components: a time invariant individual effect which captures the unobservable individual heterogeneity and the usual random noise term. Some explanatory variables (e.g., years of schooling in the earnings equation) are likely to be correlated with the individual effects (e.g., unobservable talent or IQ). A simple treatment to this problem is the within estimator which is equivalent to least squares after transformation of the data to deviations from means." @default.
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- W2324897957 date "1999-04-13" @default.
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- W2324897957 title "Estimation of Linear Panel Data Models Using GMM" @default.
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- W2324897957 doi "https://doi.org/10.1017/cbo9780511625848.009" @default.
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