一个深刻影响计量经济学发展的哈佛天才, 获得了2021年小诺贝尔奖！

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2021年的贝茨克拉克奖（小诺贝尔奖的美誉）颁发给了哈佛大学经济学教授Isaiah Andrews（非裔美国人），年仅35岁就是AER的联合主编，QJE，ECM，JOM等的副主编，父亲 Marcellus Andrews和母亲Cheryl Smith都是经济学家，2014年从MIT毕业，2020年刚当选世界计量经济学会会士，其博士生导师为Econometric theory期刊的联合主编Anna Mikusheva（44岁的女导师）。

以下是美国经济协会对其学术贡献的评价：

以赛亚·安德鲁斯（Isaiah Andrews）对计量经济学理论和实证实践的贡献提高了经济学中定量研究的质量、可信度和可交流性。在最近计量经济学转向研究实证研究中面临的最重要问题的过程中，他发挥了关键作用。安德鲁斯的贡献主要体现在三个方面。第一个是提供一些方法，使参数估计对用于估计它们的数据特征的敏感性更加透明。第二个问题涉及发表偏倚问题和有关如何从已选择的估计中得出推论的相关问题。第三个问题是存在弱识别时的估计和推断。

表征参数估计值对估计时刻和模型假设的敏感性的工具。

在测量参数估计对估计矩的敏感性(与Matthew Gentzkow和Jesse M. Shapiro, QJE，2017)中，他展示了如何量化参数估计对假设的敏感性，这些假设决定了估计量与数据特征之间的关系。第一个是敏感度矩阵。这个矩阵决定了当真实模型偏离假设模型时，我们感兴趣的参数将如何变化。第二个是偏离的大小。在最小二乘回归的情况下，这些是遗漏变量偏差公式的组成部分。安德鲁斯和他的合著者将公式推广到一个广泛的模型类别，并通过令人信服的经验示例证明了其有效性。这些方法正在成为应用研究人员工具包的标准组成部分。

同样重要的是“关于用于结构估计的描述性统计的信息性”（与Matthew Gentzkow和Jesse M. Shapiro，Econometrica，2020年）。研究人员非正式地讨论了什么统计数据推动了他们估计量的抽样分布。但是它们是否正确尚不清楚。研究人员还试图通过将基于模型的预测与描述性统计数据（均值，回归系数等）进行比较来验证结构模型。但是，通常很难知道研究人员所做的比较是否能为模型提供反事实预测的信息。

安德鲁斯和他的合作者提供了一种直观的，理论上扎根的方法来量化特定统计模型对特定感兴趣对象的估计（例如反事实政策的效果）的程度。他们的方法可以应用于各种各样的模型，并且本文中的经验示例说明了如何进行。

这两篇论文都有望从根本上改变经济学家评估和表达结果的方式。

选择后的发表偏倚和推断

“发表偏倚的识别和纠正”（与M. Kasy合作，AER，2019年）是对不断增长的发表偏倚文献的主要贡献。本文提出了选择性报告行为和选择发表的模型。它表明具有与已发表研究相同设计的重复研究可用于获得目标参数的无条件分布。此分布以及已发布研究中参数估计值的分布可用于校正发表偏倚。可以在包含已发表论文和未发表论文的元研究中使用相同的想法。本文对实证实践和发表研究规则具有重要意义。

安德鲁斯（Andrews）最近的工作论文“对获胜者的推断”（与Toru Kitagawa和Adam McCloskey合作，2020年）展示了如何对必须作为选择依据的一组选择中的“最佳”参数进行推断。根据样本估计。例如，可能会进行一项针对多种治疗方法的实验，目的是确定要推荐给政策制定者的政策干预方法。根据包含抽样误差的处理效应估计值选择最佳治疗方法，会导致“赢家的诅咒”问题偏倚。安德鲁斯和他的合作者提供了消除这种偏倚的估计量。

推断弱识别

在一系列论文中，当弱识别可能是广泛的非线性模型的一种可能时，安德鲁斯提供了更好的方法来进行统计推断。并且他提供了可以在不预先知道是否是弱识别的情况下应用的程序，并且在识别强的情况下可以很好地执行的程序。

例如，“带有功能性冗余参数的条件推断”（与计量经济学家Anna Mikushev合作，2016年），当所使用的矩条件可能不足以识别感兴趣的参数时，考虑对一大类矩条件模型的统计推断。与线性IV模型相反，GMM模型本质上是半参数模型。可以把矩方程的分布看作是包含了一个冗余的功能参数，它来自模型的非参数部分。本文的核心思想是将一个测试统计量的分布条件限定在一个足够的统计量上。这为正确的大小和良好的功率特性的检验提供了基础。本文不仅影响了实证实践，也影响了计量经济学家研究GMM模型的范式。

经济学中的许多实证论文通过选择结构参数来最小化关于简约形式参数的模型预测和这些参数的样本估计之间的距离来估计结构参数。只有当简约形式参数的抽样分布相对于作为结构参数函数的模型预测的非线性程度足够紧密时，统计推断和基于标准渐近理论是合理的。在“弱识别计量经济模型的几何方法”中（与计量经济学家Anna Mikusheva合作，2016），安德鲁斯（Andrews）和米库谢娃（Mikusheva）使用微分几何来导出一致渐近有效的最小距离检验。这些检验适用于各种数据生成过程和结构模型。这篇富有创意的论文为弱识别的本质提供了深刻的见解，并为可能不适用标准渐近推断的情况提供了更好的工具。

看看美国经济协会如何看待Andrews的贡献的：

Isaiah Andrews’ contributions to econometric theory and empirical practice have improved the quality, credibility, and communication of quantitative research in economics. He is playing a key role in the recent turn of econometrics back toward the study of the most important problems faced in empirical research. Andrews’ contributions fall in three main areas. The first is to provide ways to make the sensitivity of parameter estimates to the features of the data used to estimate them more transparent. The second concerns the problem of publication bias and related problems concerning how to draw inferences from estimates that have been selected. The third concerns estimation and inference in the presence of weak identification.

Tools to characterize the sensitivity of parameter estimates to estimation moments and model assumptions.

In “Measuring the Sensitivity of Parameter Estimates to Estimation Moments” (with Matthew Gentzkow and Jesse M. Shapiro, QJE, 2017) he shows how to quantify the sensitivity of parameter estimates to the assumptions that determine the relationship between the estimator and features of the data. The first ingredient is the “sensitivity matrix”. This matrix determines how the parameter of interest changes as the true model deviates from the assumed model. The second ingredient is the size of the departure. In the case of least squares regression, these are the ingredients of the omitted variables bias formula. Andrews and his co-authors generalize the formula to a broad class of models and demonstrate its usefulness with compelling empirical examples. The methods are becoming a standard part of the toolkit of applied researchers.

Equally important is “On the Informativeness of Descriptive Statistics for Structural Estimates” (with Matthew Gentzkow and Jesse M. Shapiro, Econometrica, 2020). Researchers informally discuss what statistics drive the sampling distribution of their estimator. But whether they are correct is not clear. Researchers also attempt to validate structural models by comparing predictions based on the model, with descriptive statistics (means, regression coefficients, etc.). However, it is often hard to know whether the comparisons researchers make are informative about the suitability of the model for counterfactual predictions.

Andrews and his co-authors provide an intuitive and theoretically grounded way to quantify the degree to which particular statistics discipline model estimates of a particular object of interest, such as the effect of a counterfactual policy. Their approach can be applied to a broad class of models, and the empirical examples in the paper show how.

The two papers hold the promise of fundamentally changing the way economists assess and communicate their results.

Publication Bias and Inference after Selection

“Identification of and Correction for Publication Bias” (with M. Kasy, AER, 2019) is a major contribution to the growing literature on publication bias. The paper proposes models of selective reporting behavior and selection into publication. It shows that replication studies with the same design as the published studies can be used to obtain the unconditional distribution of a parameter of interest. This distribution, along with the distribution of the parameter estimates in the published studies, can be used to correct for publication bias. The same idea can be used in a meta-study that includes both published papers and unpublished papers. The paper has important implications for empirical practice and for rules about publishing studies.

Andrews’ recent working paper, “Inference on Winners” (with Toru Kitagawa and Adam McCloskey, 2020) shows how to conduct inference about a parameter that is chosen as the “best” out of a set of choices, where the choice must be based on sample estimates. For example, one might run an experiment with multiple treatments with the aim of identifying the treatment to recommend to a policy maker. Choosing the best treatment based on the treatment effect estimates, which contain sampling error, leads to bias from a “winner’s curse” problem. Andrews and his co-authors provide estimators that eliminate this bias.

Inference with Weak Identification

In a series of papers, Andrews has provided better ways to perform statistical inference when weak identification is a possibility for a broad class of nonlinear models. And he has provided procedures that can be applied without knowing in advance whether identification is weak and which perform well if identification is strong.

For example, “Conditional Inference with a Functional Nuisance Parameter” (with Anna Mikusheva, Econometrica, 2016) considers statistical inference for a broad class of moment condition models when the moment conditions used may not be sufficient to identify the parameter of interest. In contrast to the linear IV model, the GMM model is essentially a semi-parametric model. One can think of the distribution of the moment equations as involving a nuisance functional parameter that arises from the nonparametric part of the model. The key idea of the paper is to condition the distribution of a test statistic on a sufficient statistic for the nuisance functional parameter. This provides the basis for a test with correct size and good power properties. The paper is influencing both empirical practice and how econometricians study GMM models.

Many empirical papers in economics estimate the structural parameters by choosing them to minimize the distance between model predictions about reduced form parameters and sample estimates of those parameters. Statistical inference and based on standard asymptotic theory is justified only if the sampling distribution of the reduced form parameters is tight enough relative to the degree of nonlinearity in the model predictions as a function of the structural parameters. In “A Geometric Approach to Weakly Identified Econometric Models” (with Anna Mikusheva, Econometrica, 2016), Andrews and Mikusheva use differential geometry to derive uniformly asymptotically valid minimum distance tests. The tests are applicable for a wide range of data generating processes and structural models. This creative paper offers deep insights into the nature of weak identification and provides a better tool for situations in which standard asymptotic inference may not apply.

Source: https://www.aeaweb.org/about-aea/honors-awards/bates-clark/isaiah-andrews

安德鲁斯（Andrews）的发表如下：

“Transparency in Structural Research” with Matthew Gentzkow and Jesse M. Shapiro (invited discussion paper) Forthcoming at Journal of Business and Economic Statistics

“Inference After Estimation of Breaks” with Toru Kitagawa and Adam McCloskey Forthcoming at Journal of Econometrics “

On the Informativeness of Descriptive Statistics for Structural Estimates” with Matthew Gentzkow and Jesse M. Shapiro (Matthew Gentzkow’s Fisher-Schultz Lecture) Econometrica (2020), 88, 2231-2258 “

A Simple Approximation for Evaluating External Validity Bias” with Emily Oster Economics Letters (2019), 178, 58-62

“Weak Instruments in IV Regression: Theory and Practice” with James Stock and Liyang Sun Annual Review of Economics (2019), 11, 727-753.

“Identification of and Correction for Publication Bias” with Maximilian Kasy American Economic Review (2019), 109(8), 2766-2794 “On the Structure of IV Estimands” Journal of Econometrics (2019), 211(1), 294-307

“Valid Two-Step Identification-Robust Confidence Sets for GMM” Review of Economics and Statistics (2018), 100(2), 337-348

“Measuring the Sensitivity of Parameter Estimates to Estimation Moments” with Matthew Gentzkow and Jesse M. Shapiro Quarterly Journal of Economics (2017), 132(4), 1553-1592

“Unbiased Instrumental Variables Estimation Under Known First-Stage Sign” with Timothy B. Armstrong Quantitative Economics (2017), 8(2), 479-503 “Conditional Linear Combination Tests for Weakly Identified Models” Econometrica (2016), 84(6), 2155-2182

“The Allocation of Future Business: Dynamic Relational Contracts with Multiple Agents” with Daniel Barron American Economic Review (2016), 106(9), 2742-2759

“Conditional Inference with a Functional Nuisance Parameter” with Anna Mikusheva Econometrica (2016), 84(4), 1571-1612

“A Geometric Approach to Weakly Identified Econometric Models” with Anna Mikusheva Econometrica (2016), 84(3), 1249-1264

“Maximum Likelihood Inference in Weakly Identified DSGE Models” with Anna Mikusheva Quantitative Economics (2015), 6(1),123-152

“Weak Identification in Maximum Likelihood: A Question of Information” with Anna Mikusheva American Economic Reviews: Papers and Proceedings (2014), 104(5), 195-199

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