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Frequentist Shrinkage under Inequality Constraints

Edvard Bakhitov

arXiv 28 Jan 2020 · Econometrics

arXiv:2001.10586 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper shows how to shrink extremum estimators towards inequality constraints motivated by economic theory. We propose an Inequality Constrained Shrinkage Estimator (ICSE) which takes the form of a weighted average between the unconstrained and inequality constrained estimators with the data dependent weight. The weight drives both the direction and degree of shrinkage. We use a local asymptotic framework to derive the asymptotic distribution and risk of the ICSE. We provide conditions under which the asymptotic risk of the ICSE is strictly less than that of the unrestricted extremum estimator. The degree of shrinkage cannot be consistently estimated under the local asymptotic framework. To address this issue, we propose a feasible plug-in estimator and investigate its finite sample behavior. We also apply our framework to gasoline demand estimation under the Slutsky restriction.

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38
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Hansen, Bruce E (2016) Efficient shrinkage in parametric models0.9098575%
2Andrews, Donald WK (1999) Estimation when a parameter is on a boundary0.8746367%
3Fessler, Pirmin, Kasy, Maximilian (2019) How to use economic theory to improve estimators: Shrinking toward theoretical restrictions0.8746367%
4Hjort, Nils Lid, Claeskens, Gerda (2003) Frequentist model average estimators0.73732100%
5James, William, Stein, Charles M (1961) Estimation with quadratic loss0.73732100%
6Chernoff, Herman (1954) On the distribution of the likelihood ratio0.64422100%
7Feder, Paul I (1968) On the distribution of the log likelihood ratio test statistic when the true parameter is near the boundaries of the hypothesis…0.64422100%
8Liu, Chu-An (2015) Distribution theory of the least squares averaging estimator0.64422100%
9Pollard, David (1985) New ways to prove central limit theorems0.64422100%
10Blundell, Richard, Horowitz, Joel L, Parey, Matthias (2012) Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation0.58531100%

Showing the top 10 of 38 scored citations.