| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13240 |
| DP13240 Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models | |
| Myrto Kalouptsidi; Eduardo Souza-Rodrigues | |
| 发表日期 | 2018-10-11 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 摘要 | In structural dynamic discrete choice models, the presence of serially correlated unob- served states and state variables that are measured with error may lead to biased parameter estimates and misleading inference. In this paper, we show that instrumental variables can address these issues, as long as measurement problems involve state variables that evolve exogenously from the perspective of individual agents (i.e., market-level states). We define a class of linear instrumental variables estimators that rely on Euler equations expressed in terms of conditional choice probabilities (ECCP estimators). These estimators do not require observing or modeling the agent’s entire information set, nor solving or simulating a dynamic program. As such, they are simple to implement and computationally light. We provide constructive identification arguments to identify the model primitives, and establish the con- sistency and asymptotic normality of the estimator. A Monte Carlo study demonstrates the good finite-sample performance of the ECCP estimator in the context of a dynamic demand model for durable goods. |
| 主题 | Industrial Organization ; Labour Economics ; Public Economics |
| URL | https://cepr.org/publications/dp13240 |
| 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/542048 |
| 推荐引用方式 GB/T 7714 | Myrto Kalouptsidi,Eduardo Souza-Rodrigues. DP13240 Linear IV Regression Estimators for Structural Dynamic Discrete Choice Models. 2018. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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