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Bayesian analysis of seasonally cointegrated VAR models 季节协整VAR模型的贝叶斯分析
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2023.02.002
Justyna Wróblewska
{"title":"Bayesian analysis of seasonally cointegrated VAR models","authors":"Justyna Wróblewska","doi":"10.1016/j.ecosta.2023.02.002","DOIUrl":"10.1016/j.ecosta.2023.02.002","url":null,"abstract":"<div><div>The aim is to develop a Bayesian seasonally cointegrated model for quarterly data. Relevant prior structure is proposed, and the set of full conditional posterior distributions is derived, enabling us to employ the Gibbs sampler for posterior inference. The identification of cointegrating spaces is obtained by orthonormality restrictions imposed on vectors spanning them. The point estimation of the cointegrating spaces is also discussed. In the presence of a seasonal pattern with one cycle per year, the cointegrating vectors belong to the complex space, which should be taken into account in the identification scheme. The methodology is illustrated by the analysis of money and prices in the Polish economy.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 55-70"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90914870","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
Model Risk of Volatility Models 模型风险波动模型
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2022.06.002
Emese Lazar , Ning Zhang
{"title":"Model Risk of Volatility Models","authors":"Emese Lazar ,&nbsp;Ning Zhang","doi":"10.1016/j.ecosta.2022.06.002","DOIUrl":"10.1016/j.ecosta.2022.06.002","url":null,"abstract":"<div><div>A new model risk measure and estimation methodology based on loss functions is proposed in order to evaluate the accuracy of volatility models. The reliability of the proposed estimation has been verified via simulations and the estimates provide a reasonable fit to the true model risk measure. An empirical analysis based on several assets is undertaken to identify the models most affected by model risk, and concludes that the accuracy of volatility models can be improved by adjusting variance forecasts for model risk. The results indicate that after crisis situations, model risk increases especially for badly fitting volatility models.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 1-22"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75035461","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 liquidity: A statistical theory based on asset staleness 流动性测试：基于资产陈旧性的统计理论
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2022.07.002
Davide Pirino , Alessandro Pollastri , Luca Trapin
{"title":"Testing liquidity: A statistical theory based on asset staleness","authors":"Davide Pirino ,&nbsp;Alessandro Pollastri ,&nbsp;Luca Trapin","doi":"10.1016/j.ecosta.2022.07.002","DOIUrl":"10.1016/j.ecosta.2022.07.002","url":null,"abstract":"<div><div>Using asset staleness as liquidity proxy, two novel test statistics that allow to make inference on the level of liquidity of an asset and on the difference in liquidity between two assets are proposed. The (in-fill) asymptotic properties of the tests are established, and correct procedures to use the tests in multiple testing are provided. A simulation study confirms that the newly defined tests show desirable finite sample properties. Two applications show how the tests can be used for the investor’s asset allocation problem in a high-dimensional setting.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 23-40"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75886874","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
On tail-risk measures for non-integrable heavy-tailed random variables 不可积重尾随机变量的尾部风险测度
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2022.10.003
Laurent Gardes
{"title":"On tail-risk measures for non-integrable heavy-tailed random variables","authors":"Laurent Gardes","doi":"10.1016/j.ecosta.2022.10.003","DOIUrl":"10.1016/j.ecosta.2022.10.003","url":null,"abstract":"<div><div><span>The assessment of risk for heavy-tailed distributions is a crucial question in various fields of application. An important family of risk measures is provided by the class of distortion risk (DR) measures which encompasses the Value-at-Risk and the Tail-Value-at-Risk measures. The Tail-Value-at-Risk is a coherent risk measure (which is not the case for the Value-at-Risk) but it is defined only for integrable quantile functions that is to say for heavy-tailed distributions with a </span>tail index smaller than 1. Moreover, it is a matter of fact that the performance of the empirical estimator is strongly deteriorated when the tail index becomes close to 1. The main contribution is the introduction and the estimation of a new risk measure which is defined for all heavy-tailed distributions and which is tail-equivalent to a coherent DR measure when the tail of the underlying distribution is not too heavy. Its finite sample performance is discussed on a fire claims dataset.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 84-100"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81341236","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 new bootstrap assisted test for checking second order stationarity 一种新的辅助检验二阶平稳性的自举法
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2022.10.004
Lei Jin , Suojin Wang
{"title":"A new bootstrap assisted test for checking second order stationarity","authors":"Lei Jin ,&nbsp;Suojin Wang","doi":"10.1016/j.ecosta.2022.10.004","DOIUrl":"10.1016/j.ecosta.2022.10.004","url":null,"abstract":"<div><div><span>A new computationally driven method is proposed to check if a possibly nonlinear time series is second order stationary or not, which is important in time series modeling. The new test relies on blocks of blocks </span>bootstrap<span> covariance matrix<span><span><span> estimates and Walsh transformations in order to capture the nonlinearity features of time series. The </span>asymptotic normality of the Walsh coefficients and their </span>asymptotic covariance matrix under the null hypothesis are derived for nonlinear processes. In addition, the asymptotic covariance matrix of an increasing dimension is shown to be consistently estimated by a blocks of blocks bootstrap procedure. In the framework of locally stationary nonlinear processes, it is shown that the proposed test is consistent under a sequence of local alternatives. A simulation study is conducted to examine the finite sample performance of the procedure. In many nonlinear time series settings, the proposed test works well while existing methods may have highly inflated type I error rates. The proposed test is applied to an analysis of a financial data set.</span></span></div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 101-119"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82134721","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
IF 2
Econometrics and Statistics Pub Date : 2025-07-01 DOI: 10.1016/j.ecosta.2023.10.003
Akira Shinkyu
{"title":"Testing Heteroskedasticity in High‐Dimensional Linear Regression","authors":"Akira Shinkyu","doi":"10.1016/j.ecosta.2023.10.003","DOIUrl":"10.1016/j.ecosta.2023.10.003","url":null,"abstract":"<div><div>A new procedure that is based on the residuals of the Lasso is proposed for testing heteroskedasticity<span> in high-dimensional linear regression, where the number of covariates<span> can be larger than the sample size. The theoretical analysis demonstrates that the test statistic exhibits asymptotic normality under the null hypothesis of homoskedasticity, and the simulation results reveal that the proposed testing procedure obtains accurate empirical sizes and powers. Finally, the procedure is applied to real economic data.</span></span></div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"35 ","pages":"Pages 120-134"},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136093724","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
Quasi-likelihood analysis for nonlinear stochastic processes 非线性随机过程的拟似然分析
IF 2
Econometrics and Statistics Pub Date : 2025-01-01 DOI: 10.1016/j.ecosta.2022.04.002
Nakahiro Yoshida
{"title":"Quasi-likelihood analysis for nonlinear stochastic processes","authors":"Nakahiro Yoshida","doi":"10.1016/j.ecosta.2022.04.002","DOIUrl":"10.1016/j.ecosta.2022.04.002","url":null,"abstract":"<div><div>A brief overview of the theory of quasi-likelihood analysis (QLA) is given and its usefulness is demonstrated with applications to estimation for a volatility parameter of a semimartingale. A simplified version of the QLA is recalled. The role of non-degeneracy of a key index reflecting identifiability is highlighted. In an application of the QLA, the concept of global jump filters is introduced for precise estimation of the volatility parameter from the data contaminated with jumps.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"33 ","pages":"Pages 246-257"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Covariate balancing for causal inference on categorical and continuous treatments 分类和连续处理因果推理的协变量平衡
IF 2
Econometrics and Statistics Pub Date : 2025-01-01 DOI: 10.1016/j.ecosta.2022.01.007
Seong-ho Lee , Yanyuan Ma , Xavier de Luna
{"title":"Covariate balancing for causal inference on categorical and continuous treatments","authors":"Seong-ho Lee ,&nbsp;Yanyuan Ma ,&nbsp;Xavier de Luna","doi":"10.1016/j.ecosta.2022.01.007","DOIUrl":"10.1016/j.ecosta.2022.01.007","url":null,"abstract":"<div><div>Novel estimators of causal effects for categorical and continuous treatments are proposed by using an optimal covariate balancing strategy for inverse probability weighting. The resulting estimators are shown to be consistent and asymptotically normal for causal contrasts of interest, either when the model explaining the treatment assignment is correctly specified, or when the correct set of bases for the outcome models has been chosen and the assignment model is sufficiently rich. For the categorical treatment case, the estimator attains the semiparametric efficiency bound when all models are correctly specified. For the continuous case, the causal parameter of interest is a function of the treatment dose. The latter is not parametrized and the estimators proposed are shown to have bias and variance of the classical nonparametric rate. Asymptotic results are complemented with simulations illustrating the finite sample properties. A data analysis suggests a nonlinear effect of BMI on self-reported health decline among the elderly.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"33 ","pages":"Pages 304-329"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81971522","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
Inference in mixed causal and noncausal models with generalized Student’s t-distributions 广义学生t分布的混合因果和非因果模型的推理
IF 2
Econometrics and Statistics Pub Date : 2025-01-01 DOI: 10.1016/j.ecosta.2021.11.007
Francesco Giancaterini, Alain Hecq
{"title":"Inference in mixed causal and noncausal models with generalized Student’s t-distributions","authors":"Francesco Giancaterini,&nbsp;Alain Hecq","doi":"10.1016/j.ecosta.2021.11.007","DOIUrl":"10.1016/j.ecosta.2021.11.007","url":null,"abstract":"<div><div>The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student’s <span><math><mi>t</mi></math></span> error process are reviewed. Several known existing methods are typically not applicable in the heavy-tailed framework. To this end, a new approach to make inference on causal and noncausal parameters in finite sample sizes is proposed. It exploits the empirical variance of the generalized Student’s <span><math><mi>t</mi></math></span>, without the existence of population variance. Monte Carlo simulations show a good performance of the new variance construction for fat tail series. Finally, different existing approaches are compared using three empirical applications: the variation of daily COVID-19 deaths in Belgium, the monthly wheat prices, and the monthly inflation rate in Brazil.</div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"33 ","pages":"Pages 1-12"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79363862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Flexible and Robust Particle Tempering for State Space Models 状态空间模型的柔性鲁棒粒子回火
IF 2
Econometrics and Statistics Pub Date : 2025-01-01 DOI: 10.1016/j.ecosta.2022.09.003
David Gunawan , Robert Kohn , Minh Ngoc Tran
{"title":"Flexible and Robust Particle Tempering for State Space Models","authors":"David Gunawan ,&nbsp;Robert Kohn ,&nbsp;Minh Ngoc Tran","doi":"10.1016/j.ecosta.2022.09.003","DOIUrl":"10.1016/j.ecosta.2022.09.003","url":null,"abstract":"<div><div><span><span><span><span>Density tempering (also called density annealing) is a sequential Monte Carlo approach to </span>Bayesian inference<span> for general state models which is an alternative to Markov chain Monte Carlo. When applied to state space models, it moves a collection of parameters and latent states (which are called particles) through a number of stages, with each stage having its own </span></span>target distribution<span>. The particles are initially generated from a distribution that is easy to sample from, e.g. the prior; the target at the final stage is the posterior distribution. Tempering is usually carried out either in batch mode, involving all the data at each stage, or sequentially with observations added at each stage, which is called data tempering. Efficient Markov moves for generating the parameters and states for each stage of particle based density tempering are proposed. This allows the proposed SMC methods to increase (scale up) the number of parameters and states that can be handled. Most current methods use a pseudo-marginal Markov move step with the states “integrated out”, and the parameters generated by a </span></span>random walk<span><span> proposal; although this strategy is general, it can be very inefficient when the states or parameters are high dimensional. By adding batch tempering at each stage, previous methods are extended to make data tempering more robust to outliers and structural changes for models with intractable likelihoods. The performance of the proposed methods is evaluated using univariate </span>stochastic volatility models with outliers and structural breaks, and high dimensional factor stochastic volatility models having many parameters and many latent states.</span></span><span><span><sup>1</sup></span></span></div></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"33 ","pages":"Pages 35-55"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80902225","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
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