Journal of Econometrics最新文献

A Bayesian approach to modeling economic growth: Variable selection and cross-sectional dependence 经济增长建模的贝叶斯方法：变量选择和横截面依赖

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-07-03 DOI: 10.1016/j.jeconom.2025.106050

Guohua Feng, Chuan Wang, Subal C. Kumbhakar

{"title":"A Bayesian approach to modeling economic growth: Variable selection and cross-sectional dependence","authors":"Guohua Feng, Chuan Wang, Subal C. Kumbhakar","doi":"10.1016/j.jeconom.2025.106050","DOIUrl":"10.1016/j.jeconom.2025.106050","url":null,"abstract":"<div><div><span>This paper introduces a Bayesian<span> framework designed to address two critical econometric challenges in empirical growth studies: variable selection and cross-sectional dependence. We first propose a growth regression model that incorporates cross-sectional dependence through a multi-factor structure and accounts for variable selection with sparse coefficients. We then introduce a new Bayesian Adaptive LASSO estimator specifically designed for this model, capable of performing variable selection while simultaneously addressing cross-sectional dependence. To validate this framework, we conduct a Monte Carlo study, demonstrating that our Adaptive Bayesian LASSO growth regression model with a factor structure consistently outperforms three benchmark models across all evaluation metrics. Furthermore, we apply our model to a dataset encompassing 55 candidate variables across 71 countries from 1961 to 2019. Our empirical comparison confirms the superiority of our model over the benchmark models. Additionally, our model identifies five global factors and 16 key growth determinants, with robustness checks using </span></span>Principal Component Analysis<span> and instrumental variables confirming the reliability of our findings.</span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106050"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523820","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

Bayesian methods in economics and finance: A unified survey and taxonomy 经济学和金融学中的贝叶斯方法：一个统一的调查和分类

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-06-15 DOI: 10.1016/j.jeconom.2026.106269

Subal C. Kumbhakar, Sushanta K. Mallick

{"title":"Bayesian methods in economics and finance: A unified survey and taxonomy","authors":"Subal C. Kumbhakar, Sushanta K. Mallick","doi":"10.1016/j.jeconom.2026.106269","DOIUrl":"10.1016/j.jeconom.2026.106269","url":null,"abstract":"<div><div>This special issue brings together 21 papers that reflect the expanding scope and methodological evolution of Bayesian econometrics. We introduce a new organizing taxonomy that distinguishes between Traditional Bayesian Approaches - rooted in conjugate priors, standard likelihood-based estimation, and established Markov Chain Monte Carlo (MCMC) techniques - and Contemporary Bayesian Frontiers, characterized by nonparametric methods, variational inference, high-dimensional shrinkage, and integration with machine learning. We further classify contributions across two broad domains covering applications in Economics (focusing on macroeconomics, microeconomics, and climate econometrics) and Finance (focusing on asset pricing, volatility, and bank business models). Across these fields, four cross-cutting themes emerge: (1) high-dimensionality, volatility, and time-varying dynamics; (2) structural identification and model uncertainty; (3) semiparametric and nonparametric flexibility; and (4) data quality, granularity, and novel data structures. This survey synthesizes the methodological and empirical contributions of the special issue, proposes a unifying framework for organizing recent advances in Bayesian econometrics, and identifies unresolved challenges and promising directions for future research.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106269"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523738","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

Partial identification of structural vector autoregressions with non-centred stochastic volatility 非中心随机波动结构向量自回归的部分辨识

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-10-09 DOI: 10.1016/j.jeconom.2025.106107

Helmut Lütkepohl, Fei Shang, Luis Uzeda, Tomasz Woźniak

引用次数: 0

Testing for differences in high-frequency network connectedness from variance decompositions 从方差分解测试高频网络连通性的差异

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2026-04-03 DOI: 10.1016/j.jeconom.2026.106238

Mattia Bevilacqua, Michael Ellington, Rodrigo Hizmeri

引用次数: 0

Bayesian nonparametric inference in bank business models with transient and persistent cost inefficiency 具有短暂和持续成本低效率的银行业务模型中的贝叶斯非参数推理

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-10-10 DOI: 10.1016/j.jeconom.2025.106109

Dimitris Korobilis, Emmanuel C. Mamatzakis, Vasileios Pappas

{"title":"Bayesian nonparametric inference in bank business models with transient and persistent cost inefficiency","authors":"Dimitris Korobilis, Emmanuel C. Mamatzakis, Vasileios Pappas","doi":"10.1016/j.jeconom.2025.106109","DOIUrl":"10.1016/j.jeconom.2025.106109","url":null,"abstract":"<div><div>This paper introduces a novel econometric framework for identifying and modeling bank business models (BBMs), which dynamically evolve in response to changing financial and economic conditions. Building on the stochastic frontier literature, we extend the traditional cost-efficiency models by decomposing inefficiency into persistent and transient components. We propose a Bayesian nonparametric approach that adapts to the data through an infinite mixture model with predictor-dependent clustering, enabling a flexible classification of banks into distinct business models. Our method, based on the Logit Stick-Breaking Process (LSBP), provides a data-driven way to capture the heterogeneity in bank strategies, allowing for dynamic transitions between business models over time. This model offers a significant advancement over existing parametric and kernel-based approaches by combining the scalability of nonparametric methods with efficient computational routines. We apply the model to a dataset of European banks and identify four distinct business model clusters, providing novel insights into the evolution of bank performance and efficiency. Our findings contribute to the growing literature on the identification and measurement of bank business models, offering valuable implications for policy and regulatory frameworks.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106109"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523739","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

Bayesian model comparison for large Bayesian VARs after the COVID-19 pandemic COVID-19大流行后大贝叶斯var的贝叶斯模型比较

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-08-11 DOI: 10.1016/j.jeconom.2025.106072

Joshua C.C. Chan, Xuewen Yu, Wei Zhang

引用次数: 0

Deviance Information Criterion for Bayesian model selection: Theoretical justification and applications 贝叶斯模型选择的偏差信息准则：理论论证与应用

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-03-17 DOI: 10.1016/j.jeconom.2025.105978

Yong Li, Sushanta K. Mallick, Nianling Wang, Jun Yu, Tao Zeng

{"title":"Deviance Information Criterion for Bayesian model selection: Theoretical justification and applications","authors":"Yong Li, Sushanta K. Mallick, Nianling Wang, Jun Yu, Tao Zeng","doi":"10.1016/j.jeconom.2025.105978","DOIUrl":"10.1016/j.jeconom.2025.105978","url":null,"abstract":"<div><div><span><span>This paper provides a theoretical justification for the Deviance Information Criterion (DIC) as a Bayesian model selection tool using MCMC output. Unlike Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), which balance model adequacy against complexity without considering prior information, DIC incorporates priors into this trade-off. The contributions of this paper are two-fold. First, it demonstrates that when a plug-in predictive distribution — obtained by substituting parameter values with their optimal estimates to yield the plug-in estimated sampling distribution — is used under a set of regularity conditions, the DIC serves as an asymptotically unbiased estimator of the expected Kullback–Leibler divergence between the data-generating process and the plug-in predictive distribution. Second, it develops higher-order expansions for DIC and the effective number of parameters, highlighting the effect of the priors. We employ DIC to compare discrete-choice models, stochastic frontier models, and copula models in three empirical applications; the results align with theoretical expectations, showing the utility of DIC as a versatile tool outperforming the traditional model selection criteria. It is found that the logit model is better than the probit model for investigating the marginal effects of parents’ education on children’s completion of high school. Additionally, the stochastic frontier model with an exponential distribution better fits electricity utility data than the </span>normal distribution. Finally, the chosen copula models for S&P index returns exhibit heavy </span>tails and strong tail dependence. By modelling the effect of priors through higher order expansions, we also find the above empirical models outperforming their benchmark counterparts in terms of predictive accuracy.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 105978"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523733","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

Bayesian space–time varying coefficient modeling for climate econometrics: A spatial–temporal Gaussian process approach 气候计量经济学的贝叶斯时空变系数建模：一种时空高斯过程方法

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.2025.106075

Gyuhyeong Goh, Jisang Yu, Myungjin Kim, Jesse Tack

{"title":"Bayesian space–time varying coefficient modeling for climate econometrics: A spatial–temporal Gaussian process approach","authors":"Gyuhyeong Goh, Jisang Yu, Myungjin Kim, Jesse Tack","doi":"10.1016/j.jeconom.2025.106075","DOIUrl":"10.1016/j.jeconom.2025.106075","url":null,"abstract":"<div><div>Quantifying the economic impacts of weather variables has been a key empirical topic in understanding the impacts of climate change. While panel methods, which utilize cross-sectional differences in within-unit over-time changes in weather variables, have been widely used to credibly identify the average effect of interest, there has been a growing interest in understanding the distribution of potentially heterogeneous weather effects. The heterogeneity of the effects across space and time, which can shed light on the differences in the local capacity of adaptation across regions or its evolution over time, provides important insights into developing climate policies. In this paper, we propose a novel Bayesian hierarchical modeling approach to estimate heterogeneous effects of weather variables using panel data. Our proposed approach models space–time varying coefficients via Gaussian process priors. Using the data of the corn yield from the US corn belt, we show that our proposed model outperforms several alternatives. Our findings are consistent with the literature and also provide additional insights into the localized nuances of climate change adaptation.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106075"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523734","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

Bayesian estimation of a semiparametric stochastic frontier model with persistent and transient inefficiencies 具有持续和暂态低效的半参数随机前沿模型的贝叶斯估计

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-08-23 DOI: 10.1016/j.jeconom.2025.106081

Puguang Nie, Christopher F. Parmeter, Valentin Zelenyuk, Xibin Zhang

{"title":"Bayesian estimation of a semiparametric stochastic frontier model with persistent and transient inefficiencies","authors":"Puguang Nie, Christopher F. Parmeter, Valentin Zelenyuk, Xibin Zhang","doi":"10.1016/j.jeconom.2025.106081","DOIUrl":"10.1016/j.jeconom.2025.106081","url":null,"abstract":"<div><div>We propose kernel estimators for the densities of persistent and transient inefficiencies in a generalized panel data stochastic frontier model. This approach relaxes the conventional distributional assumptions imposed on the inefficiencies, thereby increasing robustness. A Bayesian sampling algorithm is developed to estimate all model parameters and unobserved entities, as well as the bandwidths needed to construct the densities of persistent and transient inefficiencies. The advantages of our estimator and sampling algorithm are demonstrated through numerical studies based on pseudo-samples. We find that our estimation approach outperforms conventional Bayesian estimation, providing more accurate estimates of firm-level inefficiencies. We also apply the approach to analyze the cost frontier and related characteristics for a panel of large US commercial banks, and discuss differences relative to the traditional Bayesian framework. Our estimator reveals that the high-density region of transient inefficiency varies over time, whereas the conventional approach does not possess such a feature. Moreover, the contribution of persistent inefficiency to the total inefficiency revealed by our approach is slightly more than half of that suggested by the conventional framework.</div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 106081"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523826","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

Semiparametric Bayesian estimation in an ordinal probit model with application to life satisfaction across countries, age and gender 半参数贝叶斯估计在一个有序概率模型与应用生活满意度跨国家，年龄和性别

IF 4.7 3区经济学

Journal of Econometrics Pub Date : 2026-07-01 Epub Date: 2025-02-13 DOI: 10.1016/j.jeconom.2024.105917

Justin L. Tobias, Timothy N. Bond

{"title":"Semiparametric Bayesian estimation in an ordinal probit model with application to life satisfaction across countries, age and gender","authors":"Justin L. Tobias, Timothy N. Bond","doi":"10.1016/j.jeconom.2024.105917","DOIUrl":"10.1016/j.jeconom.2024.105917","url":null,"abstract":"<div><div>We employ a novel semiparametric Bayesian ordinal probit model to re-examine the relationships between age and life satisfaction<span> (“happiness”) across countries and gender. Within our ordinal choice framework we introduce a new scheme for cutpoint simulation and develop a locally adaptive method for smoothing an otherwise erratic collection of age dummy variable coefficients. We find strong evidence that employment status is deeply intertwined with the reported happiness of men, that employment status is relatively more important for men than it is for women, and that the common use of quadratic models to report ages when happiness is minimized can produce inference that is inaccurate and misleadingly precise. We also find evidence that refines the “U”-shaped pattern between age and happiness that has been reported and/or debated in this literature: For men in most of the European countries we consider, happiness is found to rise sharply before traditional retirement age and then into the beginning of retirement, followed by a flattening or decline in the right-tail of the age distribution. Often-used models that are quadratic in age fail to reproduce this pattern and are frequently at odds with the data.</span></div></div>","PeriodicalId":15629,"journal":{"name":"Journal of Econometrics","volume":"256 ","pages":"Article 105917"},"PeriodicalIF":4.7,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148523819","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