Abstract
AbstractThe chapter relates to, and extends parts of, Chapters 9 and 10. In focus are observation (selection) rules and systematically unbalanced panel data. The unbalance may follow from the sampling process, which often involves endogenous variables, violates ‘classical’ assumptions in regression analysis and makes the observations distorted by the data generating process. Core concepts are censoring and truncation. Truncated normal and binormal distributions therefore are essential. A specific question addressed is which kind of biases arise when the observation rules are neglected in estimation. Estimation, by OLS and ML is considered, partly with examples.
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