Estimation of a Linear Model under Microaggregation by Individual Ranking

Microaggregation by individual ranking is one of the most commonly
applied disclosure control techniques for continuous microdata. The
paper studies the effect of microaggregation by individual ranking
on the least squares estimation of a multiple linear regression
model in continuous variables. It is shown that the naive parameter
estimates are asymptotically unbiased. Moreover, the naive least
squares estimates asymptotically have the same variances as the
least squares estimates based on the original (non-aggregated)
data. Thus, asymptotically, microaggregation by individual ranking
does not induce any efficiency loss on the least squares estimation
of a multiple linear regression model.