Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Franguridi, Grigory"'
Autor:
Franguridi, Grigory, Kosenkova, Lidia
It has long been established that, if a panel dataset suffers from attrition, auxiliary (refreshment) sampling restores full identification under additional assumptions that still allow for nontrivial attrition mechanisms. Such identification results
Externí odkaz:
http://arxiv.org/abs/2410.11263
Autor:
Franguridi, Grigory
For the kernel estimator of the quantile density function (the derivative of the quantile function), I show how to perform the boundary bias correction, establish the rate of strong uniform consistency of the bias-corrected estimator, and construct t
Externí odkaz:
http://arxiv.org/abs/2207.09004
Autor:
Franguridi, Grigory
I suggest an enhancement of the procedure of Chiong, Hsieh, and Shum (2017) for calculating bounds on counterfactual demand in semiparametric discrete choice models. Their algorithm relies on a system of inequalities indexed by cycles of a large numb
Externí odkaz:
http://arxiv.org/abs/2112.04637
Autor:
Andreyanov, Pasha, Franguridi, Grigory
In a classical model of the first-price sealed-bid auction with independent private values, we develop nonparametric estimators for several policy-relevant targets, such as the bidder's surplus and auctioneer's revenue under counterfactual reserve pr
Externí odkaz:
http://arxiv.org/abs/2106.13856
We study the bias of classical quantile regression and instrumental variable quantile regression estimators. While being asymptotically first-order unbiased, these estimators can have non-negligible second-order biases. We derive a higher-order stoch
Externí odkaz:
http://arxiv.org/abs/2011.03073
For an $N \times T$ random matrix $X(\beta)$ with weakly dependent uniformly sub-Gaussian entries $x_{it}(\beta)$ that may depend on a possibly infinite-dimensional parameter $\beta\in \mathbf{B}$, we obtain a uniform bound on its operator norm of th
Externí odkaz:
http://arxiv.org/abs/1905.01096
Akademický článek
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We study the small sample properties of conditional quantile estimators such as classical and IV quantile regression. First, we propose a higher-order analytical framework for comparing competing estimators in small samples and assessing the accuracy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1687::f35c998c39eec886d3fadf4db76c0260
https://hdl.handle.net/10419/235416
https://hdl.handle.net/10419/235416
Akademický článek
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