Recent developments in econometrics that are relevant to the task of estimating costs in higher education are reviewed. The relative effectiveness of alternative statistical procedures for estimating costs are also tested. Statistical cost estimation involves three basic parts: a model, a data set, and an estimation procedure. Actual data are used to assess whether the ridge techniques provide a viable alternative to the more familiar ordinary least squares approach within the collinear environment characteristics of translog models. The translog model that is used for the study generates marginal cost estimates for full-time and part-time students at two-year colleges. In every comparison conducted for the study, the ridge procedure was superior to the ordinary least squares approach. Of importance were ridge improvements in the precision and stability of estimated coefficients, since marginal cost estimates were a function of a set of coefficients. In addition, the ridge regression provided a means for data and model exploration. Comparing ordinary least squares and ridge estimates, and especially by examining ridge traces and variance inflation factors, can also promote understanding of the effects of multicollinearity (i.e., highly correlated explanatory variables) in a given situation. Algorithms and graphs are included. A five-page list of references concludes the document. (SW)