Journal of Econometrics最新文献_第9页

Bootstraps for dynamic panel threshold models 动态面板阈值模型的自举

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-24 DOI: 10.1016/j.jeconom.2025.106153

Woosik Gong , Myung Hwan Seo

引用次数: 0

On generalized CCE estimation 关于广义CCE估计

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2026-01-14 DOI: 10.1016/j.jeconom.2026.106183

Xun Lu , Liangjun Su , Yinglong Ba

{"title":"On generalized CCE estimation","authors":"Xun Lu , Liangjun Su , Yinglong Ba","doi":"10.1016/j.jeconom.2026.106183","DOIUrl":"10.1016/j.jeconom.2026.106183","url":null,"abstract":"<div><div>The widely-used common correlated effects (CCE) estimator, pioneered by Pesaran (2006), is computed using least squares applied to auxiliary regressions where the observed regressors are augmented with cross-sectional averages of the dependent variable and regressors. However, the CCE estimator requires a crucial rank condition and becomes inconsistent when this condition is violated and the factor loadings of the <em>x</em>- and <em>y</em> -equations are correlated, causing an endogeneity issue. This paper proposes a generalized CCE (GCCE) estimator by augmenting the regression with both cross-sectional and time-series averages of the regressors. We argue that the time-series average can serve as “control variables” to address the endogeneity issue. We show that the GCCE and CCE estimators are asymptotically equivalent when the rank condition holds, and the GCCE estimator remains consistent even when the rank condition is violated under our “control variable” condition. Therefore, our GCCE estimator is doubly robust, achieving consistency under either the rank condition or the “control variable” condition. Furthermore, we propose a leave-one-out jackknife method to conduct valid inferences regardless of whether the rank condition holds. Monte Carlo simulations demonstrate excellent performance of our estimators and inference methods in finite samples. We apply our new methods to two datasets to estimate the production function and gravity equation.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106183"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976964","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

Estimation and inference for large-dimensional generalized matrix factor models 大维广义矩阵因子模型的估计与推理

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2026-01-04 DOI: 10.1016/j.jeconom.2025.106179

Xinbing Kong, Tong Zhang

{"title":"Estimation and inference for large-dimensional generalized matrix factor models","authors":"Xinbing Kong, Tong Zhang","doi":"10.1016/j.jeconom.2025.106179","DOIUrl":"10.1016/j.jeconom.2025.106179","url":null,"abstract":"<div><div>This article introduces a nonlinear generalized matrix factor model, moving beyond the linear-Gaussian framework to accommodate a broader class of response models typically handled via logit, probit, Poisson, or Tobit structures. We introduce a novel Lagrange multiplier method carefully tailored to ensure that the penalized likelihood function is locally concave around the true factor and loading parameters. This leads to central limit theorems of the estimated factors and loadings which is nontrivial for nonlinear matrix factor modeling. We establish the convergence rates of the estimated factor and loading matrices for the generalized matrix factor model under general conditions that allow for correlations across samples, rows, and columns. We provide a model selection criterion to determine the numbers of row and column factors. Extensive simulation studies demonstrate the superiority in handling discrete and mixed-type variables of the generalized matrix factor model. An empirical data analysis of the company’s operating performance shows that the generalized matrix factor model does clustering and reconstruction well in the presence of discontinuous entries in the data matrix.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106179"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938557","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

Decomposing informed trading in equity options 股票期权的信息交易分解

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-20 DOI: 10.1016/j.jeconom.2025.106131

Felipe Asencio , Alejandro Bernales , Daniel González , Richard Holowczak , Thanos Verousis

引用次数: 0

Strategic network formation with many agents 战略网络形成与许多代理商

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jeconom.2025.106174

Konrad Menzel

引用次数: 0

Identification in nonlinear dynamic panel models under partial stationarity 部分平稳下非线性动态面板模型的辨识

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2026-01-16 DOI: 10.1016/j.jeconom.2026.106185

Wayne Yuan Gao , Rui Wang

引用次数: 0

Testing for peer effects without specifying the network structure 在不指定网络结构的情况下测试对等效应

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jeconom.2025.106124

Hyunseok Jung , Xiaodong Liu

引用次数: 0

Uncovering mild drift in asset prices with intraday high-frequency data 利用日内高频数据揭示资产价格的温和波动

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jeconom.2025.106177

Shuping Shi , Peter C.B. Phillips

{"title":"Uncovering mild drift in asset prices with intraday high-frequency data","authors":"Shuping Shi , Peter C.B. Phillips","doi":"10.1016/j.jeconom.2025.106177","DOIUrl":"10.1016/j.jeconom.2025.106177","url":null,"abstract":"<div><div>Asset prices are commonly represented as a drift-diffusion process, wherein the drift component denotes the anticipated return of the asset within some time frame, while the diffusion component accommodates random shocks. The drift component has substantial practical significance but accurate estimation is typically challenging and has met with limited success in the existing literature except over large time spans. This paper explores a comprehensive range of drift-diffusion models that include constant, linear, trending, and bursting drift. Conditions are identified under which realized squared drift <span><math><mi>RSD</mi></math></span> is a reliable tool for gauging integrated squared drift when the time span <em>T<sub>n</sub></em> is large enough. The recently introduced drift-robust quarticity estimator <span><math><mi>RiceQ</mi></math></span> is found to retain consistency under twin asymptotics with <em>T<sub>n</sub></em> → ∞ and infill Δ<sub><em>n</em></sub> → 0, subject to some constraints on the divergence rate of <em>T<sub>n</sub></em> across different drift specifications. An inferential method of detecting nonzero drift using <span><math><mi>RSD</mi></math></span> and <span><math><mi>RiceQ</mi></math></span> is proposed and the drift tests are shown to be consistent under different data generating processes with various conditions on <em>T<sub>n</sub></em>. Simulation studies reveal excellent performance of the realized squared drift measure and the drift test in finite samples. The drift test is demonstrated empirically in real-time surveillance of market abnormalities in the Nasdaq Composite Index over two notable sample periods: the dotcom bubble (1996–2003) and the artificial intelligence boom (2016–2024), using intraday data.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106177"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880563","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

A jackknife bias correction for nonlinear network data models with fixed effects 具有固定效应的非线性网络数据模型的折刀偏差校正

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-19 DOI: 10.1016/j.jeconom.2025.106130

David W. Hughes

引用次数: 0

Jump detection in high-frequency order prices 高频订单价格的跳跃检测

IF 4 3区经济学

Journal of Econometrics Pub Date : 2026-01-01 Epub Date: 2025-11-12 DOI: 10.1016/j.jeconom.2025.106133

Markus Bibinger , Nikolaus Hautsch , Alexander Ristig

{"title":"Jump detection in high-frequency order prices","authors":"Markus Bibinger , Nikolaus Hautsch , Alexander Ristig","doi":"10.1016/j.jeconom.2025.106133","DOIUrl":"10.1016/j.jeconom.2025.106133","url":null,"abstract":"<div><div>We propose methods to infer jumps of a semi-martingale, which describes long-term price dynamics, based on discrete, noisy, high-frequency observations. Different to the classical model of additive, centered market microstructure noise, we consider one-sided microstructure noise for order prices in a limit order book.</div><div>We develop methods to estimate, locate and test for jumps using local minima of best ask quotes. We provide a local jump test and show that we can consistently estimate jump sizes and jump times. One main contribution is a global test for jumps. We establish the asymptotic properties and optimality of this test. We derive the asymptotic distribution of a maximum statistic under the null hypothesis of no jumps based on extreme value theory. We prove consistency under the alternative hypothesis. The rate of convergence for local alternatives is determined and shown to be much faster than optimal rates for the standard market microstructure noise model. This allows the identification of smaller jumps. In the process, we establish uniform consistency for spot volatility estimation under one-sided noise. Online jump detection based on the new approach is shown to achieve a speed advantage compared to standard methods applied to mid quotes.</div><div>A simulation study sheds light on the finite-sample implementation and properties of the new approach and draws a comparison to a popular method for market microstructure noise. We showcase how our new approach helps to improve jump detection in an empirical analysis of intra-daily limit order book data.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"253 ","pages":"Article 106133"},"PeriodicalIF":4.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145486066","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