Naturally Occurring Noise: Experimental Economics & Stochastic Production Frontier Models

该项目考察了如何使用来自人类受试者的受控实验数据来提高对潜在数据生成过程的统计估计器的理解和应用。受控实验可以产生自然产生的噪声，这些噪声可以作为标准蒙特卡罗方法合成噪声的补充。此外，这些数据将证明使用一般统计技术来纠正经济学中经常被忽视的变量问题中的错误的重要性。通过这种方式，可以使用更自然的数据测量来测试应用于人类行为的一般统计估计，并为社会科学家在该领域应用产生更清晰的测量工具。具体来说，该方法被应用于对经济学中常用的随机生产前沿模型的几个估计器的小样本性质的研究，以了解它们如何能够很好地预测潜在的性能前沿。本研究利用了看似不同的经验方法之间的互补性，突出了实验作为统计方法测试的蒙特卡罗研究的补充的作用。通过使用来自一系列受控环境的实验数据作为现场使用的统计方法的测试平台，当真实的底层环境未知时，我们将更好地了解这些统计方法在现场应用中的可靠性。因此，这项研究有助于实验经济学更广泛的发展，更关注正确的统计方法，特别是正确的误差规范，以应用于如此丰富的数据。使用实验产生的自然噪声作为蒙特卡罗模拟的补充，对于检验对人类决策者产生的数据应用的任何统计方法都是有价值的。因此，我们将看到如何将来自受控实验室的经验教训和来自非受控领域的观察结果结合起来，对人类行为做出更可靠的统计推断。

The project examines the way in which controlled experimental data from human subjects can be used to improve the understanding and application of statistical estimators of latent data-generation processes. Controlled experiments can generate naturally occurring noise that can serve as a complement to the synthetic noise of standard Monte Carlo methods. In addition, those data will demonstrate the importance of using general statistical techniques for correcting for errors in variables problems that often are neglected in economics. In this manner, more natural measurements of data can be used to test general statistical estimators applied to human behavior and to generate sharper measurement tools for social scientists to apply in the field. Specifically, this methodology is applied to an investigation of the small-sample properties of several estimators for stochastic production frontier models commonly used in economics to see how well they are able to predict the latent performance frontier.This research exploits the complementarity between seemingly different empirical methods by highlighting the role of experiments as supplements to Monte Carlo studies for tests of statistical methods. By using experimental data from a range of controlled environments as a test-bed for statistical methods used in the field, we will have a better sense of the reliability of those statistical methods in field applications when the true underlying environment is unknown. Thus, the research contributes to a broader development within experimental economics to be much more concerned about the right statistical methods and in particular the right error specifications to be applied to such rich data. The use of natural noise from experiments, as a complement to Monte Carlo simulations, will be valuable for tests of any statistical method applied to data generated by human decision makers. Hence we will see how lessons from the controlled lab and observations from the uncontrolled field can be used together to make more reliable statistical inferences about human behavior.