Availability of data on a large number of individuals, but on each individual only over a very short period of time, has become increasingly common in a number of different fields in economics. Very often we would like to use such data to study behavioral relationships that are dynamic in character, i.e., that contain a distributed lag or other form of autogressive relationship. Since only a few observations are available over time, but a great many observations are available for different individuals at a point in time, it is exceptionally important to make the most efficient use of the data across individuals to estimate that part of the behavioral relationship containing variables that differ substantially from one individual to another, in order that the lesser amount of information over time can be used to best advantage in the estimation of the dynamic part of the relationship studied. As it turns out, the problem is far from simple: obvious devices such as the pooling of all observations and estimation by ordinary least squares, or the introduction of dummy variables for individuals, produce estimates having serious small sample bias. In earlier papers, the author and others have formulated a simple variance components model for the disturbance term in a relationship to be estimated from cross section data over time. This paper presents a series of Monte Carlo studies designed to explore the small sample properties of various types of estimates within this context. Not only is the bias of the obvious methods of estimation mentioned above confirmed, but certain serious deficiencies of the maximum likelihood approach which had been suspected earlier are also confirmed. A two-round estimation procedure is proposed which appears to work well for a wide variety of parameter values.