G2TT
来源类型Discussion paper
规范类型论文
来源IDDP14402
DP14402 Partial Identification and Inference for Dynamic Models and Counterfactuals
Myrto Kalouptsidi; Yuichi Kitamura; Eduardo Souza-Rodrigues; Lucas Lima
发表日期2020-02-11
出版年2020
语种英语
摘要We provide a general framework for investigating partial identification of structural dynamic discrete choice models and their counterfactuals, along with uniformly valid inference procedures. In doing so, we derive sharp bounds for the model parameters, counterfactual behavior, and low-dimensional outcomes of interest, such as the average welfare effects of hypothetical policy interventions. We char- acterize the properties of the sets analytically and show that when the target outcome of interest is a scalar, its identified set is an interval whose endpoints can be calculated by solving well-behaved constrained optimization problems via standard algorithms. We obtain a uniformly valid inference pro- cedure by an appropriate application of subsampling. To illustrate the performance and computational feasibility of the method, we consider both a Monte Carlo study of firm entry/exit, and an empirical model of export decisions applied to plant-level data from Colombian manufacturing industries. In these applications, we demonstrate how the identified sets shrink as we incorporate alternative model restrictions, providing intuition regarding the source and strength of identification.
主题Industrial Organization ; International Trade and Regional Economics ; Labour Economics
关键词Dynamic discrete choice Counterfactual Partial identification Subsampling Uni- form inference Structural model
URLhttps://cepr.org/publications/dp14402
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/543296
推荐引用方式
GB/T 7714
Myrto Kalouptsidi,Yuichi Kitamura,Eduardo Souza-Rodrigues,et al. DP14402 Partial Identification and Inference for Dynamic Models and Counterfactuals. 2020.
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