Slutsky matrix norms: The size, classification, and comparative statics of bounded rationality

Aguiar, Victor H.; Serrano, Roberto

doi:10.1016/j.jet.2017.08.007

Cited by 30 publications

(32 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The SMN (Slutsky matrix norm) approach, developed in Aguiar and Serrano (2017), provides a natural way to do this since the SMN can be decomposed additively in terms of the intensity of violations of the Slutsky regularity conditions. The SMN measures the departures from rationality by computing a norm of the smallest perturbing error matrix function E such that S − E is a rational Slutsky matrix.…”

Section: Slutsky Matrix Norms For Finite Data: Classifying Consumer Bmentioning

confidence: 99%

“…In a recent contribution, Aguiar and Serrano (2017) propose an index of violations of rationality based on the Slutsky matrix. Their Slutsky matrix norm (SMN) approach helps to answer the question of how far is a given behavior (demand function) from rationality, using the entire observed Slutsky matrix function distance to its closest rational Slutsky matrix function.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we demonstrate how the approach in Aguiar and Serrano (2017) can be adapted to finite data sets. In doing so, we attempt to provide a complementary approach to the RP methodology, stemming from the tradition of Antonelli (1951), Slutsky (1915), and Hurwicz and Uzawa (1971) by using the Slutsky matrix to test the empirical implications of consumer behavior in a finite data set environment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classifying bounded rationality in limited data sets: a Slutsky matrix approach

Aguiar

Serrano

2018

SERIEs

Self Cite

View full text Add to dashboard Cite

Given any observed finite sequence of prices, wealth, and demand choices, we propose a way to measure and classify the departures from rationality in a systematic fashion, by connecting violations of the underlying Slutsky matrix properties to the length of revealed demand cycles. The approach complements our previous study (Aguiar and Serrano in J Econ Theory 172: 2017), which is based on the entire demand function. The methodology can be easily applied in experimental demand data sets.

show abstract

Section: Slutsky Matrix Norms For Finite Data: Classifying Consumer Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classifying bounded rationality in limited data sets: a Slutsky matrix approach

Aguiar

Serrano

2018

SERIEs

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach is analogous to that of Aguiar and Serrano (2015) who study departures of a demand function from rationality. Despite the similarity of our respective approaches, in this paper, we are tackling a completely new question in a different economic environment.…”

Section: A Decomposition Of the Distance To The Shapley Pay Scheme Fomentioning

confidence: 99%

“…We also provide a way of quantifying the violations of pay fairness and identifying their different sources (according to the axioms that characterize the Shapley pay scheme and according to an observer's tastes or priority order over these axioms). The decomposition of a goodness-of-fit measure into components that correspond to the violations of axioms was first explored in Aguiar and Serrano (2015) in the context of consumer theory. We study a completely different economic environment.…”

mentioning

confidence: 99%

Measuring and Decomposing the Distance to the Shapley Wage Function with Limited Data

2016

View full text Add to dashboard Cite

We study the Shapley wage function, a wage scheme in which a worker's pay depends both on the number of hours worked and on the output of the firm. We then provide a way to measure the distance of an arbitrary wage scheme to this function in limited datasets. In particular, for a fixed technology and a given supply of labor, this distance is additively decomposable into violations of the classical axioms of efficiency, equal treatment of identical workers, and marginality. The findings have testable implications for the different ways in which popular wage schemes violate basic properties of distributive justice in market organizations. Applications to the linear contract and to other well-known compensation schemes are shown.JEL: C71, C78, D20, D30, J30

show abstract

Computing revealed preference goodness-of-fit measures with integer programming

Demuynck

Rehbeck

2023

Econ Theory

View full text Add to dashboard Cite

This paper develops mixed integer linear programming (MILP) formulations to compute various revealed preference goodness-of-fit measures. We provide MILP formulations to compute the Houtman-Maks index, the average Varian index, and the minimum cost index when there are linear budgets. Next, we provide MILPs to compute minimal "measurement error" in expenditures, prices, and quantities. Finally, we show that our method is also applicable in settings beyond the classical consumer setting. As a proof of concept, we compute various goodness-of-fit measures for experimental choice data sets from the literature. The maximal computation time is less than 3 seconds for all measures examined on these datasets.

show abstract

Slutsky matrix norms: The size, classification, and comparative statics of bounded rationality

Cited by 30 publications

References 38 publications

Classifying bounded rationality in limited data sets: a Slutsky matrix approach

Classifying bounded rationality in limited data sets: a Slutsky matrix approach

Measuring and Decomposing the Distance to the Shapley Wage Function with Limited Data

Computing revealed preference goodness-of-fit measures with integer programming

Contact Info

Product

Resources

About