Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Christensen, Timothy"'
The leading strategy for analyzing unstructured data uses two steps. First, latent variables of economic interest are estimated with an upstream information retrieval model. Second, the estimates are treated as "data" in a downstream econometric mode
Externí odkaz:
http://arxiv.org/abs/2402.15585
We consider the problem of learning personalized treatment policies that are externally valid or generalizable: they perform well in other target populations besides the experimental (or training) population from which data are sampled. We first show
Externí odkaz:
http://arxiv.org/abs/2205.05561
We derive optimal statistical decision rules for discrete choice problems when payoffs depend on a partially-identified parameter $\theta$ and the decision maker can use a point-identified parameter $P$ to deduce restrictions on $\theta$. Leading exa
Externí odkaz:
http://arxiv.org/abs/2204.11748
We introduce two data-driven procedures for optimal estimation and inference in nonparametric models using instrumental variables. The first is a data-driven choice of sieve dimension for a popular class of sieve two-stage least squares estimators. W
Externí odkaz:
http://arxiv.org/abs/2107.11869
We use a decision-theoretic framework to study the problem of forecasting discrete outcomes when the forecaster is unable to discriminate among a set of plausible forecast distributions because of partial identification or concerns about model misspe
Externí odkaz:
http://arxiv.org/abs/2011.03153
Autor:
Christensen, Timothy M.
Publikováno v:
Journal of Economic Theory 200 (2022) 105413
This paper derives primitive, easily verifiable sufficient conditions for existence and uniqueness of (stochastic) recursive utilities for several important classes of preferences. In order to accommodate models commonly used in practice, we allow bo
Externí odkaz:
http://arxiv.org/abs/2008.00963
Publikováno v:
Econometrica (2023) Vol. 91(1), 263-298
We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variab
Externí odkaz:
http://arxiv.org/abs/1904.00989
Autor:
Christensen, Timothy M.
This paper studies identification and estimation of a class of dynamic models in which the decision maker (DM) is uncertain about the data-generating process. The DM surrounds a benchmark model that he or she fears is misspecified by a set of models.
Externí odkaz:
http://arxiv.org/abs/1812.11246
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