We provide two methods to compute the largest subset of a set of observations that is consistent with the Generalised Axiom of Revealed Preference. The algorithm provided by Houtman and Maks (1985) is not computationally feasible for larger data sets, while our methods are not limited in that respect. The first method is an application of Gross and Kaiser’s (1996) approximate algorithm and is only applicable for two-dimensional data sets, but it is very fast and easy to implement. The second method is a mixedinteger linear programming approach that is slightly more involved but still fast and not limited by the dimension of the data set.
Consistent Subsets: Computationally Feasible Methods to Compute the Houtman–Maks–Index