P. de Zea Bermudez , J. Miguel Marín , Håvard Rue , Helena Veiga
{"title":"Integrated nested Laplace approximations for threshold stochastic volatility models","authors":"P. de Zea Bermudez , J. Miguel Marín , Håvard Rue , Helena Veiga","doi":"10.1016/j.ecosta.2021.08.006","DOIUrl":"10.1016/j.ecosta.2021.08.006","url":null,"abstract":"<div><p><span>The aim is to implement the integrated nested Laplace approximations<span> (INLA), known to be very fast and efficient, for estimating the parameters of the threshold stochastic volatility (TSV) model. INLA replaces Markov chain Monte Carlo (MCMC) simulations with accurate deterministic approximations. Weakly informative proper priors are used, as well as Penalizing Complexity (PC) priors. The simulation results favor the use of PC priors, specially when the sample size varies from small to moderate. For these sample sizes, PC priors provide more accurate estimates of the model parameters. However, as sample size increases, both types of priors lead to similar estimates of the parameters. The estimation method is applied to six series of returns, including stock market, commodity and cryptocurrency returns, and its performance is assessed, by means of in-sample and out-of-sample approaches; the forecasting of one-day-ahead volatilities is also carried out. The empirical results support that the TSV is the model that generally fits the best to the series of returns and most of the times ranks the first in terms of forecasting one-day-ahead volatility, when compared to the symmetric </span></span>stochastic volatility model.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 15-35"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78259920","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}
Daniel Felix Ahelegbey , Monica Billio , Roberto Casarin
{"title":"Modeling Turning Points in the Global Equity Market","authors":"Daniel Felix Ahelegbey , Monica Billio , Roberto Casarin","doi":"10.1016/j.ecosta.2021.10.004","DOIUrl":"10.1016/j.ecosta.2021.10.004","url":null,"abstract":"<div><p>Turning points in financial markets are often characterized by changes in the direction and/or magnitude of market movements with short-to-long term impacts on investors’ decisions. A Bayesian technique is developed for turning point detection in financial equity markets. The interconnectedness among stock market returns from a piece-wise network vector autoregressive model is derived. The turning points in the global equity market over the past two decades are examined in the empirical application. The level of interconnectedness during the Covid-19 pandemic and the 2008 global financial crisis are compared. Similarities and most central markets responsible for spillover propagation emerged from the analysis.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 60-75"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452306221001192/pdfft?md5=d5813acc3b1da0160286a1921ccc7e7d&pid=1-s2.0-S2452306221001192-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85936966","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}
{"title":"Data segmentation algorithms: Univariate mean change and beyond","authors":"Haeran Cho , Claudia Kirch","doi":"10.1016/j.ecosta.2021.10.008","DOIUrl":"10.1016/j.ecosta.2021.10.008","url":null,"abstract":"<div><p><span>Data segmentation a.k.a. multiple change point analysis has received considerable attention due to its importance in time series analysis<span> and signal processing, with applications in a variety of fields including natural and social sciences, medicine, engineering and finance. The first part reviews the existing literature on the </span></span><em>canonical data segmentation problem</em><span> which aims at detecting and localising multiple change points in the mean of univariate time series. An overview of popular methodologies is provided on their computational complexity and theoretical properties. In particular, the theoretical discussion focuses on the </span><em>separation rate</em> relating to which change points are detectable by a given procedure, and the <em>localisation rate</em><span> quantifying the precision of corresponding change point estimators, and a distinction is made whether a </span><em>homogeneous</em> or <em>multiscale</em><span> viewpoint has been adopted in their derivation. It is further highlighted that the latter viewpoint provides the most general setting for investigating the optimality of data segmentation algorithms.</span></p><p>Arguably, the canonical segmentation problem has been the most popular framework to propose new data segmentation algorithms and study their efficiency in the last decades. The second part of this survey motivates the importance of attaining an in-depth understanding of strengths and weaknesses of methodologies for the change point problem in a simpler, univariate setting, as a stepping stone for the development of methodologies for more complex problems. This point is illustrated with a range of examples showcasing the connections between complex distributional changes and those in the mean. Extensions towards high-dimensional change point problems are also discussed where it is demonstrated that the challenges arising from high dimensionality are orthogonal to those in dealing with multiple change points.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 76-95"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76584108","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}
{"title":"Exact Simulation of Max-Infinitely Divisible Processes","authors":"Peng Zhong , Raphaël Huser , Thomas Opitz","doi":"10.1016/j.ecosta.2022.02.007","DOIUrl":"10.1016/j.ecosta.2022.02.007","url":null,"abstract":"<div><p><span>Max-infinitely divisible (max-id) processes play a central role in extreme-value theory and include the subclass of all max-stable processes. They allow for a constructive representation based on the pointwise maximum of random functions drawn from a </span>Poisson point process<span><span> defined on a suitable function space. Simulating from a max-id process is often difficult due to its complex stochastic structure, while calculating its joint density in high dimensions is often numerically infeasible. Therefore, exact and efficient simulation techniques for max-id processes are useful tools for studying the characteristics of the process and for drawing </span>statistical inferences<span><span>. Inspired by the simulation algorithms for max-stable processes, theory and algorithms to generalize simulation approaches tailored for certain flexible (existing or new) classes of max-id processes are presented. Efficient simulation for a large class of models can be achieved by implementing an adaptive rejection sampling scheme to sidestep a numerical integration step in the algorithm. The results of a simulation study highlight that our simulation algorithm works as expected and is highly accurate and efficient, such that it clearly outperforms customary approximate sampling schemes. As a by-product, new max-id models, which can be represented as pointwise maxima of general location-scale mixtures and possess flexible tail </span>dependence structures capturing a wide range of asymptotic dependence scenarios, are also developed.</span></span></p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 96-109"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85561550","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}
Karim M. Abadir , Walter Distaso , Liudas Giraitis
{"title":"Partially one-sided semiparametric inference for trending persistent and antipersistent processes","authors":"Karim M. Abadir , Walter Distaso , Liudas Giraitis","doi":"10.1016/j.ecosta.2021.12.007","DOIUrl":"10.1016/j.ecosta.2021.12.007","url":null,"abstract":"<div><p>Hypothesis testing in models allowing for trending processes that are possibly nonstationary and non-Gaussian is considered. Using semiparametric estimators, joint hypothesis testing for these processes is developed, taking into account the one-sided nature of typical hypotheses on the persistence parameter in order to gain power. The results are applicable for a wide class of processes and are easy to implement. They are illustrated with an application to the dynamics of GDP.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 1-14"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78683738","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}
{"title":"A model specification test for semiparametric nonignorable missing data modeling","authors":"Cheng Yong Tang","doi":"10.1016/j.ecosta.2021.08.005","DOIUrl":"10.1016/j.ecosta.2021.08.005","url":null,"abstract":"<div><p>The instrumental variable approaches have been demonstrated effective for semiparametrically modeling the propensity function in analyzing data that may be missing not at random. A model specification test is considered for a class of parsimonious semiparametric propensity models. The test is constructed based on assessing an over-identification so as to detect possible incompatibility in the moment conditions when the model and/or instrumental variables are misspecified. Validity of the test under the null hypothesis is established; and its power is studied when the model is misspecified. A data analysis and simulations are presented to demonstrate the effectiveness of our methods.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"30 ","pages":"Pages 124-132"},"PeriodicalIF":1.9,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81308182","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}
Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen
{"title":"Tandem clustering with invariant coordinate selection","authors":"Andreas Alfons, Aurore Archimbaud, Klaus Nordhausen, Anne Ruiz-Gazen","doi":"10.1016/j.ecosta.2024.03.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.03.002","url":null,"abstract":"For multivariate data, tandem clustering is a well-known technique aiming to improve cluster identification through initial dimension reduction. Nevertheless, the usual approach using principal component analysis (PCA) has been criticized for focusing solely on inertia so that the first components do not necessarily retain the structure of interest for clustering. To address this limitation, a new tandem clustering approach based on invariant coordinate selection (ICS) is proposed. By jointly diagonalizing two scatter matrices, ICS is designed to find structure in the data while providing affine invariant components. Certain theoretical results have been previously derived and guarantee that under some elliptical mixture models, the group structure can be highlighted on a subset of the first and/or last components. However, ICS has garnered minimal attention within the context of clustering. Two challenges associated with ICS include choosing the pair of scatter matrices and selecting the components to retain. For effective clustering purposes, it is demonstrated that the best scatter pairs consist of one scatter matrix capturing the within-cluster structure and another capturing the global structure. For the former, local shape or pairwise scatters are of great interest, as is the minimum covariance determinant (MCD) estimator based on a carefully chosen subset size that is smaller than usual. The performance of ICS as a dimension reduction method is evaluated in terms of preserving the cluster structure in the data. In an extensive simulation study and empirical applications with benchmark data sets, various combinations of scatter matrices as well as component selection criteria are compared in situations with and without outliers. Overall, the new approach of tandem clustering with ICS shows promising results and clearly outperforms the PCA-based approach.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140198960","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}
{"title":"Stein-Like Shrinkage Estimators for Coefficients of a Single-Equation in Simultaneous Equation Systems","authors":"A, l, i, , M, e, h, r, a, b, a, n, i","doi":"10.1016/j.ecosta.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.03.001","url":null,"abstract":"Two stein-like shrinkage estimators are introduced to modify the 2SLS and the LIML estimators for coefficients of a single equation in a simultaneous system of equations. The proposed estimators are weighted averages of the 2SLS/LIML estimators and the OLS estimator. The shrinkage weight depends on the Wu-Hausman misspecification test statistic which evaluates the null of exogeneity against the alternative hypothesis of endogeneity. The approximate finite sample bias, mean squared errors, and density functions of the Stein-like shrinkage estimators are obtained using small-disturbance approximations. The dominance conditions of the Stein-like shrinkage estimators over the 2SLS/LIML estimator under the mean squared error and the concentration probability are obtained. The proposed method is further illustrated by simulation studies which demonstrate the good finite sample performance of the method, and is also applied to an empirical application of returns to education.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"44 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140108289","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}
{"title":"Comments on “Challenges of cellwise outliers” by Jakob Raymaekers and Peter J. Rousseeuw","authors":"Claudio Agostinelli","doi":"10.1016/j.ecosta.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.02.003","url":null,"abstract":"","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139948536","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}
{"title":"Challenges of cellwise outliers","authors":"Jakob Raymaekers, Peter J. Rousseeuw","doi":"10.1016/j.ecosta.2024.02.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.02.002","url":null,"abstract":"It is well-known that real data often contain outliers. The term outlier typically refers to a case, typically denoted by a row of the data matrix. In recent times a different type has come into focus, the cellwise outliers. These are suspicious cells (entries) that can occur anywhere in the data matrix. Even a relatively small proportion of outlying cells can contaminate over half the cases, which is a problem for robust methods. This article discusses the challenges posed by cellwise outliers, and some methods developed so far to deal with them. New results are obtained on cellwise breakdown values for location, covariance and regression. A cellwise robust method is proposed for correspondence analysis, with real data illustrations. The paper concludes by formulating some points for debate.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"18 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920276","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}