Econometrics and Statistics最新文献_第8页

Cluster Regularization via a Hierarchical Feature Regression 通过分层特征回归实现集群正规化

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-19 DOI: 10.1016/j.ecosta.2024.01.003

Johann Pfitzinger

引用次数: 0

Inference on Multiple Change Points in High Dimensional Linear Regression Models 高维线性回归模型中多个变化点的推论

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-17 DOI: 10.1016/j.ecosta.2024.01.002

Hongjin Zhang, Abhishek Kaul

引用次数: 0

Highly irregular serial correlation tests 高度不规则的序列相关性测试

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-09 DOI: 10.1016/j.ecosta.2024.01.001

Dante Amengual, Xinyue Bei, Enrique Sentana

引用次数: 0

Forecasting corporate credit spreads: regime-switching in LSTM 预测公司信贷息差：LSTM 中的制度转换

IF 1.9

Econometrics and Statistics Pub Date : 2023-12-30 DOI: 10.1016/j.ecosta.2023.12.002

Christina Erlwein-Sayer, Stefanie Grimm, Alexander Pieper, Rümeysa Alsaç

{"title":"Forecasting corporate credit spreads: regime-switching in LSTM","authors":"Christina Erlwein-Sayer, Stefanie Grimm, Alexander Pieper, Rümeysa Alsaç","doi":"10.1016/j.ecosta.2023.12.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.12.002","url":null,"abstract":"<p>A long short-term memory model (LSTM) which utilises regime-switching state information as a feature to predict the change of credit spreads is developed. Latent changes in the market are filtered out from observable credit spread time series. These hidden information of regime changes are incorporated into an LSTM, where the state probability is utilised as a feature for one-step ahead predictions of the credit spreads. Firstly, time series from corporate credit spreads are modelled through a Hidden Markov model (HMM) which is based on a discretised Ornstein-Uhlenbeck process. State-related information of the Markov chain, like the jump frequency and state occupation time hidden in the observed spreads are filtered out and adaptive HMM filters are built to estimate probabilities of hidden market states. The performance of the LSTM with regime-switching information is analysed and compared to the accuracy of a pure LSTM without state features. Furthermore, purely utilising the HMM forecast, the prediction of the credit spread is compared to the prediction within the LSTM. Beyond a simulations study, the HMM-LSTM model is calibrated on corporate credit spreads from three European countries between 2004 and 2019. The findings show that the LSTM forecast error is improved when regime information is added, mostly in cases with stronger market fluctuations.</p>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity 存在内生性的概率模型的斯坦因型控制函数最大似然估计器

IF 1.9

Econometrics and Statistics Pub Date : 2023-12-10 DOI: 10.1016/j.ecosta.2023.12.001

Muhammad Qasim, Kristofer Månsson, Pär Sjölander, B. M. Golam Kibria

引用次数: 0

Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects 平均因果效应熵平衡估计的大样本性质

IF 1.9

Econometrics and Statistics Pub Date : 2023-11-30 DOI: 10.1016/j.ecosta.2023.11.004

David Källberg, Ingeborg Waernbaum

引用次数: 0

A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks 动态时空随机波动模型及其在环境风险中的应用

Econometrics and Statistics Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.002

Philipp Otto, Osman Doğan, Süleyman Taşpınar

{"title":"A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks","authors":"Philipp Otto, Osman Doğan, Süleyman Taşpınar","doi":"10.1016/j.ecosta.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.11.002","url":null,"abstract":"A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"5 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135564688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Consistent estimation of panel data sample selection models 面板数据样本选择模型的一致性估计

Econometrics and Statistics Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.003

Sergi Jiménez-Martín, José M. Labeaga, Majid al Sadoon

{"title":"Consistent estimation of panel data sample selection models","authors":"Sergi Jiménez-Martín, José M. Labeaga, Majid al Sadoon","doi":"10.1016/j.ecosta.2023.11.003","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.11.003","url":null,"abstract":"The properties of classical panel data estimators including fixed effect, first-differences, random effects, and generalized method of moments-instrumental variables estimators in both static as well as dynamic panel data models are investigated under sample selection. The correlation of the unobserved errors is shown not to be sufficient for the inconsistency of these estimators. A necessary condition for this to arise is the presence of common (and/or non-independent) non-deterministic covariates in the selection and outcome equations. When both equations do not have covariates in common and independent of each other, the fixed effects, and random effects estimators in static models with exogenous covariates are consistent. Furthermore, the first-differenced generalized method of moments estimator uncorrected for sample selection as well as the instrumental variables estimator uncorrected for sample selection are both consistent for autoregressive models even with endogenous covariates. The same results hold when both equations have no covariates in common but are correlated once we account for such correlation. Under the same circumstances, the system generalized method of moments estimator adding more moments from the levels equation has moderate bias. Alternatively, when both equations have common covariates the appropriate correction method is suggested. Serial correlation of the errors being a key determinant for that choice. The finite sample properties of the proposed estimators are evaluated using a Monte Carlo study. Two empirical illustrations are provided.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"46 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Clustering with Normal Mixture Models: A Pseudo β-Likelihood Approach 正态混合模型的鲁棒聚类:一种伪β似然方法

Econometrics and Statistics Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.10.004

Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh

{"title":"Robust Clustering with Normal Mixture Models: A Pseudo β-Likelihood Approach","authors":"Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh","doi":"10.1016/j.ecosta.2023.10.004","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.10.004","url":null,"abstract":"As in other estimation scenarios, likelihood based estimation in the normal mixture set-up is highly non-robust against model misspecification and presence of outliers (apart from being an ill-posed optimization problem). A robust alternative to the ordinary likelihood approach for this estimation problem is proposed which performs simultaneous estimation and data clustering and leads to subsequent anomaly detection. To invoke robustness, the methodology based on the minimization of the density power divergence (or alternatively, the maximization of the β-likelihood) is utilized under suitable constraints. An iteratively reweighted least squares approach has been followed in order to compute the proposed estimators for the component means (or equivalently cluster centers) and component dispersion matrices which leads to simultaneous data clustering. Some exploratory techniques are also suggested for anomaly detection, a problem of great importance in the domain of statistics and machine learning. The proposed method is validated with simulation studies under different set-ups; it performs competitively or better compared to the popular existing methods like K-medoids, TCLUST, trimmed K-means and MCLUST, especially when the mixture components (i.e., the clusters) share regions with significant overlap or outlying clusters exist with small but non-negligible weights (particularly in higher dimensions). Two real datasets are also used to illustrate the performance of the newly proposed method in comparison with others along with an application in image processing. The proposed method detects the clusters with lower misclassification rates and successfully points out the outlying (anomalous) observations from these datasets.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"49 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135410228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels 动态异质面板长期关系的混合Bewley估计

Econometrics and Statistics Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.001

Alexander Chudik, M. Hashem Pesaran, Ron P. Smith

引用次数: 0