Estimation: Inference and Model Selection for Neural Network Models in Econometrics

估计：计量经济学中神经网络模型的推理和模型选择

• 批准号: 8806990
• 负责人: Halbert White
• 金额: $ 9.31万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-08-15 至 1991-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8806990&HistoricalAwards=false
• 关键词: Estimation; Inference; Model; Selection; Neural

Cognitive scientists have recently developed a rich and interesting class of nonlinear models inspired by the neural architecture of the brain (neural network models). These networks are capable of learning through interaction with their environment, in a process which can be viewed as a recursive statistical estimation procedure. The promise of these models and associated estimation procedures and the excitement evident across a spectrum of disciplines including psychology, computer science, genetics, linguistics and engineering is founded on the demonstrated success of neural network modeling in solving a diverse range of difficult problems. Especially impressive have been solutions to problems which had previously resisted conventional attempts at solution, as well as relatively quick and reliable solutions to problems which had previously yielded comparable effective solutions grudgingly, and after several man- years of more conventional effort. The objectives of this project are (1) to investigate the applicability of neural network models to the study of economic phenomena and to refine and extend these models in directions suitable to the study of economic phenomena, (2) to refine and extend the learning methods (estimation procedures) used to train the networks so as to obtain parameter estimates which converge quickly and reliably when faced with economic data, and (3) to apply model specification and selection techniques developed by the investigator in previous funded research to neural network models in order to develop techniques for choosing between competing neural network architectures for particular problems. This is an exciting project because no one has ever applied neural network models to economics. These new methods will dramatically reduce the computational time needed to solve complex economic problems. The neural network models will provide a new methodology for studying the way economic agents learn from their environment. Neural networks appear to be particularly well suited to nonlinear economic forecasting, so these new methods could provide us with better predictions of the economic future.

认知科学家最近开发了一类丰富而有趣的非线性模型，灵感来自大脑的神经架构(神经网络模型)。这些网络能够通过与其环境的互动进行学习，这一过程可视为递归的统计估计程序。这些模型和相关的评估程序的前景，以及在心理学、计算机科学、遗传学、语言学和工程学等一系列学科中显而易见的兴奋，是建立在神经网络建模在解决各种困难问题方面取得成功的证明基础上的。尤其令人印象深刻的是对以前抵制传统解决尝试的问题的解决方案，以及对以前勉强产生类似有效解决方案的问题的相对快速和可靠的解决方案，以及经过几个人年的更常规努力后的解决方案。该项目的目标是(1)考察神经网络模型在经济现象研究中的适用性，并朝着适合经济现象研究的方向改进和扩展这些模型；(2)改进和扩展用于训练网络的学习方法(估计过程)，以获得在面对经济数据时快速而可靠地收敛的参数估计，以及(3)将研究人员在以前的资助研究中开发的模型规范和选择技术应用于神经网络模型，以便开发用于在特定问题的竞争神经网络结构中进行选择的技术。这是一个令人兴奋的项目，因为从来没有人将神经网络模型应用于经济学。这些新方法将大大减少解决复杂经济问题所需的计算时间。神经网络模型将为研究经济主体从环境中学习的方式提供一种新的方法。神经网络似乎特别适合于非线性经济预测，因此这些新方法可以为我们提供更好的经济未来预测。