Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2026.106184
Yao Luo , Peijun Sang
We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators circumvent repeated solution of the structural model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve unconstrained optimization problems with fewer unknowns and offer convenient standard error calculations. As an illustration, we apply our method to an entry game between Walmart and Kmart.
{"title":"Efficient estimation of structural models via sieves","authors":"Yao Luo , Peijun Sang","doi":"10.1016/j.jeconom.2026.106184","DOIUrl":"10.1016/j.jeconom.2026.106184","url":null,"abstract":"<div><div>We propose a class of sieve-based efficient estimators for structural models (SEES), which approximate the solution using a linear combination of basis functions and impose equilibrium conditions as a penalty to determine the best-fitting coefficients. Our estimators circumvent repeated solution of the structural model, apply to a broad class of models, and are consistent, asymptotically normal, and asymptotically efficient. Moreover, they solve <em>unconstrained</em> optimization problems with fewer unknowns and offer convenient standard error calculations. As an illustration, we apply our method to an entry game between Walmart and Kmart.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106184"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976962","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.2025.106172
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}
Pub Date : 2026-01-01DOI: 10.1016/j.jeconom.2025.106178
Nan Liu , Yanbo Liu , Yuya Sasaki
We propose methods for estimation and uniform inference for a broad class of causal functions, such as conditional average treatment effects and continuous treatment effects, under multi-way clustering. The causal function is identified as the conditional expectation of a Neyman-orthogonal signal that depends on high-dimensional nuisance parameters. We introduce a two-step procedure: the first step uses machine learning to estimate the nuisance parameters, and the second step projects the estimated Neyman-orthogonal signal onto a dictionary of basis functions whose dimension grows with the sample size. We consider both full-sample and multi-way cross-fitting approaches to this procedure and derive a functional limit theory for the resulting estimators. For uniform inference, we develop a novel resampling method, the multi-way cluster-robust sieve score bootstrap, which extends the sieve score bootstrap of Chen and Christensen (2018) to settings with multi-way clustering. Extensive simulations demonstrate that the proposed methods exhibit favorable finite-sample performance. We apply our approach to study the causal relationship between mistrust levels in Africa and historical exposure to the slave trade. Accounting for the two-way clustering by ethnicity and region, our inference method rejects the null hypothesis of uniformly zero effects and uncover heterogeneous treatment effects, with particularly strong impacts in regions with high historical trade intensity.
{"title":"Estimation and inference for causal functions with multi-way clustered data","authors":"Nan Liu , Yanbo Liu , Yuya Sasaki","doi":"10.1016/j.jeconom.2025.106178","DOIUrl":"10.1016/j.jeconom.2025.106178","url":null,"abstract":"<div><div>We propose methods for estimation and uniform inference for a broad class of causal functions, such as conditional average treatment effects and continuous treatment effects, under multi-way clustering. The causal function is identified as the conditional expectation of a Neyman-orthogonal signal that depends on high-dimensional nuisance parameters. We introduce a two-step procedure: the first step uses machine learning to estimate the nuisance parameters, and the second step projects the estimated Neyman-orthogonal signal onto a dictionary of basis functions whose dimension grows with the sample size. We consider both full-sample and multi-way cross-fitting approaches to this procedure and derive a functional limit theory for the resulting estimators. For uniform inference, we develop a novel resampling method, <em>the multi-way cluster-robust sieve score bootstrap</em>, which extends the sieve score bootstrap of Chen and Christensen (2018) to settings with multi-way clustering. Extensive simulations demonstrate that the proposed methods exhibit favorable finite-sample performance. We apply our approach to study the causal relationship between mistrust levels in Africa and historical exposure to the slave trade. Accounting for the two-way clustering by ethnicity and region, our inference method rejects the null hypothesis of uniformly zero effects and uncover heterogeneous treatment effects, with particularly strong impacts in regions with high historical trade intensity.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106178"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938446","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.2026.106182
Ignace De Vos , Gerdie Everaert
Local projections (LPs) are widely used for estimating impulse responses (IRs) as they are considered more robust to model misspecification than forward-iterated IRs from dynamic models such as VARs. However, this robustness comes at the cost of higher variance, particularly at longer horizons. To mitigate this trade-off, several GLS transformations of LPs have been proposed. This paper analyzes two broad strands of GLS-type LP estimators: those that condition on residuals from an auxiliary VAR, and those that condition on residuals from previous-horizon LPs. We show that the former impose a VAR structure, which leads them to align with VAR IRs, while the latter preserve the unrestricted nature of LPs but end up replicating LP OLS estimates. Consequently, the intended efficiency gains are either not achieved or come at the expense of the very robustness that motivates the use of LPs.
{"title":"GLS estimation of local projections: Trading robustness for efficiency","authors":"Ignace De Vos , Gerdie Everaert","doi":"10.1016/j.jeconom.2026.106182","DOIUrl":"10.1016/j.jeconom.2026.106182","url":null,"abstract":"<div><div>Local projections (LPs) are widely used for estimating impulse responses (IRs) as they are considered more robust to model misspecification than forward-iterated IRs from dynamic models such as VARs. However, this robustness comes at the cost of higher variance, particularly at longer horizons. To mitigate this trade-off, several GLS transformations of LPs have been proposed. This paper analyzes two broad strands of GLS-type LP estimators: those that condition on residuals from an auxiliary VAR, and those that condition on residuals from previous-horizon LPs. We show that the former impose a VAR structure, which leads them to align with VAR IRs, while the latter preserve the unrestricted nature of LPs but end up replicating LP OLS estimates. Consequently, the intended efficiency gains are either not achieved or come at the expense of the very robustness that motivates the use of LPs.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106182"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034688","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.2026.106180
Adam Baybutt , Manu Navjeevan
Plausible identification of conditional average treatment effects (CATEs) can rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second-stage CATE estimator are not doubly-robust. Using the popular augmented inverse propensity weighting signal, we propose an estimator for the CATE whose resulting Wald-type confidence intervals are doubly-robust. We assume a logistic model for the propensity score and a linear model for the outcome regression, and estimate the parameters of these models using an ℓ1 (Lasso) penalty to address the high-dimensional covariates. Inference based on this estimator remains valid even if one of the logistic propensity score or linear outcome regression models are misspecified.
{"title":"Doubly-robust inference for conditional average treatment effects with high-dimensional controls","authors":"Adam Baybutt , Manu Navjeevan","doi":"10.1016/j.jeconom.2026.106180","DOIUrl":"10.1016/j.jeconom.2026.106180","url":null,"abstract":"<div><div>Plausible identification of conditional average treatment effects (CATEs) can rely on controlling for a large number of variables to account for confounding factors. In these high-dimensional settings, estimation of the CATE requires estimating first-stage models whose consistency relies on correctly specifying their parametric forms. While doubly-robust estimators of the CATE exist, inference procedures based on the second-stage CATE estimator are not doubly-robust. Using the popular augmented inverse propensity weighting signal, we propose an estimator for the CATE whose resulting Wald-type confidence intervals are doubly-robust. We assume a logistic model for the propensity score and a linear model for the outcome regression, and estimate the parameters of these models using an ℓ<sub>1</sub> (Lasso) penalty to address the high-dimensional covariates. Inference based on this estimator remains valid even if one of the logistic propensity score or linear outcome regression models are misspecified.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106180"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976963","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.2026.106189
Jizhou Liu
This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-“average of averages” estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Then, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. Finally, I discuss covariate adjustment, which incorporates additional baseline covariates not used for treatment assignment. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application.
{"title":"Inference for two-stage experiments under covariate-adaptive randomization","authors":"Jizhou Liu","doi":"10.1016/j.jeconom.2026.106189","DOIUrl":"10.1016/j.jeconom.2026.106189","url":null,"abstract":"<div><div>This paper studies inference in two-stage randomized experiments under covariate-adaptive randomization. In the initial stage of this experimental design, clusters (e.g., households, schools, or graph partitions) are stratified and randomly assigned to control or treatment groups based on cluster-level covariates. Subsequently, an independent second-stage design is carried out, wherein units within each treated cluster are further stratified and randomly assigned to either control or treatment groups, based on individual-level covariates. Under the homogeneous partial interference assumption, I establish conditions under which the proposed difference-in-“average of averages” estimators are consistent and asymptotically normal for the corresponding average primary and spillover effects and develop consistent estimators of their asymptotic variances. Combining these results establishes the asymptotic validity of tests based on these estimators. My findings suggest that ignoring covariate information in the design stage can result in efficiency loss, and commonly used inference methods that ignore or improperly use covariate information can lead to either conservative or invalid inference. Then, I apply these results to studying optimal use of covariate information under covariate-adaptive randomization in large samples, and demonstrate that a specific generalized matched-pair design achieves minimum asymptotic variance for each proposed estimator. Finally, I discuss covariate adjustment, which incorporates additional baseline covariates not used for treatment assignment. The practical relevance of the theoretical results is illustrated through a simulation study and an empirical application.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106189"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034687","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.2025.106174
Konrad Menzel
We derive asymptotic approximations for models of strategic network formation, where limits are taken as the number of nodes (agents) increases to infinity. Our framework assumes a random utility model where agents have heterogeneous tastes over links, and payoffs allow for anonymous and non-anonymous interaction effects, and the observed network is assumed to be pairwise stable. Our main results concern convergence of the link intensity from finite pairwise stable networks to the (many-player) limiting distribution. The set of possible limiting distributions is shown to have a fairly simple form and is characterized through aggregate equilibrium conditions, which may permit multiple solutions. We illustrate how these formal results can be used to analyze identification of link preferences and estimate or bound preference parameters. We also derive an analytical expression for agents’ welfare (expected surplus) from the structure of the network.
{"title":"Strategic network formation with many agents","authors":"Konrad Menzel","doi":"10.1016/j.jeconom.2025.106174","DOIUrl":"10.1016/j.jeconom.2025.106174","url":null,"abstract":"<div><div>We derive asymptotic approximations for models of strategic network formation, where limits are taken as the number of nodes (agents) increases to infinity. Our framework assumes a random utility model where agents have heterogeneous tastes over links, and payoffs allow for anonymous and non-anonymous interaction effects, and the observed network is assumed to be pairwise stable. Our main results concern convergence of the link intensity from finite pairwise stable networks to the (many-player) limiting distribution. The set of possible limiting distributions is shown to have a fairly simple form and is characterized through aggregate equilibrium conditions, which may permit multiple solutions. We illustrate how these formal results can be used to analyze identification of link preferences and estimate or bound preference parameters. We also derive an analytical expression for agents’ welfare (expected surplus) from the structure of the network.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106174"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880566","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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