under identification

简明释义

证实未定

英英释义

例句

1.The researchers found that under identification 身份识别不足 of variables could skew their findings.

研究人员发现，变量的<品>身份识别不足可能会扭曲他们的发现。

2.Teachers noted under identification 身份识别不足 of students' learning needs during assessments.

教师们注意到在评估过程中学生学习需求的<品>身份识别不足。

3.In the context of data analysis, under identification 身份识别不足 can lead to inaccurate results.

在数据分析的背景下，<品>身份识别不足可能导致不准确的结果。

4.The police department is working to address under identification 身份识别不足 in their crime reports.

警方正在努力解决他们犯罪报告中的<品>身份识别不足问题。

5.The study revealed that many participants were facing issues with under identification 身份识别不足 in the survey.

研究表明，许多参与者在调查中面临着<品>身份识别不足的问题。

作文

In the realm of economics and social sciences, the concept of under identification refers to a situation where there are not enough independent equations to solve for all the parameters of interest. This often occurs in statistical models where the number of observed variables is less than the number of parameters that need to be estimated. Understanding this concept is crucial for researchers and policymakers alike, as it can significantly affect the validity of their findings and decisions. To illustrate, consider a simple economic model that attempts to explain consumer behavior based on income, price levels, and preferences. If we only have information about income and prices but lack sufficient data on preferences, the model may suffer from under identification because we cannot accurately estimate the impact of preferences on consumer choices. This limitation can lead to misleading conclusions, which may result in ineffective policies aimed at influencing consumer behavior.Furthermore, under identification can also emerge in more complex models, such as those used in econometrics to assess the effects of policy interventions. For example, if a government implements a new tax policy but does not have comprehensive data on how consumers will respond to this change, the analysis may be compromised. Researchers might find themselves in a position where they cannot determine the causal relationships due to the insufficient data available, leading to an under identification scenario.The implications of under identification extend beyond academic research. In the business world, companies rely on accurate models to forecast demand, set prices, and strategize effectively. If their models are under identified, businesses may misallocate resources or misjudge market trends, ultimately resulting in financial losses. To address under identification, researchers can employ various strategies. One approach is to gather more data, ensuring that all relevant variables are included in the analysis. This could involve conducting surveys, utilizing administrative data, or leveraging new technologies to collect information. Another strategy is to use advanced statistical techniques that can help identify relationships even in the presence of under identification. For instance, methods like instrumental variable estimation can provide alternative ways to infer causality when direct measurement is not possible.In conclusion, recognizing and addressing under identification is essential for producing reliable and valid research outcomes. Whether in economics, social sciences, or business analytics, the consequences of neglecting this issue can be far-reaching. By improving data collection and employing sophisticated analytical techniques, researchers and practitioners can mitigate the risks associated with under identification and enhance the robustness of their findings. Ultimately, a deeper understanding of this concept will lead to better-informed decisions that benefit society as a whole.

在经济学和社会科学领域，under identification这一概念指的是一种情况，即没有足够的独立方程来解决所有感兴趣的参数。这种情况通常发生在统计模型中，当观察变量的数量少于需要估计的参数数量时。理解这一概念对研究人员和政策制定者至关重要，因为它可能会显著影响他们发现和决策的有效性。例如，考虑一个简单的经济模型，它试图根据收入、价格水平和偏好来解释消费者行为。如果我们只拥有关于收入和价格的信息，但缺乏足够的偏好数据，那么该模型可能会遭遇under identification的问题，因为我们无法准确估计偏对消费者选择的影响。这种限制可能导致误导性的结论，最终导致旨在影响消费者行为的无效政策。此外，under identification也可能出现在更复杂的模型中，例如那些用于计量经济学以评估政策干预效果的模型。例如，如果政府实施了一项新的税收政策，但没有全面的数据来了解消费者将如何响应这一变化，那么分析可能会受到影响。研究人员可能发现自己处于一种无法确定因果关系的境地，因为可用的数据不足，从而导致under identification的情形。under identification的影响超出了学术研究。在商业世界中，公司依赖于准确的模型来预测需求、设定价格并有效制定战略。如果他们的模型是under identified，企业可能会错误分配资源或误判市场趋势，最终导致财务损失。为了解决under identification问题，研究人员可以采用各种策略。一种方法是收集更多数据，确保分析中包含所有相关变量。这可能涉及进行调查、利用行政数据或利用新技术收集信息。另一种策略是使用先进的统计技术，即使在under identification的情况下也能帮助识别关系。例如，工具变量估计等方法可以在无法直接测量的情况下提供推断因果关系的替代方法。总之，认识到并解决under identification对于产生可靠和有效的研究结果至关重要。无论是在经济学、社会科学还是商业分析中，忽视这一问题的后果都可能深远。通过改善数据收集和采用复杂的分析技术，研究人员和从业者可以减轻与under identification相关的风险，并增强其发现的稳健性。最终，对这一概念的深入理解将导致更好的决策，这将使整个社会受益。