{"title":"Dynamic treatment effect estimation with interactive fixed effects and short panels","authors":"Nicholas L. Brown , Kyle Butts","doi":"10.1016/j.jeconom.2025.106013","DOIUrl":"10.1016/j.jeconom.2025.106013","url":null,"abstract":"<div><div>We study the estimation and inference of dynamic average treatment effect parameters when parallel trends holds conditional on interactive fixed effects and where units enter into treatment at different time periods. Our proposed generalized method of moments estimator consists of two parts: first, we estimate the unobserved time effects by applying the fixed-<span><math><mi>T</mi></math></span> consistent quasi-long-differencing estimator of Ahn et al., (2013) to the never-treated group. Second, we estimate the interactive fixed effects for treated groups post-treatment to recover their unobserved counterfactual outcomes then subtract this quantity from the observed outcomes and average over group membership to estimate the Average Treatment Effect on the Treated. We also demonstrate the robustness of two-way fixed effects to certain parallel trends violations and describe how to test for consistency. We investigate the effect of Walmart openings on local economic conditions and demonstrate that our methods ameliorate pre-trend violations commonly found in the literature. We also provide statistical software to implement our estimator in <span>Julia</span> and <span>R</span>.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106013"},"PeriodicalIF":9.9,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927528","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}
{"title":"A simple and computationally trivial estimator for grouped fixed effects models","authors":"Martin Mugnier","doi":"10.1016/j.jeconom.2025.106011","DOIUrl":"10.1016/j.jeconom.2025.106011","url":null,"abstract":"<div><div>This paper introduces a new fixed effects estimator for linear panel data models with clustered time patterns of unobserved heterogeneity. The method avoids non-convex and combinatorial optimization by combining a preliminary consistent estimator of the slope coefficient, an agglomerative pairwise-differencing clustering of cross-sectional units, and a pooled ordinary least squares regression. Asymptotic guarantees are established in a framework where <span><math><mi>T</mi></math></span> can grow at any power of <span><math><mi>N</mi></math></span>, as both <span><math><mi>N</mi></math></span> and <span><math><mi>T</mi></math></span> approach infinity. Unlike most existing approaches, the proposed estimator is computationally straightforward and does not require a known upper bound on the number of groups. As existing approaches, this method leads to a consistent estimation of well-separated groups and an estimator of common parameters asymptotically equivalent to the infeasible regression controlling for the true groups. An application revisits the statistical association between income and democracy.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106011"},"PeriodicalIF":9.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922986","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}
{"title":"Faster estimation of dynamic discrete choice models using index invertibility","authors":"Jackson Bunting , Takuya Ura","doi":"10.1016/j.jeconom.2025.106004","DOIUrl":"10.1016/j.jeconom.2025.106004","url":null,"abstract":"<div><div>Many estimators of dynamic discrete choice models with persistent unobserved heterogeneity have desirable statistical properties but are computationally intensive. In this paper we propose a method to quicken estimation for a broad class of dynamic discrete choice problems by exploiting semiparametric index restrictions. Specifically, we propose an estimator for models whose reduced form parameters are invertible functions of one or more linear indices (Ahn et al., 2018) , a property we term index invertibility. We establish that index invertibility implies a set of equality constraints on the model parameters. Our proposed estimator uses the equality constraints to decrease the dimension of the optimization problem, thereby generating computational gains. Our main result shows that the proposed estimator is asymptotically equivalent to the unconstrained, computationally heavy estimator. In addition, we provide a series of results on the number of independent index restrictions on the model parameters, providing theoretical guidance on the extent of computational gains. Finally, we demonstrate the advantages of our approach via Monte Carlo simulations.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106004"},"PeriodicalIF":9.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923089","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}
{"title":"Dimension-agnostic change point detection","authors":"Hanjia Gao , Runmin Wang , Xiaofeng Shao","doi":"10.1016/j.jeconom.2025.106012","DOIUrl":"10.1016/j.jeconom.2025.106012","url":null,"abstract":"<div><div>Change point testing for high-dimensional data has attracted a lot of attention in statistics, econometrics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than the sample size but is not small, it is often unclear whether a method that is tailored to high-dimensional data or simply a classical method that is developed and justified for low-dimensional data is preferred. In addition, the methods designed for low-dimensional data may not work well in the high-dimensional environment and vice versa. In this paper, we propose a dimension-agnostic testing procedure targeting a single change point in the mean of a multivariate weakly dependent time series. Specifically, we can show that the limiting null distribution for our test statistic is the same regardless of the dimensionality and the magnitude of cross-sectional dependence. The power analysis is also conducted to understand the large sample behavior of the proposed test. Through Monte Carlo simulations and a real data illustration, we demonstrate that the finite sample results strongly corroborate the theory and suggest that the proposed test can be used as a benchmark for change-point detection of time series of low, medium, and high dimensions with complex cross-sectional and temporal dependence.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106012"},"PeriodicalIF":9.9,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923088","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}
{"title":"Pairwise valid instruments","authors":"Zhenting Sun , Kaspar Wüthrich","doi":"10.1016/j.jeconom.2025.106009","DOIUrl":"10.1016/j.jeconom.2025.106009","url":null,"abstract":"<div><div>Finding valid instruments is difficult. We propose Validity Set Instrumental Variable (VSIV) estimation, a method for estimating local average treatment effects (LATEs) in heterogeneous causal effect models when the instruments are partially invalid. We consider settings with pairwise valid instruments, that is, instruments that are valid for a subset of instrument value pairs. VSIV estimation exploits testable implications of instrument validity to remove invalid pairs and provides estimates of the LATEs for all remaining pairs, which can be aggregated into a single parameter of interest using researcher-specified weights. We show that the proposed VSIV estimators are asymptotically normal under weak conditions and remove or reduce the asymptotic bias relative to standard LATE estimators (that is, LATE estimators that do not use testable implications to remove invalid variation). We evaluate the finite sample properties of VSIV estimation in application-based simulations and apply our method to estimate the returns to college education using parental education as an instrument.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"250 ","pages":"Article 106009"},"PeriodicalIF":9.9,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917270","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}
{"title":"Inference on quantile processes with a finite number of clusters","authors":"Andreas Hagemann","doi":"10.1016/j.jeconom.2024.105672","DOIUrl":"10.1016/j.jeconom.2024.105672","url":null,"abstract":"<div><div>I introduce a generic method for inference on entire quantile<span> and regression quantile<span> processes in the presence of a finite number of large and arbitrarily heterogeneous clusters. The method asymptotically controls size by generating statistics that exhibit enough distributional symmetry such that randomization tests can be applied. The randomization test does not require ex-ante matching of clusters, is free of user-chosen parameters, and performs well at conventional significance levels with as few as five clusters. The method tests standard (non-sharp) hypotheses and can even be asymptotically similar in empirically relevant situations. The main focus of the paper is inference on quantile treatment effects but the method applies more broadly. Numerical and empirical examples are provided.</span></span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105672"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139677800","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}
{"title":"Semiparametric approach to estimation of marginal mean effects and marginal quantile effects","authors":"Seong-ho Lee , Yanyuan Ma , Elvezio Ronchetti","doi":"10.1016/j.jeconom.2023.05.002","DOIUrl":"10.1016/j.jeconom.2023.05.002","url":null,"abstract":"<div><div>We consider a semiparametric generalized linear model and study estimation of both marginal mean effects and marginal quantile effects in this model. We propose an approximate maximum likelihood estimator, and rigorously establish the consistency, the asymptotic normality, and the semiparametric efficiency of our method in both the marginal mean effect and the marginal quantile effect estimation. Simulation studies are conducted to illustrate the finite sample performance, and we apply the new tool to analyze a Swiss non-labor income data and discover a new interesting predictor.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105455"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47877548","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}
{"title":"Simultaneous estimation and group identification for network vector autoregressive model with heterogeneous nodes","authors":"Xuening Zhu , Ganggang Xu , Jianqing Fan","doi":"10.1016/j.jeconom.2023.105564","DOIUrl":"10.1016/j.jeconom.2023.105564","url":null,"abstract":"<div><div>Individuals or companies in a large social or financial network often display rather heterogeneous behaviors for various reasons. In this work, we propose a network vector autoregressive model with a latent group structure to model heterogeneous dynamic patterns observed from network nodes, for which group-wise network effects and time-invariant fixed-effects can be naturally incorporated. In our framework, the model parameters and network node memberships can be simultaneously estimated by minimizing a least-squares type objective function. In particular, our theoretical investigation allows the number of latent groups <span><math><mi>G</mi></math></span> to be over-specified when achieving the estimation consistency of the model parameters and group memberships, which significantly improves the robustness of the proposed approach. When <span><math><mi>G</mi></math></span> is correctly specified, valid statistical inference can be made for model parameters based on the asymptotic normality of the estimators. A data-driven criterion is developed to consistently identify the true group number for practical use. Extensive simulation studies and two real data examples are used to demonstrate the effectiveness of the proposed methodology.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105564"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455296","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}
Harold D. Chiang , Yukitoshi Matsushita , Taisuke Otsu
{"title":"Multiway empirical likelihood","authors":"Harold D. Chiang , Yukitoshi Matsushita , Taisuke Otsu","doi":"10.1016/j.jeconom.2024.105861","DOIUrl":"10.1016/j.jeconom.2024.105861","url":null,"abstract":"<div><div>This paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likelihood statistic that converges to a chi-square distribution under the non-degenerate case, where corresponding Hoeffding type decomposition is dominated by linear terms. Our methodology is related to the notion of jackknife empirical likelihood but the leave-out pseudo values are constructed by leaving out columns or rows. We further develop a modified version of our multiway empirical likelihood statistic, which converges to a chi-square distribution regardless of the degeneracy, and discuss its desirable higher-order property in a simplified setup. The proposed methodology is illustrated by several important econometric problems, such as bipartite network, generalized estimating equations, and three-way observations.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105861"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071667","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}
Jiawei Wen , Songshan Yang , Christina Dan Wang , Yifan Jiang , Runze Li
{"title":"Feature-splitting algorithms for ultrahigh dimensional quantile regression","authors":"Jiawei Wen , Songshan Yang , Christina Dan Wang , Yifan Jiang , Runze Li","doi":"10.1016/j.jeconom.2023.01.028","DOIUrl":"10.1016/j.jeconom.2023.01.028","url":null,"abstract":"<div><div>This paper is concerned with computational issues related to penalized quantile regression (PQR) with ultrahigh dimensional predictors. Various algorithms have been developed for PQR, but they become ineffective and/or infeasible in the presence of ultrahigh dimensional predictors due to the storage and scalability limitations. The variable updating schema of the feature-splitting algorithm that directly applies the ordinary alternating direction method of multiplier (ADMM) to ultrahigh dimensional PQR may make the algorithm fail to converge. To tackle this hurdle, we propose an efficient and parallelizable algorithm for ultrahigh dimensional PQR based on the three-block ADMM. The compatibility of the proposed algorithm with parallel computing alleviates the storage and scalability limitations of a single machine in the large-scale data processing. We establish the rate of convergence of the newly proposed algorithm. In addition, Monte Carlo simulations are conducted to compare the finite sample performance of the proposed algorithm with that of other existing algorithms. The numerical comparison implies that the proposed algorithm significantly outperforms the existing ones. We further illustrate the proposed algorithm via an empirical analysis of a real-world data set.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"249 ","pages":"Article 105426"},"PeriodicalIF":9.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47340418","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}