Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2025.106172
Jiafeng Chen
This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment algorithms, students face both lottery-driven variation and regression discontinuity- (RD) driven variation. We characterize the full set of identified atomic treatment effects (aTEs), defined as the conditional average treatment effect between a pair of schools given student characteristics. Atomic treatment effects are the building blocks of more aggregated treatment contrasts, and common approaches to estimating aTE aggregations can mask important heterogeneity. In particular, many aggregations of aTEs put zero weight on aTEs driven by RD variation, and estimators of such aggregations put asymptotically vanishing weight on the RD-driven aTEs. We provide a diagnostic and recommend new aggregation schemes. Lastly, we provide estimators and asymptotic results for inference on these aggregations.
{"title":"Nonparametric treatment effect identification in school choice","authors":"Jiafeng Chen","doi":"10.1016/j.jeconom.2025.106172","DOIUrl":"10.1016/j.jeconom.2025.106172","url":null,"abstract":"<div><div>This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment algorithms, students face both lottery-driven variation and regression discontinuity- (RD) driven variation. We characterize the full set of identified <em>atomic treatment effects</em> (aTEs), defined as the conditional average treatment effect between a pair of schools given student characteristics. Atomic treatment effects are the building blocks of more aggregated treatment contrasts, and common approaches to estimating aTE aggregations can mask important heterogeneity. In particular, many aggregations of aTEs put zero weight on aTEs driven by RD variation, and estimators of such aggregations put asymptotically vanishing weight on the RD-driven aTEs. We provide a diagnostic and recommend new aggregation schemes. Lastly, we provide estimators and asymptotic results for inference on these aggregations.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106172"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2025.106171
Z. Merrick Li , Xiye Yang
We test for the presence of market frictions that induce transitory deviations of observed asset prices from the underlying efficient prices. Our test is based on the joint inference of return covariances across multiple horizons. We demonstrate that a small set of horizons suffices to identify a broad spectrum of frictions, both theoretically and practically. Our method works for high- and low-frequency data under different asymptotic regimes. Extensive simulations show our method outperforms widely used state-of-the-art tests. Our empirical studies indicate that intraday transaction prices from recent years can be considered effectively friction-free at significantly higher frequencies.
{"title":"Multi-horizon test for market frictions","authors":"Z. Merrick Li , Xiye Yang","doi":"10.1016/j.jeconom.2025.106171","DOIUrl":"10.1016/j.jeconom.2025.106171","url":null,"abstract":"<div><div>We test for the presence of market frictions that induce transitory deviations of observed asset prices from the underlying efficient prices. Our test is based on the joint inference of return covariances across multiple horizons. We demonstrate that a small set of horizons suffices to identify a broad spectrum of frictions, both theoretically and practically. Our method works for high- and low-frequency data under different asymptotic regimes. Extensive simulations show our method outperforms widely used state-of-the-art tests. Our empirical studies indicate that intraday transaction prices from recent years can be considered effectively friction-free at significantly higher frequencies.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106171"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2026.106188
Shiwei Huang , Yu Chen , Jie Hu , Weiping Zhang
This paper introduces a dynamic panel data quantile regression model with network-linked fixed effects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a quantile network cohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a particularly increased capacity to capture tail network dependence. Relative to conventional fixed-effects specifications, the proposed framework improves the estimation of unobserved heterogeneity and enables more accurate prediction in cold-start settings where training data are unavailable. We establish the consistency and asymptotic normality of the DQR-NFE estimators within a general nonlinear structural framework. These theoretical guarantees hold under both correctly specified and misspecified network structures, with an explicit characterization of their dependence on the network topology. Simulation studies and empirical applications reveal that the proposed estimator outperforms competing approaches in terms of both estimation accuracy and out-of-sample forecasting.
{"title":"Dynamic panel data quantile regression with network-linked fixed effects","authors":"Shiwei Huang , Yu Chen , Jie Hu , Weiping Zhang","doi":"10.1016/j.jeconom.2026.106188","DOIUrl":"10.1016/j.jeconom.2026.106188","url":null,"abstract":"<div><div>This paper introduces a <strong>d</strong>ynamic panel data <strong>q</strong>uantile <strong>r</strong>egression model with <strong>n</strong>etwork-linked <strong>f</strong>ixed <strong>e</strong>ffects, named DQR-NFE, in which unobserved individual heterogeneity is structured through an underlying network. The corresponding estimator is derived by incorporating a <strong>q</strong>uantile <strong>n</strong>etwork <strong>c</strong>ohesion (QNC) penalty into the dynamic panel quantile regression framework. This penalty encourages connected units within the network to exhibit similar conditional quantiles, with a particularly increased capacity to capture tail network dependence. Relative to conventional fixed-effects specifications, the proposed framework improves the estimation of unobserved heterogeneity and enables more accurate prediction in cold-start settings where training data are unavailable. We establish the consistency and asymptotic normality of the DQR-NFE estimators within a general nonlinear structural framework. These theoretical guarantees hold under both correctly specified and misspecified network structures, with an explicit characterization of their dependence on the network topology. Simulation studies and empirical applications reveal that the proposed estimator outperforms competing approaches in terms of both estimation accuracy and out-of-sample forecasting.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106188"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2025.106173
Serafin Grundl , Yu Zhu
This paper proposes a new approach to the identification of first-price auctions that is robust to overbidding, but at the same time remains contiguous with the canonical point-identification approach of Guerre et al. (2000) (GPV) and its simple estimators. We show that a weak identifying restriction allows us to reinterpret the GPV estimates as a bound. We demonstrate that the identifying restriction holds in a set of commonly used auction models that can generate overbidding and is satisfied in the bid data from a laboratory experiment. We illustrate the approach in applications to laboratory data and field data. We recommend that practitioners continue to follow the GPV approach, but interpret the estimates as a bound in applications where they are concerned about overbidding.
本文提出了一种新的识别首价拍卖的方法，该方法对超标价具有鲁棒性，但同时与Guerre et al. (2000) （GPV）及其简单估计器的标准点识别方法保持一致。我们表明，一个弱识别限制允许我们将GPV估计重新解释为一个界。我们证明了识别限制在一组常用的拍卖模型中成立，这些模型可以产生过高的出价，并且在实验室实验的出价数据中得到满足。我们在实验室数据和现场数据的应用中说明了这种方法。我们建议从业者继续遵循GPV方法，但在他们担心过高出价的应用程序中，将估计解释为一个界限。
{"title":"A simple, robust identification approach for first-price auctions","authors":"Serafin Grundl , Yu Zhu","doi":"10.1016/j.jeconom.2025.106173","DOIUrl":"10.1016/j.jeconom.2025.106173","url":null,"abstract":"<div><div>This paper proposes a new approach to the identification of first-price auctions that is robust to overbidding, but at the same time remains contiguous with the canonical point-identification approach of Guerre et al. (2000) (GPV) and its simple estimators. We show that a weak identifying restriction allows us to reinterpret the GPV estimates as a bound. We demonstrate that the identifying restriction holds in a set of commonly used auction models that can generate overbidding and is satisfied in the bid data from a laboratory experiment. We illustrate the approach in applications to laboratory data and field data. We recommend that practitioners continue to follow the GPV approach, but interpret the estimates as a bound in applications where they are concerned about overbidding.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106173"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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