Econometrics and Statistics最新文献_第8页

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

Numerical Methods for Finding A-optimal Designs Analytically 分析求解A最优设计的数值方法

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2022.09.005

Ping-Yang Chen , Ray-Bing Chen , Yu-Shi Chen , Weng Kee Wong

{"title":"Numerical Methods for Finding A-optimal Designs Analytically","authors":"Ping-Yang Chen , Ray-Bing Chen , Yu-Shi Chen , Weng Kee Wong","doi":"10.1016/j.ecosta.2022.09.005","DOIUrl":"https://doi.org/10.1016/j.ecosta.2022.09.005","url":null,"abstract":"<div>The traditional way in statistics to find optimal designs for regression models is an analytical approach. Technical conditions that may be restrictive in practice are sometimes imposed to obtain the analytical results. Even then, the mathematical technique is invariably not amendable to find an optimal design under a different criterion or for the same criterion with a slightly changed model, suggesting that developing flexible and effective algorithms to search for the optimum is very useful. In particular, numerical results from an algorithm can be helpful to find analytical descriptions of optimal designs. As an example, particle swarm optimization has been shown to be quite effective for finding optimal designs for hard design problems and this paper demonstrates how its output can be used to find new analytic <math><mi>A</mi></math>-optimal approximate designs for the Gamma and inverse Gaussian models, each with the inverse link function. The methodology is quite general and may be applied to find analytical <math><mi>A</mi></math>-optimal designs for other models, like the Poisson model with the log link function, or other types of optimal designs.</div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"28 ","pages":"Pages 155-162"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50198657","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

Testing Heteroskedasticity in High-Dimensional Linear Regression 高维线性回归的异方差检验

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.10.003

Akira Shinkyu

引用次数: 0

Bayesian estimation of realized GARCH-type models with application to financial tail risk management 已实现GARCH型模型的贝叶斯估计及其在金融尾部风险管理中的应用

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.03.006

Cathy W.S. Chen , Toshiaki Watanabe , Edward M.H. Lin

{"title":"Bayesian estimation of realized GARCH-type models with application to financial tail risk management","authors":"Cathy W.S. Chen , Toshiaki Watanabe , Edward M.H. Lin","doi":"10.1016/j.ecosta.2021.03.006","DOIUrl":"https://doi.org/10.1016/j.ecosta.2021.03.006","url":null,"abstract":"<div>Advances in the various realized GARCH models have proven effective in taking account of the bias in realized volatility (RV) introduced by microstructure noise and non-trading hours. They have been extended into nonlinear or long-memory patterns, including the realized exponential GARCH (EGARCH), realized heterogeneous autoregressive GARCH (HAR-GARCH), and realized threshold GARCH (TGARCH) models. These models with skew Student’s t-distribution are applied to quantile forecasts such as Value-at-Risk and expected shortfall of financial returns as well as volatility forecasting. Parameter estimation and quantile forecasting are built on Bayesian Markov chain Monte Carlo sampling methods. Backtesting measures are presented for both Value-at-Risk and expected shortfall forecasts and employ two loss functions to assess volatility forecasts. Results taken from the S&P500 in the U.S. market with approximately 5-year out-of-sample periods covering the COVID-19 pandemic period are reported as follows: (1) The realized HAR-GARCH model performs best in respect of violation rates and expected shortfall at the 1% and 5% significance levels. (2) The realized EGARCH model performs best with regard to volatility forecasts.</div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"28 ","pages":"Pages 30-46"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ecosta.2021.03.006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50198789","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: 8

A review of effective age models and associated non- and semiparametric methods 有效年龄模型及其相关的非参数和半参数方法综述

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.12.005

Eric Beutner

引用次数: 2

Change point estimation under a copula-based Markov chain model for binomial time series 基于copula的二项时间序列马尔可夫链模型下的变点估计

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.07.007

Takeshi Emura , Ching-Chieh Lai , Li-Hsien Sun

{"title":"Change point estimation under a copula-based Markov chain model for binomial time series","authors":"Takeshi Emura , Ching-Chieh Lai , Li-Hsien Sun","doi":"10.1016/j.ecosta.2021.07.007","DOIUrl":"https://doi.org/10.1016/j.ecosta.2021.07.007","url":null,"abstract":"<div>Estimation of a change point is a classical statistical problem in sequential analysis and process control. For binomial time series, the existing maximum likelihood estimators (MLEs) for a change point are limited to independent observations. If the independence assumption is violated, the MLEs substantially lose their efficiency, and a likelihood function provides a poor fit to the data. A novel change point estimator is proposed under a copula-based Markov chain model for serially dependent observations. The main novelty is the adaptation of a three-state copula model, consisting of the in-control state, out-of-control state, and transition state. Under this model, a MLE is proposed with the aid of profile likelihood. A parametric bootstrap method is adopted to compute a confidence set for the unknown change point. The simulation studies show that the proposed MLE is more efficient than the existing estimators when serial dependence in observations are specified by the model. The proposed method is illustrated by the jewelry manufacturing data, where the proposed model gives an improved fit.</div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"28 ","pages":"Pages 120-137"},"PeriodicalIF":1.9,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.ecosta.2021.07.007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50198795","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

Partially orthogonal blocked three-level response surface designs 部分正交阻塞三电平响应面设计

IF 1.9

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2021.08.007

Heiko Großmann , Steven G. Gilmour

引用次数: 3

Robust nonparametric multiple changepoint detection for multivariate variability 多变量变异性的鲁棒非参数多变化点检测

Econometrics and Statistics Pub Date : 2023-10-01 DOI: 10.1016/j.ecosta.2023.09.001

Kelly Ramsay, Shojaeddin Chenouri

{"title":"Robust nonparametric multiple changepoint detection for multivariate variability","authors":"Kelly Ramsay, Shojaeddin Chenouri","doi":"10.1016/j.ecosta.2023.09.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.09.001","url":null,"abstract":"Two robust, nonparametric multiple changepoint detection algorithms are introduced: DWBS and MKWP. These algorithms can detect multiple changes in the variability of a sequence of independent multivariate observations, even when the number of changepoints is unknown. The algorithms DWBS and MKWP require minimal distributional assumptions and are robust to outlying observations and heavy tails. The DWBS algorithm uses a local search method based on depth-based ranks and wild binary segmentation. The MKWP algorithm estimates changepoints globally via maximizing a penalized version of the classical Kruskal–Wallis ANOVA test statistic. It is demonstrated that this objective function can be maximized via the well-known PELT algorithm. Under mild, nonparametric assumptions, both of these algorithms are shown to be consistent for the correct number of changepoints and the correct location(s) of the changepoint(s). A data driven thresholding method for multivariate data is introduced, based on the Schwartz information criteria. The robustness and accuracy of the new methods is demonstrated with a simulation study, where the algorithms are compared to several existing algorithms. These new methods can estimate the number of changepoints and their locations accurately when the data are heavy tailed or skewed and the sample size is large. Lastly, the proposed algorithms are applied to a four-dimensional sequence of European daily stock returns.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134936298","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

Vine Copula based Portfolio Level Conditional Risk Measure Forecasting 基于Vine Copula的组合水平条件风险度量预测

Econometrics and Statistics Pub Date : 2023-08-01 DOI: 10.1016/j.ecosta.2023.08.002

Emanuel Sommer, Karoline Bax, Claudia Czado

{"title":"Vine Copula based Portfolio Level Conditional Risk Measure Forecasting","authors":"Emanuel Sommer, Karoline Bax, Claudia Czado","doi":"10.1016/j.ecosta.2023.08.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.08.002","url":null,"abstract":"Accurately estimating risk measures for financial portfolios and validating their robustness is critical for both financial institutions and regulators. However, many existing models operate at the aggregate portfolio level, hence they fail to capture the complex cross-dependencies between portfolio components and particularly provide no methodology to perform a sensitivity analysis on the estimates. To address both aspects, a new approach is presented that uses vine copulas in combination with univariate ARMA-GARCH models for marginal modelling to compute conditional portfolio-level risk measure estimates by simulating portfolio-level forecasts conditioned on a stress factor. A quantile-based approach is then presented to observe the behaviour of risk measures given a particular state of the conditioning asset(s). In an illustrative case study of Spanish equities with different stress factors, the results show that the portfolio is quite robust to a sharp downturn in the American market. At the same time, there is no evidence of this behaviour with respect to the European market. The novel algorithms presented are ready for use through the R package portvine, which is publicly available on CRAN.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136221687","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

A Computationally Efficient Mixture Innovation Model for Time-Varying Parameter Regressions 一个计算效率高的时变参数回归混合创新模型

IF 1.9

Econometrics and Statistics Pub Date : 2023-08-01 DOI: 10.1016/j.ecosta.2023.08.001

Zhongfang He

引用次数: 0