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

认知科学家最近开发了一类丰富而有趣的非线性模型，其灵感来自于大脑的神经结构（神经网络模型）。这些网络能够通过与环境的相互作用来学习，这个过程可以看作是一个递归的统计估计过程。这些模型和相关的估计程序的前景，以及在包括心理学、计算机科学、遗传学、语言学和工程学在内的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各样的难题方面取得成功的基础上的。尤其令人印象深刻的是，解决了以前无法用常规方法解决的问题，以及相对迅速和可靠地解决了以前勉强得到相当有效解决的问题。经过几年的传统努力。该项目的目标是：(1)研究神经网络模型在经济现象研究中的适用性，并在适合经济现象研究的方向上完善和扩展这些模型；(2)改进和扩展用于训练网络的学习方法（估计过程），以便在面对经济数据时获得快速可靠收敛的参数估计；(3)将研究者在之前资助的研究中开发的模型规范和选择技术应用于神经网络模型，以开发针对特定问题在竞争的神经网络架构之间进行选择的技术。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。

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.