In aggregation theory, the admissibility condition for clustering together components to be aggregated is blockwise weak separability, which also is the condition needed to separate out sectors of the economy. Although weak separability is thereby of central importance in aggregation and index number theory and in econometrics, prior attempts to produce statistical tests of weak separability have performed poorly in Monte Carlo studies. This paper deals with seminonparametric tests for weak separability. It introduces both a necessary and su¢ cient test, and a fully stochastic procedure allowing to take into account measurement error. Simulations show that the test performs well, even for large measurement errors.

The Generalized Axiom of Revealed Preference (GARP) can be violated because of random measurement errors in the observed quantity data. We study two tests proposed by Varian (1985) and de Peretti (2004), which test GARP within an explicit stochastic framework. Both tests compute adjusted quantity data that are compliant with GARP. We compare and contrast the two tests in theoretical terms and in an empirical application. The empirical application is based on testing a large group of monetary assets for the United States over multiple sample periods spanning 1960-1992. We found that both tests provided reasonable results and were largely consistent with each other

This paper introduces a general procedure that tests the significance of the departures from utility maximization, departures defined as violations of the general axiom of revealed preference (GARP). This general procedure is based on (i) an adjustment procedure that computes the minimal perturbation in order to satisfy GARP by using the information content in the transitive closure matrix and (ii) a test procedure that checks the significance of the necessary adjustment. This procedure can be easily implemented and programmed, and we run Monte Carlo simulations to show that it is quite powerful

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The mere complexity of scenarios which could lead tothe onset of financial market instability seems to demand new tools, in particular concerning the role of human decision-making during crises. Here we present agent-based models that could provide new insights into the wayperiods of market turmoil unfold. We illustrate the method through a well-controlled setup in a series of experiments. We are thereby able to:i) validate the impact of model parameters and test their relevance by predicting the average outcome of an experiment; andii) consider each individual experiment and predict outcomes through a scenario analysis. These illustrations should show the appeal of the method in applications to real market situations.