Econometrics and Statistics最新文献_第5页

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

Networks in risk spillovers: A multivariate GARCH perspective 风险溢出中的网络：多元GARCH视角

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2020.12.003

Monica Billio , Massimiliano Caporin , Lorenzo Frattarolo , Loriana Pelizzon

引用次数: 0

Bayesian Analysis of ARCH-M model with a dynamic latent variable 具有动态潜变量的ARCH-M模型的贝叶斯分析

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.10.001

Zefang Song , Xinyuan Song , Yuan Li

引用次数: 1

Multi-objective optimisation of split-plot designs 分割地块设计的多目标优化

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2022.04.001

Matteo Borrotti , Francesco Sambo , Kalliopi Mylona

引用次数: 2

Factor-augmented Bayesian treatment effects models for panel outcomes 面板结果的因子增强贝叶斯治疗效果模型

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2022.04.003

Helga Wagner , Sylvia Frühwirth-Schnatter , Liana Jacobi

引用次数: 0

Estimating the number of common trends in large T and N factor models via canonical correlations analysis 通过典型相关分析估计大T和N因子模型中共同趋势的数量

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.10.001

Massimo Franchi, Iliyan Georgiev, Paolo Paruolo

引用次数: 0

On the consistency of K-sign depth tests 关于k符号深度检验的一致性

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.10.002

Kevin Leckey, Mirko Jakubzik, Christine H. Müller

引用次数: 0

Implicit Copulas: An Overview 隐式Copulas：综述

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.12.002

Michael Stanley Smith

引用次数: 9