Victor H. Aguiar, Nail Kashaev
arXiv 11 Oct 2018 · Econometrics · publishedThe Review of Economic Studies (2020) · 8 citations (OpenAlex)
arXiv:1810.05287 · PDF · DOI · OpenAlex · Extracted main text
A long-standing question about consumer behavior is whether individuals' observed purchase decisions satisfy the revealed preference (RP) axioms of the utility maximization theory (UMT). Researchers using survey or experimental panel data sets on prices and consumption to answer this question face the well-known problem of measurement error. We show that ignoring measurement error in the RP approach may lead to overrejection of the UMT. To solve this problem, we propose a new statistical RP framework for consumption panel data sets that allows for testing the UMT in the presence of measurement error. Our test is applicable to all consumer models that can be characterized by their first-order conditions. Our approach is nonparametric, allows for unrestricted heterogeneity in preferences, and requires only a centering condition on measurement error. We develop two applications that provide new evidence about the UMT. First, we find support in a survey data set for the dynamic and time-consistent UMT in single-individual households, in the presence of nonclassical measurement error in consumption. In the second application, we cannot reject the static UMT in a widely used experimental data set in which measurement error in prices is assumed to be the result of price misperception due to the experimental design. The first finding stands in contrast to the conclusions drawn from the deterministic RP test of Browning (1989). The second finding reverses the conclusions drawn from the deterministic RP test of Afriat (1967) and Varian (1982).
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ahn, D., Choi, S., Gale, D., & Kariv, S (2014) Estimating ambiguity aversion in a portfolio choice experiment | 1.000 | 11 | 3 | 100% |
| 2 | Afriat, S. N (1967) The construction of utility functions from expenditure data | 1.000 | 10 | 5 | 100% |
| 3 | Varian, H. R (1985) Non-parametric analysis of optimizing behavior with measurement error | 1.000 | 8 | 5 | 100% |
| 4 | Varian, H. R (1982) The nonparametric approach to demand analysis | 1.000 | 8 | 3 | 100% |
| 5 | Beatty, T. K. & Crawford, I. A (2011) How demanding is the revealed preference approach to demand? | 1.000 | 5 | 4 | 100% |
| 6 | Blow, L., Browning, M., & Crawford, I (2017) Nonparametric analysis of time-inconsistent preferences | 0.961 | 9 | 4 | 89% |
| 7 | Browning, M (1989) A nonparametric test of the life-cycle rational expections hypothesis | 0.928 | 20 | 7 | 80% |
| 8 | Blundell, R. W., Browning, M., & Crawford, I. A (2003) Nonparametric engel curves and revealed preference | 0.928 | 4 | 3 | 100% |
| 9 | Gillen, B., Snowberg, E., & Yariv, L (2017) Experimenting with measurement error: techniques with applications to the Caltech cohort study | 0.928 | 4 | 3 | 100% |
| 10 | Kurtz-David, V., Persitz, D., Webb, R., & Levy, D. J (2019) The neural computation of inconsistent choice behavior | 0.874 | 9 | 2 | 100% |
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