Journal of Econometrics最新文献

Matrix-valued factor model with time-varying main effects 具有时变主效应的矩阵值因子模型

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-26 DOI: 10.1016/j.jeconom.2025.106105

Clifford Lam , Zetai Cen

{"title":"Matrix-valued factor model with time-varying main effects","authors":"Clifford Lam , Zetai Cen","doi":"10.1016/j.jeconom.2025.106105","DOIUrl":"10.1016/j.jeconom.2025.106105","url":null,"abstract":"<div><div>We introduce the matrix-valued time-varying Main Effects Factor Model (MEFM). MEFM is a generalization to the traditional matrix-valued factor model (FM). We give rigorous definitions of MEFM and its identifications, and propose estimators for the time-varying grand mean, row and column main effects, and the row and column factor loading matrices for the common component. Rates of convergence for different estimators are spelt out, with asymptotic normality shown. The core rank estimator for the common component is also proposed, with consistency of the estimators presented. As time series, the row and column main effects <span><math><mrow><mo>{</mo><msub><mrow><mi>α</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>}</mo></mrow></math></span> and <span><math><mrow><mo>{</mo><msub><mrow><mi>β</mi></mrow><mrow><mi>t</mi></mrow></msub><mo>}</mo></mrow></math></span> can be non-stationary without affecting the estimation accuracy of our estimators. The number of main effects factors contributing to row or column main effects is also consistently estimated by our proposed estimators. We propose a test for testing if FM is sufficient against the alternative that MEFM is necessary, and demonstrate the power of such a test in various simulation settings. We also demonstrate numerically the accuracy of our estimators in extended simulation experiments. A set of NYC Taxi traffic data is analyzed and our test suggests that MEFM is indeed necessary for analyzing the data against a traditional FM.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106105"},"PeriodicalIF":4.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155354","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}

引用次数: 0

Inference on model parameters with many L-moments 具有多个l矩的模型参数推理

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-23 DOI: 10.1016/j.jeconom.2025.106101

Luis A.F. Alvarez , Chang Chiann , Pedro A. Morettin

{"title":"Inference on model parameters with many L-moments","authors":"Luis A.F. Alvarez , Chang Chiann , Pedro A. Morettin","doi":"10.1016/j.jeconom.2025.106101","DOIUrl":"10.1016/j.jeconom.2025.106101","url":null,"abstract":"<div><div>This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matching sample L-moments is known to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments used in estimation remains <em>ad-hoc</em>, though: researchers typically set the number of L-moments equal to the number of parameters, which is inefficient in larger samples. In this paper, we show that, by properly choosing the number of L-moments and weighting these accordingly, one is able to construct an estimator that outperforms MLE in finite samples, and yet retains asymptotic efficiency. We do so by introducing a generalised method of L-moments estimator and deriving its properties in an asymptotic framework where the number of L-moments varies with sample size. We then propose methods to automatically select the number of L-moments in a sample. Monte Carlo evidence shows our approach can provide mean-squared-error improvements over MLE in smaller samples, whilst working as well as it in larger samples. We consider extensions of our approach to the estimation of conditional models and a class semiparametric models. We apply the latter to study expenditure patterns in a ridesharing platform in Brazil.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106101"},"PeriodicalIF":4.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118398","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}

引用次数: 0

Nonparametric regression under cluster sampling 聚类抽样下的非参数回归

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-22 DOI: 10.1016/j.jeconom.2025.106102

Yuya Shimizu

引用次数: 0

Structural periodic vector autoregressions 结构周期向量自回归

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-18 DOI: 10.1016/j.jeconom.2025.106099

Daniel Dzikowski, Carsten Jentsch

{"title":"Structural periodic vector autoregressions","authors":"Daniel Dzikowski, Carsten Jentsch","doi":"10.1016/j.jeconom.2025.106099","DOIUrl":"10.1016/j.jeconom.2025.106099","url":null,"abstract":"<div><div>While seasonality inherent to raw macroeconomic data is commonly removed by seasonal adjustment techniques before it is used for structural inference, this may distort valuable information in the data. As an alternative method to commonly used structural vector autoregressions (SVARs) for seasonally adjusted data, we propose to model potential periodicity in seasonally unadjusted (raw) data directly by structural periodic vector autoregressions (SPVARs). This approach does not only allow for periodically time-varying intercepts, but also for periodic autoregressive parameters and innovations variances. As this larger flexibility leads to an increased number of parameters, we propose linearly constrained estimation techniques. Moreover, based on SPVARs, we provide two novel identification schemes and propose a general framework for impulse response analyses that allows for direct consideration of seasonal patterns. We provide asymptotic theory for SPVAR estimators and impulse responses under flexible linear restrictions and introduce a test for seasonality in impulse responses. For the construction of confidence intervals, we discuss several residual-based (seasonal) bootstrap methods and prove their bootstrap consistency under different assumptions. A real data application shows that useful information about the periodic structure in the data may be lost when relying on common seasonal adjustment methods.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106099"},"PeriodicalIF":4.0,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096098","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}

引用次数: 0

Misspecification-robust bootstrap t-test for irrelevant factor in linear stochastic discount factor models 线性随机折现因子模型中不相关因子的错误规格-稳健自举t检验

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-12 DOI: 10.1016/j.jeconom.2025.106097

Antoine A. Djogbenou , Ulrich Hounyo

引用次数: 0

On-line detection of changes in the shape of intraday volatility curves 日内波动率曲线形状变化的在线检测

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-09-08 DOI: 10.1016/j.jeconom.2025.106089

Torben G. Andersen , Yingwen Tan , Viktor Todorov , Zhiyuan Zhang

引用次数: 0

High dimensional factor analysis with weak factors 含弱因子的高维因子分析

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-08-30 DOI: 10.1016/j.jeconom.2025.106086

Jungjun Choi , Ming Yuan

{"title":"High dimensional factor analysis with weak factors","authors":"Jungjun Choi , Ming Yuan","doi":"10.1016/j.jeconom.2025.106086","DOIUrl":"10.1016/j.jeconom.2025.106086","url":null,"abstract":"<div><div>This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading (<span><math><msup><mrow><mi>Λ</mi></mrow><mrow><mn>0</mn></mrow></msup></math></span>) scales sublinearly in the number <span><math><mi>N</mi></math></span> of cross-section units, i.e., <span><math><mrow><msup><mrow><mi>Λ</mi></mrow><mrow><mn>0</mn><mo>⊤</mo></mrow></msup><msup><mrow><mi>Λ</mi></mrow><mrow><mn>0</mn></mrow></msup><mo>/</mo><msup><mrow><mi>N</mi></mrow><mrow><mi>α</mi></mrow></msup></mrow></math></span> is positive definite in the limit for some <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>. While the consistency and asymptotic normality of these estimates are by now well known when the factors are strong, i.e., <span><math><mrow><mi>α</mi><mo>=</mo><mn>1</mn></mrow></math></span>, the statistical properties for weak factors remain less explored. Here, we show that the PC estimator maintains consistency and asymptotic normality for any <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>, provided suitable conditions regarding the dependence structure in the noise are met. This complements earlier result by Onatski (2012) that the PC estimator is inconsistent when <span><math><mrow><mi>α</mi><mo>=</mo><mn>0</mn></mrow></math></span>, and the more recent work by Bai and Ng (2023) who established the asymptotic normality of the PC estimator when <span><math><mrow><mi>α</mi><mo>∈</mo><mrow><mo>(</mo><mn>1</mn><mo>/</mo><mn>2</mn><mo>,</mo><mn>1</mn><mo>)</mo></mrow></mrow></math></span>. Our proof strategy integrates the traditional eigendecomposition-based approach for factor models with leave-one-out analysis similar in spirit to those used in matrix completion and other settings. This combination allows us to deal with factors weaker than the former and at the same time relax the incoherence and independence assumptions often associated with the later.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"252 ","pages":"Article 106086"},"PeriodicalIF":4.0,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144917858","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}

引用次数: 0

On regression-adjusted imputation estimators of average treatment effects 平均处理效果的回归校正归因估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-08-26 DOI: 10.1016/j.jeconom.2025.106080

Zhexiao Lin , Fang Han

引用次数: 0

Support vector decision making 支持向量决策

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-08-26 DOI: 10.1016/j.jeconom.2025.106087

Yixiao Sun

引用次数: 0

Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure 具有多重结构断裂和多因素误差结构的空间面板数据模型的收缩估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2025-08-22 DOI: 10.1016/j.jeconom.2025.106082

Siqi Dai , Yongmiao Hong , Haiqi Li , Chaowen Zheng

{"title":"Shrinkage estimation of spatial panel data models with multiple structural breaks and a multifactor error structure","authors":"Siqi Dai , Yongmiao Hong , Haiqi Li , Chaowen Zheng","doi":"10.1016/j.jeconom.2025.106082","DOIUrl":"10.1016/j.jeconom.2025.106082","url":null,"abstract":"<div><div>This study investigates spatial panel data models with a multifactor error structure and multiple structural breaks occurring in the coefficients of both spatial lagged and explanatory variables. While extensive research has addressed cross-sectional dependence in panel data, including approaches that integrate spatial and factor structures within a single framework, few studies account for time-varying model parameters and achieving consistent estimation remains a significant challenge. To address the dual challenges of endogeneity and time heterogeneity, we propose a novel penalized generalized method of moments estimation with common correlated effects (PGMM-CCEX). Specifically, this method addresses the endogeneity issue by utilizing the cross-sectional averages of regressors as factor proxies when constructing the internal instrumental variables, while employing adaptive group fused Lasso to detect multiple structural breaks. The PGMM-CCEX method consistently estimates both the number of breaks and their locations. Furthermore, the post-PGMM-CCEX regime-specific coefficient estimates are consistent and asymptotically follow a normal distribution. Notably, the method remains valid even when factor loadings vary over time, whether synchronously or asynchronously with the parameters of interest. Monte Carlo simulations confirm the satisfactory finite-sample performance of the proposed PGMM-CCEX method. Finally, we apply our method to analyze cross-country economic growth across 106 countries from 1970 to 2019, revealing the time-varying influence of key economic factors on growth dynamics.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"251 ","pages":"Article 106082"},"PeriodicalIF":4.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886017","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}

引用次数: 0