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Working Paper
Validating Large Language Model Annotations
Lundgaard Hansen, Anne (2026-03-30)
This paper proposes a validation framework for LLM-generated measurements when reliable benchmarks are unavailable. Validity is established by testing whether an LLM can reconstruct passages from annotated labels while maintaining semantic consistency with the original text. The framework avoids circular reasoning by establishing testable prerequisite properties that must be met for a validation to be considered successful. Application to news article data demonstrates that the framework serves as a practical alternative to human benchmarking, which offers advantages in objectivity, ...
Finance and Economics Discussion Series , Paper 2026-020

Working Paper
Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
Hu, Luojia; Honore, Bo E. (2015-09-15)
The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honor and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. ...
Working Paper Series , Paper WP-2015-7

Report
OLS Limit Theory for Drifting Sequences of Parameters on the Explosive Side of Unity
Magdalinos, Tassos; Petrova, Katerina (2024-08-01)
A limit theory is developed for the least squares estimator for mildly and purely explosive autoregressions under drifting sequences of parameters with autoregressive roots ρn satisfyingρn → ρ ∈ (—∞, —1] ∪ [1, ∞) and n (|ρn| —1) → ∞.Drifting sequences of innovations and initial conditions are also considered. A standard specification of a short memory linear process for the autoregressive innovations is extended to a triangular array formulation both for the deterministic weights and for the primitive innovations of the linear process, which are allowed to be ...
Staff Reports , Paper 1113

Report
Nonlinear Binscatter Methods
Cattaneo, Matias D.; Crump, Richard K.; Feng, Yingjie; Farrell, Max H. (2024-08-01)
Binscatters are a powerful tool for empirical work in the social, behavioral, and biomedical sciences. Available tools rely on least squares estimation of the conditional mean. We introduce novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation, covering generalized linear, robust, and quantile regression models. We provide theoretical results and practical tools, including optimal bin selection, confidence bands, and statistical tests regarding functional form or shape restrictions. We demonstrate our methods by studying the relationship of income and (lack of) health ...
Staff Reports , Paper 1110

Working Paper
Composition-Adjusted Wage Growth: A Robust Measure from Microdata
Honore, Bo E.; Hu, Luojia (2025-07)
Wage growth is a key indicator of labor market conditions, but common measures often conflate individual wage changes with shifts in workforce composition. This paper develops a composition-adjusted measure of wage growth using nonparametric decomposition and program evaluation methods. The adjusted measure tracks unadjusted growth in stable periods but diverges during disruptions: during the Covid-19 pandemic, wage growth falls from 12% to 6% after adjustment. The method accommodates rich covariates, is robust to data quality issues such as rounding, heaping and top-coding, and enables ...
Working Paper Series , Paper WP 2025-14

Working Paper
Understanding Models and Model Bias with Gaussian Processes
Palmer, Nathan M.; Cook, Thomas R. (2023-06-15)
Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential biases of these models, especially if our access to the underlying model is limited? We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty in the underlying ...
Research Working Paper , Paper RWP 23-07

Working Paper
The Income-Achievement Gap and Adult Outcome Inequality
Nielsen, Eric R. (2015-05-14)
This paper discusses various methods for assessing group differences in academic achievement using only the ordinal content of achievement test scores. Researchers and policymakers frequently draw conclusions about achievement differences between various populations using methods that rely on the cardinal comparability of test scores. This paper shows that such methods can lead to erroneous conclusions in an important application: measuring changes over time in the achievement gap between youth from high- and low-income households. Commonly-employed, cardinal methods suggest that this ...
Finance and Economics Discussion Series , Paper 2015-41

Working Paper
Robust Bayesian Analysis for Econometrics
Giacomini, Raffaella; Read, Matthew; Kitagawa, Toru (2021-08-23)
We review the literature on robust Bayesian analysis as a tool for global sensitivity analysis and for statistical decision-making under ambiguity. We discuss the methods proposed in the literature, including the different ways of constructing the set of priors that are the key input of the robust Bayesian analysis. We consider both a general set-up for Bayesian statistical decisions and inference and the special case of set-identified structural models. We provide new results that can be used to derive and compute the set of posterior moments for sensitivity analysis and to compute the ...
Working Paper Series , Paper WP-2021-11

Working Paper
Easy Bootstrap-Like Estimation of Asymptotic Variances
Hu, Luojia; Honore, Bo E. (2018-06-29)
The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the bootstrap can be very time-consuming. In a recent paper, Honor and Hu (2017), we propose a ?Poor (Wo)man's Bootstrap? based on one-dimensional estimators. In this paper, we propose a modified, simpler method and illustrate its potential for estimating asymptotic variances.
Working Paper Series , Paper WP-2018-11

Working Paper
Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning
Abowd, John M.; Abramowitz, Joelle Hillary; Levenstein, Margaret Catherine; McCue, Kristin; Patki, Dhiren; Raghunathan, Trivellore; Rodgers, Ann Michelle; Shapiro, Matthew D.; Wasi, Nada; Zinsser, Dawn (2021-10-01)
This paper considers the problem of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across establishments is highly skewed. To address these difficulties, this paper develops a probabilistic record linkage methodology that combines machine learning (ML) with multiple imputation (MI). This ML-MI methodology is applied to link survey respondents in the Health and Retirement Study to their workplaces in the Census Business Register. ...
Working Papers , Paper 22-11

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