Nonparametric, Semiparametric, and Bootstrap Methods in Econometrics

计量经济学中的非参数、半参数和 Bootstrap 方法

• 批准号: 9910925
• 负责人: Joel Horowitz
• 金额: $ 19.59万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-04-01 至 2001-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9910925&HistoricalAwards=false
• 关键词: Nonparametric; Semiparametric; Bootstrap; Methods; Econometrics

This project consists of research on three topics with wide potential empirical application: testing parametric models against nonparametric alternatives, Semiparametric estimation of proportional hazard models with unobserved heterogeneity, and bootstrap methods for econometric models estimated from time series. All build on prior work by the investigator. The first topic is concerned with testing a parametric model of a (possibly vector valued) conditional moment or quartile function against a nonparametric alternative. The research will develop tests that are uniformly consistent over smooth, nonparametric alternative models whose distance from the parametric model converges to zero at the fastest possible rate as the sample size increases. Tests which do not require a priori knowledge of the smoothness of the alternative model will also be developed. These properties provide important practical benefits in terms of power, and the new research will develop tests for more general parametric models and for models based on time-series data. The second topic is concerned with estimating proportional hazard models with unobserved heterogeneity. Neither the baseline hazard function nor the distribution of the unobserved heterogeneity will be assumed to belong to a known, finite-dimensional parametric family of functions. They will be treated nonparametrically. The research will focus particularly on fixed-effects models for panel data, models with covariates that are time varying within spells, and cross-sectional models in which the unobserved heterogeneity is an unknown form of heteroskedasticity. All of the models are relevant to applied research. The third topic is concerned with testing hypotheses about a finite-dimensional parameter that is estimated by the generalized method of moments (GMM) using dependent data. Since first-order approximations can be very inaccurate with the sample sizes found in applications, the true and nominal probabilities that a test rejects a correct null hypothesis can be very different when critical values are based on first-order asymptotic approximations. Similarly, the true and nominal coverage probabilities of confidence intervals based on first-order approximations can be very different. The block bootstrap provides a way to obtain improved approximations with dependent data, but recent research has shown that the amount of improvement is not large. The new research will investigate the use of the sieve bootstrap in which the data generation process is approximated by an expanding sequence of finite-dimensional parametric models, and bootstrap samples are generated by simulation from the sieve approximation. In settings much simpler than those of GMM estimation and testing, it has been found that the sieve bootstrap provides a substantially greater improvement over first-order approximations than does the block bootstrap. The new research will investigate whether the improved performance of the sieve bootstrap extends to the kinds of tests and models to which for which GMM is typically used in economics. In addition, the research will investigate whether iterated versions of the sieve bootstrap can be used to provide asymptotic refinements without the need for heteroskedasticity and autocorrelation consistent (MAC) covariance matrix estimation.

本项目包括三个具有广泛实证应用潜力的主题：参数模型对非参数替代方案的检验，未观察到异质性的比例风险模型的半参数估计，以及从时间序列估计的计量经济模型的自举方法。所有这些都建立在调查员之前的工作基础上。第一个主题是测试（可能是矢量值）条件矩或四分位函数的参数模型与非参数替代。该研究将开发在平滑、非参数替代模型上一致的测试，随着样本量的增加，这些模型与参数模型的距离以尽可能快的速度收敛到零。还将开发不需要先验地了解备选模型的平滑性的测试。这些特性在功率方面提供了重要的实际好处，新的研究将为更一般的参数模型和基于时间序列数据的模型开发测试。第二个主题是关于估计未观察到异质性的比例风险模型。基线危险函数和未观察到的异质性的分布都不会被假设属于已知的有限维参数函数族。它们将被非参数化处理。研究将特别侧重于面板数据的固定效应模型，在时间段内随时间变化的协变量模型，以及未观察到的异质性是一种未知形式的异方差的横截面模型。所有模型都与应用研究相关。第三个主题是关于检验关于有限维参数的假设，该参数是由广义矩量法（GMM）使用相关数据估计的。由于在应用程序中发现的一阶近似与样本量可能非常不准确，因此当临界值基于一阶渐近近似时，检验拒绝正确零假设的真实概率和名义概率可能会非常不同。类似地，基于一阶近似的置信区间的真实覆盖概率和名义覆盖概率可能非常不同。块引导提供了一种利用相关数据获得改进的近似的方法，但最近的研究表明，改进的量并不大。新的研究将研究筛子自举的使用，其中数据生成过程由有限维参数模型的扩展序列近似，并通过筛近似的模拟生成自举样本。在比GMM估计和测试简单得多的设置中，已经发现筛引导比块引导提供了比一阶近似更大的改进。新的研究将调查是否改进的性能筛自举延伸到各种测试和模型，其中GMM通常用于经济学。此外，本研究将探讨是否可以使用迭代版本的筛自举法来提供渐近改进，而不需要异方差和自相关一致（MAC）协方差矩阵估计。