Journal of Econometrics最新文献_第2页

Large Bayesian matrix autoregressions 大贝叶斯矩阵自回归

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-01-30 DOI: 10.1016/j.jeconom.2025.105955

Joshua C.C. Chan, Yaling Qi

引用次数: 0

Bayesian model averaging with non-conjugate priors 非共轭先验贝叶斯平均模型

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-05-07 DOI: 10.1016/j.jeconom.2026.106256

Anastasios E. Tasiopoulos, Efthymios G. Tsionas, Nikolaos D. Vlastakis

引用次数: 0

A dynamic state-space HAR model 动态状态空间HAR模型

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jeconom.2025.106146

Mike Tsionas, Aya Ghalayini, Marwan Izzeldin, Lorenzo Trapani

{"title":"A dynamic state-space HAR model","authors":"Mike Tsionas, Aya Ghalayini, Marwan Izzeldin, Lorenzo Trapani","doi":"10.1016/j.jeconom.2025.106146","DOIUrl":"10.1016/j.jeconom.2025.106146","url":null,"abstract":"<div><div>The Heterogeneous AutoRegressive model for the logs of Realised Volatility (HARL) has established itself as the benchmark specification for modelling and forecasting return volatility, owing to its parsimony and ability to capture the strong persistence typically observed in RV. To address potential concerns such as measurement errors, nonlinearities, and non-spherical residuals, numerous variants of the baseline HARL model have been developed in the literature. This paper contributes to this body of work by proposing a new class of dynamic state-space models with time-varying parameters. The parameter dynamics are assumed to follow an autoregressive process, with or without stochastic volatility, giving rise to two specifications: SHARP and SHARP-SV. Both models are designed to capture the evolving nature of return volatility and are estimated via Bayesian inference using Particle Gibbs sampling. Empirical applications to high-frequency data on SPY, sector ETFs, representative NYSE stocks, and the VIX index demonstrate that our proposed models on average outperform alternative HARL-based specifications in forecasting volatility, particularly at medium- and long-term horizons. An extensive Monte Carlo analysis further illustrates the advantages of our approach in terms of both estimation accuracy and predictive performance.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106146"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523832","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

Identification and estimation in a time-varying endogenous random coefficient panel data model 时变内生随机系数面板数据模型的辨识与估计

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-06-18 DOI: 10.1016/j.jeconom.2026.106272

Ming Li

引用次数: 0

A semiparametric Bayesian estimator of copula density 联结密度的半参数贝叶斯估计

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-08-28 DOI: 10.1016/j.jeconom.2025.106085

Qiaoyu Wang, Ximing Wu, Taining Wang, Subal C. Kumbhakar, Sui Luo

引用次数: 0

Likelihood specification in simultaneous equation models for discrete data 离散数据联立方程模型的似然规范

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-01-21 DOI: 10.1016/j.jeconom.2026.106190

Ivan Jeliazkov, Angela Vossmeyer

引用次数: 0

Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets 工具变量回归中的弱工具稳健子向量推理：子向量拉格朗日乘数检验和子向量Anderson-Rubin置信集的性质

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-05-07 DOI: 10.1016/j.jeconom.2026.106239

Malte Londschien, Peter Bühlmann

{"title":"Weak-instrument-robust subvector inference in instrumental variables regression: A subvector Lagrange multiplier test and properties of subvector Anderson-Rubin confidence sets","authors":"Malte Londschien, Peter Bühlmann","doi":"10.1016/j.jeconom.2026.106239","DOIUrl":"10.1016/j.jeconom.2026.106239","url":null,"abstract":"<div><div>We propose a weak-instrument-robust subvector Lagrange multiplier test for instrumental variables regression. We show that it is asymptotically size-correct under a technical condition or as the number of instruments grows to infinity. This is the first weak-instrument-robust subvector test for instrumental variables regression to recover the degrees of freedom of the commonly used non-weak-instrument-robust Wald test. Additionally, we provide a closed-form solution for subvector confidence sets obtained by inverting the subvector Anderson-Rubin test. We show that they are centered around a k-class estimator. We show that the subvector confidence sets for single coefficients of the causal parameter are jointly bounded if and only if Anderson’s likelihood-ratio test rejects the null hypothesis that the first-stage regression parameter is of reduced rank, that is, that the causal parameter is not identified. Finally, we show that if a confidence set obtained by inverting the Anderson-Rubin test is bounded and nonempty, it is equal to a Wald-based confidence set with a data-dependent confidence level. We explicitly compute this Wald-based confidence set and its confidence level.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106239"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148539711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Macro-prudential policy under asymmetric risks: A Bayesian structural quantile VAR approach 非对称风险下的宏观审慎政策：贝叶斯结构分位数VAR方法

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-04-13 DOI: 10.1016/j.jeconom.2026.106230

Sulkhan Chavleishvili, Robert F. Engle, Stephan Fahr, Manfred Kremer, Frederik Lund-Thomsen, Simone Manganelli, Bernd Schwaab

{"title":"Macro-prudential policy under asymmetric risks: A Bayesian structural quantile VAR approach","authors":"Sulkhan Chavleishvili, Robert F. Engle, Stephan Fahr, Manfred Kremer, Frederik Lund-Thomsen, Simone Manganelli, Bernd Schwaab","doi":"10.1016/j.jeconom.2026.106230","DOIUrl":"10.1016/j.jeconom.2026.106230","url":null,"abstract":"<div><div>Macro-prudential policymakers face a risk-management problem: they must assess medium-term downside risks to the real economy stemming from financial imbalances and the associated threat of future financial crises, while accounting for the cost of policy interventions in terms of foregone upside potential. This trade-off between limiting downside risks and preserving upside growth is inherently asymmetric and depends on the distribution of future macroeconomic outcomes. We formalize this decision problem by defining novel measures of downside risk and upside potential and by stipulating an explicit loss function for macro-prudential policy. To evaluate policy in this setting, we develop and apply a novel Bayesian structural quantile vector autoregressive model that delivers flexible and potentially asymmetric predictive distributions. Using quantile impulse responses, we assess the scope for macro-prudential policy to reduce growth volatility and negative skewness, infer the macro-prudential policy stance, and characterize the circumstances under which macro-prudential interventions are likely to be beneficial.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106230"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523821","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

Structural breaks, model uncertainty and factor selection 结构断裂、模型不确定性与因素选择

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-08-06 DOI: 10.1016/j.jeconom.2025.106067

Siddhartha Chib, Simon C. Smith

引用次数: 0

Bayesian evaluation of mutual fund performance with non-random missing data: Application to the Chinese market 非随机缺失数据下共同基金业绩的贝叶斯评价：在中国市场的应用

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-09-19 DOI: 10.1016/j.jeconom.2025.106098

Tianyi Liu, Qianchao Wang, Yanping Yi, Yonghui Zhang

{"title":"Bayesian evaluation of mutual fund performance with non-random missing data: Application to the Chinese market","authors":"Tianyi Liu, Qianchao Wang, Yanping Yi, Yonghui Zhang","doi":"10.1016/j.jeconom.2025.106098","DOIUrl":"10.1016/j.jeconom.2025.106098","url":null,"abstract":"<div><div>This paper studies mutual fund performance evaluation, where non-random missing data is an important issue. Ignoring sample selection issues can lead to biased parameter estimates and invalid performance evaluation. Therefore, we propose a novel Beta selection model to characterize this non-random missingness mechanism. We can significantly reduce the bias in alpha estimates, by introducing our Beta selection mechanism into the Noise-Reduced Alpha (NRA) model of Harvey and Liu (2018). A computationally attractive Bayesian estimation procedure is also provided. Simulation results show that our proposed method achieves superior finite-sample performance compared to classical fund-by-fund ordinary least squares (OLS) and the baseline NRA model. Finally, we apply our method to Chinese mutual fund data, and find that: (<em>i</em>) our out-of-sample alpha forecasts exhibit greater predictive accuracy across all tested missingness rates; (<em>ii</em>) approximately 18% of funds exhibit statistically significant positive alphas, suggesting market-beating skill; and (<em>iii</em>) portfolios constructed using our method deliver significantly higher returns than those using alternative approaches.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106098"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523833","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