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pro vyhledávání: '"Zhao, Shunan"'
In this paper, we consider the problem of estimating parameters in a linear regression model. We propose a sequential learning procedure to determine the sample size for achieving a given small estimation risk, under the widely used Gauss-Markov setu
Externí odkaz:
http://arxiv.org/abs/2311.02273
Motivated by the long-standing interest in understanding the role of location for firm performance, this paper provides a semiparametric methodology to accommodate locational heterogeneity in production analysis. Our approach is novel in that we expl
Externí odkaz:
http://arxiv.org/abs/2302.13430
There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This paper extends Gandhi et al.'s (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm p
Externí odkaz:
http://arxiv.org/abs/2302.13429
Autor:
Malikov, Emir, Zhao, Shunan
We develop a novel methodology for the proxy variable identification of firm productivity in the presence of productivity-modifying learning and spillovers which facilitates a unified "internally consistent" analysis of the spillover effects between
Externí odkaz:
http://arxiv.org/abs/2302.14602
Publikováno v:
In International Journal of Production Economics August 2024 274
Autor:
Song, Ge, Zhao, Shunan, Wang, Jiaqi, Zhao, Kai, Zhao, Jing, Liang, He, Liu, Ruiping, Li, Yu-You, Hu, Chengzhi, Qu, Jiuhui
Publikováno v:
In Water Research 1 February 2024 249
Publikováno v:
In Applied Thermal Engineering 5 January 2024 236 Part C
Publikováno v:
In Carbohydrate Polymers 1 September 2023 315
Akademický článek
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Publikováno v:
In Science of the Total Environment 15 June 2023 877