{"title":"Generalized Lee bounds","authors":"Vira Semenova","doi":"10.1016/j.jeconom.2025.106055","DOIUrl":"10.1016/j.jeconom.2025.106055","url":null,"abstract":"<div><div>Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects. This paper generalizes Lee bounds to allow the sign of this effect to be identified by pretreatment covariates, relaxing the standard (unconditional) monotonicity to its conditional analog. Asymptotic theory for generalized Lee bounds is proposed in low-dimensional smooth and high-dimensional sparse designs. The paper also generalizes Lee bounds to accommodate multiple outcomes. Focusing on JobCorps job training program, I first show that unconditional monotonicity is unlikely to hold, and then demonstrate the use of covariates to tighten the bounds.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106055"},"PeriodicalIF":9.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702752","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 comparative analysis of two-way fixed effects estimators in staggered treatment designs","authors":"Jhordano Aguilar-Loyo","doi":"10.1016/j.jeconom.2025.106059","DOIUrl":"10.1016/j.jeconom.2025.106059","url":null,"abstract":"<div><div>Two-way fixed effects (TWFE) is a flexible and widely used approach for estimating treatment effects, and several TWFE estimators have been proposed for staggered treatment designs. This paper focuses on the extended TWFE estimator, introduced by Borusyak et al. (2024) and Wooldridge (2021), and compares it with alternative TWFE estimators. The main contribution is the derivation of an equation that connects the extended TWFE estimator with the difference-in-differences estimator. This equivalence provides a transparent decomposition of the components of the extended TWFE estimand. The results show that the extended TWFE estimand consists of two distinct components: one that captures meaningful comparisons and a residual term. The paper outlines the assumptions required to identify treatment effects. In line with previous literature, the findings show that the extended TWFE estimator relies on a parallel trends assumption that extends across multiple periods. Additionally, illustrative examples compare the TWFE estimators under violations of the parallel trends assumption. The results suggest that no single estimator outperforms the others. The choice of the TWFE estimator depends on the parameter of interest and the characteristics of the empirical application.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106059"},"PeriodicalIF":9.9,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702751","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}
Yin Lu , Chunbai Tao , Di Wang , Gazi Salah Uddin , Libo Wu , Xuening Zhu
{"title":"Robust estimation for dynamic spatial autoregression models with nearly optimal rates","authors":"Yin Lu , Chunbai Tao , Di Wang , Gazi Salah Uddin , Libo Wu , Xuening Zhu","doi":"10.1016/j.jeconom.2025.106065","DOIUrl":"10.1016/j.jeconom.2025.106065","url":null,"abstract":"<div><div>Spatial autoregression has been extensively studied in various applications, yet its robust estimation methods have received limited attention. In this work, we introduce two dynamic spatial autoregression (DSAR) models aimed at capturing temporal trends and depicting the asymmetric network effects of the units. For both DSAR models, we propose a truncated Yule–Walker estimation method, which is tailored to achieve robust estimation in the presence of heavy-tailed data. Additionally, we extend this robust estimation procedure to a constrained estimation framework using the Dantzig selector, enabling the identification of sparse network effects observed in real-world applications. Theoretically, the minimax optimality of proposed estimators is derived under certain conditions on the weighting matrix. Empirical studies, including an analysis of financial contagion in the Chinese stock market and the dynamics of live streaming popularity, demonstrate the practical efficacy of our methods.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106065"},"PeriodicalIF":9.9,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144696381","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":"Sieve estimation of state-varying factor models","authors":"Liangjun Su , Sainan Jin , Xia Wang","doi":"10.1016/j.jeconom.2025.106064","DOIUrl":"10.1016/j.jeconom.2025.106064","url":null,"abstract":"<div><div>In this paper, we propose a sieve approach to estimate state-varying factor models, where the factor loadings vary over specific state variables. Our methodology consists of a two-step estimation procedure for the parameters of interest. In the first step, we achieve preliminary consistent estimates of the factors and factor loadings via nuclear norm regularization (NNR). In the second step, we perform post-NNR iterative least squares estimations for the factors and factor loadings. We establish the asymptotic properties of these estimators. Based on the estimation theory, we propose a test for the null hypothesis of constant factor loadings and examine the asymptotic properties of the test statistic. Monte Carlo simulations demonstrate the favorable performance of the proposed estimation procedure and testing method in finite samples. An application to a U.S. macroeconomic dataset suggests potential state-dependency within the U.S. economy.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106064"},"PeriodicalIF":9.9,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686202","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 general test for functional inequalities","authors":"Jia Li , Zhipeng Liao , Wenyu Zhou","doi":"10.1016/j.jeconom.2025.106063","DOIUrl":"10.1016/j.jeconom.2025.106063","url":null,"abstract":"<div><div>This paper develops a nonparametric test for general functional inequalities that include conditional moment inequalities as a special case. It is shown that the test controls size uniformly over a large class of distributions for observed data, importantly allowing for general forms of time series dependence. New results on uniform growing dimensional Gaussian coupling for general mixingale processes are developed for this purpose, which readily accommodate most applications in economics and finance. The proposed method is applied in a portfolio evaluation context to test for “all-weather” portfolios with uniformly superior conditional Sharpe ratio functions.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106063"},"PeriodicalIF":9.9,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144631495","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":"Layered policy analysis in program evaluation using the marginal treatment effect","authors":"Ismael Mourifié , Yuanyuan Wan","doi":"10.1016/j.jeconom.2025.106060","DOIUrl":"10.1016/j.jeconom.2025.106060","url":null,"abstract":"<div><div>This paper proposes a unified approach to derive sharp bounds on conventional policy parameters when the instrumental variables (IVs) are potentially invalid. Using a <em>vine copula</em> approach, we propose a novel characterization of the identified sets for the marginal treatment effect (MTE) and the policy-relevant treatment effect (PRTE) parameters. Our method has various advantages: First, it explicitly demonstrates how imposing different IV-related assumptions with different credibility levels affects the MTE and PRTE’s identified set. Second, it provides a basis for testing model specifications and hypotheses about various imperfect IV-related assumptions. Third, it provides a tractable way to inform policy choices in the presence of uncertainty of the validity of identifying assumptions. Our approach enlarges the MTE framework’s scope by showing how it can be used to inform policy decisions even when valid instruments are not available.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106060"},"PeriodicalIF":9.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596430","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":"Global identification, estimation and inference of structural impulse response functions in factor models: A unified framework","authors":"Xu Han","doi":"10.1016/j.jeconom.2025.106057","DOIUrl":"10.1016/j.jeconom.2025.106057","url":null,"abstract":"<div><div>This paper develops a theory for the global identification, estimation and inference of impulse response functions (IRFs) in structural factor models (SFMs). We examine the impact of normalization choices on IRF identification and propose to use identification restrictions robust to such choices. A new theorem is established to address IRF identification under both recursive and nonrecursive schemes in SFMs. Moreover, we develop two new estimators for structural IRFs under principal component normalization and establish their asymptotic distributions. We also propose a test for overidentifying restrictions. Simulation results demonstrate the validity of the asymptotic approximations and the favorable finite-sample properties of the overidentification test. To illustrate the flexibility of our methodology, we employ a hybrid identification scheme and analyze the dynamic effects of oil shocks using a US dataset.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106057"},"PeriodicalIF":9.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587828","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":"Bernstein-type inequalities and nonparametric estimation under near-epoch dependence","authors":"Zihao Yuan , Martin Spindler","doi":"10.1016/j.jeconom.2025.106054","DOIUrl":"10.1016/j.jeconom.2025.106054","url":null,"abstract":"<div><div>The main contributions of this paper are twofold. First, we derive Bernstein-type inequalities for irregularly spaced data under near-epoch dependent (NED) conditions and deterministic domain-expanding-infill (DEI) asymptotics. By introducing the concept of “effective dimension” to describe the geometric structure of sampled locations, we illustrate – unlike previous research – that the sharpness of these inequalities is affected by this effective dimension. To our knowledge, ours is the first study to report this phenomenon and show Bernstein-type inequalities under deterministic DEI asymptotics. This work represents a direct generalization of the work of Xu and Lee (2018), thus marking an important contribution to the topic. As a corollary, we derive a Bernstein-type inequality for irregularly spaced <span><math><mi>α</mi></math></span>-mixing random fields under DEI asymptotics. Our second contribution is to apply these inequalities to explore the attainability of optimal convergence rates for the local linear conditional mean estimator under algebraic NED conditions. Our results illustrate how the effective dimension affects assumptions of dependence. This finding refines the results of Jenish (2012) and extends the work of Hansen (2008), Vogt (2012), Chen and Christensen (2015) and Li, Lu, and Linton (2012).</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106054"},"PeriodicalIF":9.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579228","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":"Fast computation of exact confidence intervals for randomized experiments with binary outcomes","authors":"P.M. Aronow , Haoge Chang , Patrick Lopatto","doi":"10.1016/j.jeconom.2025.106056","DOIUrl":"10.1016/j.jeconom.2025.106056","url":null,"abstract":"<div><div>Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid for all sample sizes, as it does not rely on large-sample approximations such as those implied by the central limit theorem. We show that these confidence intervals can be found in <span><math><mrow><mi>O</mi><mrow><mo>(</mo><mi>n</mi><mo>log</mo><mi>n</mi><mo>)</mo></mrow></mrow></math></span> permutation tests in the case of balanced designs, where the treatment and control groups have equal sizes, and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> permutation tests in the general case. Prior to this work, the most efficient known constructions required <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> such tests in the balanced case (Li and Ding, 2016), and <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>4</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span> tests in the general case (Rigdon and Hudgens, 2015). Our results thus facilitate exact inference as a viable option for randomized experiments far larger than those accessible by previous methods. We also generalize our construction to produce confidence intervals for other causal estimands, including the relative risk ratio and odds ratio, yielding similar computational gains.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106056"},"PeriodicalIF":9.9,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579227","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":"High frequency factor analysis with partially observable factors","authors":"Dachuan Chen , Wenqi Lu , Siyu Xie","doi":"10.1016/j.jeconom.2025.106058","DOIUrl":"10.1016/j.jeconom.2025.106058","url":null,"abstract":"<div><div>This paper considers a novel factor structure – <em>Partially Observable Factor Model</em> – where both observable factors and latent factors exist in the model simultaneously. Such factor structure can make sure both interpretability and goodness-of-fit at the same time. Necessary estimation methodologies for this partially observable factor model are developed in this paper for the high frequency data. The proposed estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously.</div><div>When the observable factors are exogenous, we provide the estimation theory for the integrated eigenvalues of the residual covariance matrix, which including the bias-corrected estimator, central limit theorem and asymptotic variance estimator. As a result, the asymptotic normality of the bias-corrected estimator can be applied to test the existence of the latent factors.</div><div>When the observable factors are endogenous, we propose a novel framework of high frequency unsupervised exogenous component learning (HF-UECL), which can help people quantify the contributions of the observable factors into the latent factors. This is the first work on high frequency instrumental variables, and it can be regard as a necessary and non-trivial extension of the Projected-PCA in the world of continuous-time model. Statistical inferences have been established for the loadings of the observable factors onto the latent factors.</div><div>Monte Carlo simulation demonstrates the validity of our estimation methodologies. Empirical study demonstrates that (i) in the exogenous setting, the latent factors significantly exist in the residual process of the high frequency regression; (ii) in the endogenous setting, the correlations between the observable factors and latent factors do exist significantly.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106058"},"PeriodicalIF":9.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144572708","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}