  A common problem faced by statistical institutes is that data may be missing
from collected data sets. The typical way to overcome this problem is to impute
the missing data. The problem of imputing missing data is complicated by the
fact that statistical data often have to satisfy certain edit rules and that
values of variables across units sometimes have to sum up to known totals. For
numerical data, edit rules are most often formulated as linear restrictions on
the variables. For example, for data on enterprises edit rules could be that
the profit and costs of an enterprise should sum up to its turnover and that
the turnover should be at least zero. The totals of some variables across units
may already be known from administrative data (e.g., turnover from a tax
register) or estimated from other sources. Standard imputation methods for
numerical data as described in the literature generally do not take such edit
rules and totals into account. In this article we describe algorithms for
imputing missing numerical data that take edit restrictions into account and
ensure that sums are calibrated to known totals. These algorithms are based on
a sequential regression approach that uses regression predictions to impute the
variables one by one. To assess the performance of the imputation methods, a
simulation study is carried out as well as an evaluation study based on a real
data set.
