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Stein-Like Shrinkage Estimators for Coefficients of a Single-Equation in Simultaneous Equation Systems Econometrics and Statistics Pub Date : 2024-03-08 A, l, i, , M, e, h, r, a, b, a, n, i
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
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The Asymptotic Equivalence of Ridge and Principal Component Regression with Many Predictors Econometrics and Statistics Pub Date : 2024-03-05 Christine De Mol, Domenico Giannone, Lucrezia Reichlin
The asymptotic properties of ridge regression in large dimension are studied. Two key results are established. First, consistency and rates of convergence for ridge regression are obtained under assumptions which impose different rates of increase in the dimension between the first and the remaining eigenvalues of the population covariance of the predictors. Second, it is proved that under the special
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Comments on “Challenges of cellwise outliers” by Jakob Raymaekers and Peter J. Rousseeuw Econometrics and Statistics Pub Date : 2024-02-24 Claudio Agostinelli
The main aim of robust statistics is the development of methods able to cope with the presence of outliers. A new type of outliers, namely “cellwise”, has garnered considerable attention. The state of the art for dealing with cellwise contamination in different models is presented in Raymaekers and Rousseeuw (0000). Outliers in time series can be treated as cellwise outliers, a further discussion on
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Rejoinder to the comment of Agostinelli Econometrics and Statistics Pub Date : 2024-02-17 Jakob Raymaekers, Peter J. Rousseeuw
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Challenges of cellwise outliers Econometrics and Statistics Pub Date : 2024-02-17 Jakob Raymaekers, Peter J. Rousseeuw
It is well-known that real data often contain outliers. The term outlier usually refers to a case, usually 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
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Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals Econometrics and Statistics Pub Date : 2024-02-17 Guillermo Granados-Garcia, Raquel Prado, Hernando Ombao
One of neuroscience’s goals is to study the interactions between different brain regions during rest and while performing specific cognitive tasks. Multivariate Bayesian autoregressive decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent
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Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process Econometrics and Statistics Pub Date : 2024-02-17 Jinlu Liu, Sara Wade, Natalia Bochkina
Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows researchers to understand gene expression patterns at the single-cell level and uncover the heterogeneous nature of cells. Clustering is an important tool in scRNA-seq analysis to discover groups of cells with similar gene expression patterns and identify potential cell types. Integration of multiple scRNA-seq datasets is a
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Multivariate Hermite polynomials and information matrix tests Econometrics and Statistics Pub Date : 2024-02-06 Dante Amengual, Gabriele Fiorentini, Enrique Sentana
The information matrix test for a normal random vector is shown to coincide with the sum of the moment tests for all third- and fourth-order multivariate Hermite polynomials. The statistic is decomposed as the sum of the marginal information matrix test for a subvector, the conditional information matrix test for the complementary subvector, and a third leftover component. It is also shown that exact
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Inference on Multiple Change Points in High Dimensional Linear Regression Models Econometrics and Statistics Pub Date : 2024-01-17 Hongjin Zhang, Abhishek Kaul
Confidence intervals are constructed for multiple change points in high-dimensional linear regression models. Locally refitted estimators are developed, and their rate of convergence is evaluated. The componentwise rate of estimation obtained is optimal, and the simultaneous rate is the sharpest available in the literature. Limiting distributions of the considered estimates are provided in both vanishing
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Cluster Regularization via a Hierarchical Feature Regression Econometrics and Statistics Pub Date : 2024-01-19 Johann Pfitzinger
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic
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Highly irregular serial correlation tests Econometrics and Statistics Pub Date : 2024-01-09 Dante Amengual, Xinyue Bei, Enrique Sentana
Tests are developed for neglected serial correlation when the information matrix is repeatedly singular under the null hypothesis. Specifically, consideration is given to white noise against a multiplicative seasonal Ar model, and a local-level model against a nesting Ucarimaone. The proposed tests, which involve higher-order derivatives, are asymptotically equivalent to the likelihood ratio test but
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Forecasting corporate credit spreads: regime-switching in LSTM Econometrics and Statistics Pub Date : 2023-12-30 Christina Erlwein-Sayer, Stefanie Grimm, Alexander Pieper, Rümeysa Alsaç
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
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Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity Econometrics and Statistics Pub Date : 2023-12-10 Muhammad Qasim, Kristofer Månsson, Pär Sjölander, B. M. Golam Kibria
A Stein-type control function maximum likelihood (CFML) estimator is suggested for the probit model in the presence of endogeneity. This novel estimator combines the probit maximum likelihood and CFML estimators. The asymptotic distribution and risk function for the new estimator is derived. It is demonstrated that, subject to certain conditions of the shrinkage parameter, the asymptotic risk of the
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Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects Econometrics and Statistics Pub Date : 2023-11-30 David Källberg, Ingeborg Waernbaum
Weighting methods are used in observational studies to adjust for covariate imbalances between treatment and control groups. Entropy balancing (EB) is an alternative to inverse probability weighting with an estimated propensity score. The EB weights are constructed to satisfy balance constraints and optimized towards stability. Large sample properties of EB estimators of the average causal treatment
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Consistent estimation of panel data sample selection models Econometrics and Statistics Pub Date : 2023-11-11 Badi H. Baltagi, Sergi Jiménez-Martín, José M. Labeaga, Majid al Sadoon
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
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A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks Econometrics and Statistics Pub Date : 2023-11-08 Philipp Otto, Osman Doğan, Süleyman Taşpınar
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
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Pooled Bewley Estimator of Long Run Relationships in Dynamic Heterogenous Panels Econometrics and Statistics Pub Date : 2023-11-03 Alexander Chudik, M. Hashem Pesaran, Ron P. Smith
Using a transformation of the autoregressive distributed lag model due to Bewley, a novel pooled Bewley (PB) estimator of long-run coefficients for dynamic panels with heterogeneous short-run dynamics is proposed. The PB estimator is directly comparable to the widely used Pooled Mean Group (PMG) estimator, and is shown to be consistent and asymptotically normal. Monte Carlo simulations show good small
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Robust Clustering with Normal Mixture Models: A Pseudo β-Likelihood Approach Econometrics and Statistics Pub Date : 2023-11-02 Soumya Chakraborty, Ayanendranath Basu, Abhik Ghosh
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
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Testing Heteroskedasticity in High-Dimensional Linear Regression Econometrics and Statistics Pub Date : 2023-10-28 Akira Shinkyu
A new procedure that is based on the residuals of the Lasso is proposed for testing heteroskedasticity in high-dimensional linear regression, where the number of covariates 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
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On the consistency of K-sign depth tests Econometrics and Statistics Pub Date : 2023-10-15 Kevin Leckey, Mirko Jakubzik, Christine H. Müller
The consistency of the so-called K-sign depth tests is considered. These tests are based on the K-sign depth, which originated from the simplicial regression depth, but is easier to compute. The K-sign depth tests use only the signs of residuals and are equivalent to the classical sign test for K=2. However, K-sign depth tests with K≥3 show a much better power than the classical sign tests in simulation
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Robust nonparametric multiple changepoint detection for multivariate variability Econometrics and Statistics Pub Date : 2023-10-05 Kelly Ramsay, Shojaeddin Chenouri
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
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Vine Copula based Portfolio Level Conditional Risk Measure Forecasting Econometrics and Statistics Pub Date : 2023-08-21 Emanuel Sommer, Karoline Bax, Claudia Czado
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
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A Computationally Efficient Mixture Innovation Model for Time-Varying Parameter Regressions Econometrics and Statistics Pub Date : 2023-08-12 Zhongfang He
The mixture innovation (MI) model places a spike-and-slab mixture distribution for the innovations of time-varying regression coefficients and permits flexible time variation patterns while allowing for dynamic shrinkage. Despite its appeal, the standard Bayesian algorithm to block sample the vector of 0/1 mixture indicators at each time t needs to evaluate the model likelihood over all its 2K scenarios
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Estimation of Extreme Risk Measures for Stochastic Volatility Models with Long Memory and Heavy Tails Econometrics and Statistics Pub Date : 2023-07-22 Clémonell Bilayi-Biakana, Gail Ivanoff, Rafał Kulik
Financial data, such as returns on investments, typically exhibit some non-standard features: long memory or long range dependence (LRD) and heavy tails. Therefore, any mathematical model approximating the evolution of asset price should be able to generate these properties. This can be achieved through the use of a long memory stochastic volatility (LMSV) model. The focus is on estimation of Value-at-Risk
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Robust empirical risk minimization via Newton’s method Econometrics and Statistics Pub Date : 2023-07-20
A new variant of Newton’s method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing literature on robust mean estimation for multivariate data. After proving a general theorem about the convergence of successive iterates to a small ball around the
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Macroeconomic Uncertainty and Vector Autoregressions Econometrics and Statistics Pub Date : 2023-07-16
A procedure to estimate measures of macroeconomic uncertainty and compute the effects of uncertainty shocks based on standard VARs is proposed. Uncertainty and its effects are estimated using a single model so to ensure internal consistency. Under suitable assumptions, the procedure is equivalent to using the square of the VAR forecast error as an external instrument in a proxy SVAR. The procedure
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A novel estimation procedure for robust CANDECOMP/PARAFAC model fitting Econometrics and Statistics Pub Date : 2023-07-13
The parameter estimation in CANDECOMP/PARAFAC (CP) is carried out by alternating least squares (ALS) that yields least-squares solutions and provides consistent outcomes. At the same time it has several drawbacks, like sensitivity to the presence of outliers in the data, issues with the computational efficiency in terms of processing time and memory requirements, as well as susceptibility to degeneracy
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A Unified Frequency Domain Cross-Validatory Approach to HAC Standard Error Estimation Econometrics and Statistics Pub Date : 2023-07-03 Zhihao Xu, Clifford M. Hurvich
A unified frequency domain cross-validation (FDCV) method is proposed to obtain a heteroskedasticity and autocorrelation consistent (HAC) standard error. This method enables model/tuning parameter selection across both parametric and nonparametric spectral estimators simultaneously. The candidate class for this approach consists of restricted maximum likelihood-based (REML) autoregressive spectral
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Fluctuation-type monitoring test for explosive behavior Econometrics and Statistics Pub Date : 2023-06-29 Eiji Kurozumi
A fluctuation-type monitoring test for a bubble is proposed. The initial value is dealt with by either OLS or quasi-difference demeaning. The asymptotic property of the test under mildly explosive and local alternatives is investigated. It is shown that the fluctuation-type test has an advantage over the existing methods when the bubble appears mid- to late in the monitoring period or the bubble period
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On the distribution-freeness of a test of angular symmetry based on halfspace depth Econometrics and Statistics Pub Date : 2023-06-22 Alexander Dürre, Davy Paindaveine
The problem of testing the null hypothesis of angular symmetry about a specified location in Rd is considered, with the focus being on a well-known test based on halfspace depth. In the bivariate case d=2, the exact null distribution of the corresponding test statistic is explicitly known and turns out not to depend on the underlying angularly symmetric distribution, so that the test is distribution-free
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Mean group instrumental variable estimation of time-varying large heterogeneous panels with endogenous regressors Econometrics and Statistics Pub Date : 2023-06-22 Yu Bai, Massimiliano Marcellino, George Kapetanios
The large heterogeneous panel data models are extended to the setting where the heterogenous coefficients are changing over time and the regressors are endogenous. Kernel-based non-parametric time-varying parameter instrumental variable mean group (TVP-IV-MG) estimator is proposed for the time-varying cross-sectional mean coefficients. The uniform consistency is shown and the pointwise asymptotic normality
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Instrumental variable quantile regression for clustered data Econometrics and Statistics Pub Date : 2023-06-21 Galina Besstremyannaya, Sergei Golovan
The purpose is to enable inference in case of quantile regression with endogenous covariates and clustered data. It is proven that the instrumental variable quantile regression estimator is consistent where there is correlation of errors within clusters, and an asymptotic distribution for the estimator, which may be used for inference for a given quantile τ, is derived. As regards inference based on
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A Robust Quantitative Risk Screening for Subgroup Pursuit in Clinical Trials Econometrics and Statistics Pub Date : 2023-06-20 Xinzhou Guo, Ruosha Li, Jianjun Zhou, Xuming He
In clinical studies, when to recommend or decide further pursuit of the most promising subgroup that has been observed from an existing trial is a very important question. It is well recognized that the working models in assessing subgroup effects might be misspecified and the observed treatment effect size of the best selected subgroup tends to be too optimistic. Therefore, a careful and robust statistical
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Normality testing after outlier removal Econometrics and Statistics Pub Date : 2023-06-08 Vanessa Berenguer-Rico, Bent Nielsen
The cumulant based normality test after outlier removal is analyzed. It is shown that the standard least squares normalizations can be misleading in this context. The sample cumulants should be standardized according to the truncation imposed at the removal stage and the estimation method being used. New standardizations that lead to chi-squared inference are derived.
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Robust thin-plate splines for multivariate spatial smoothing Econometrics and Statistics Pub Date : 2023-06-08 Ioannis Kalogridis
A novel family of multivariate robust smoothers based on the thin-plate (Sobolev) penalty that is particularly suitable for the analysis of spatial data is proposed. The proposed family of estimators can be expediently computed even in high dimensions, is invariant with respect to rigid transformations of the coordinate axes and can be shown to possess optimal theoretical properties under mild assumptions
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Robust logistic regression for ordered and unordered responses Econometrics and Statistics Pub Date : 2023-06-02 Maria Iannario, Anna Clara Monti
Multinomial regression models and cumulative, adjacent-categories and continuation-ratio models are applied in many fields to analyze unordered or ordered responses with respect to subjects’ profiles. They are typically fitted by maximum likelihood estimators, which unfortunately are sensitive to anomalous data. In order to cope with these data robust M type estimators can be applied. They exploit
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Estimating a discrete distribution subject to random left-truncation with an application to structured finance Econometrics and Statistics Pub Date : 2023-06-02 Jackson P. Lautier, Vladimir Pozdnyakov, Jun Yan
Proper econometric analysis should be informed by data structure. Many forms of financial data are recorded in discrete-time and relate to products of a finite term. If the data is sampled from a financial trust, it will often be further subject to random left-truncation. The estimation of a distribution function from left-truncated data has been extensively addressed, but the case of discrete data
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Robust Fixed-b Inference in the Presence of Time-Varying Volatility Econometrics and Statistics Pub Date : 2023-06-01 Matei Demetrescu, Christoph Hanck, Robinson Kruse-Becher
Time-varying volatility arises in many macroeconomic and financial applications. While “fixed-b” arguments provide refinements in the use of estimators for the asymptotic variance of GMM estimators, the resulting fixed-b distributions of test statistics are not pivotal under time-varying volatility. Three approaches to robustify inference are investigated: (i) wild bootstrapping, (ii) time transformations
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A spline-assisted semiparametric approach to nonparametric measurement error models Econometrics and Statistics Pub Date : 2023-06-01 Fei Jiang, Yanyuan Ma, Raymond J. Carroll
A spline-assisted approach is proposed to handle the measurement error problem in treating the pollution and asthma data. It is well known that the minimax rate of convergence in nonparametric regression function estimation of a random variable measured with error is much slower than the rate in the error free case. A different problem is considered. It is shown that if one is willing to impose a relatively
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On approximate robust confidence distributions Econometrics and Statistics Pub Date : 2023-04-30 Elena Bortolato, Laura Ventura
A confidence distribution is a complete tool for making frequentist inference for a parameter of interest based on an assumed parametric model. Indeed, it provides point estimates, along with confidence intervals, allows to define rejection regions for testing unilateral and bilateral hypotheses, to assign measures of evidence or levels of confidence to prespecified regions of the parameter space,
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Nonparametric estimation of copulas and copula densities by orthogonal projections Econometrics and Statistics Pub Date : 2023-04-29 Yves I. Ngounou Bakam, Denys Pommeret
A nonparametric copula density estimator based on Legendre orthogonal polynomials is proposed. A nonparametric copula estimator is then deduced by integration. Their asymptotic properties are reviewed. Both estimators are based on a sequence of moments that characterize the copulas and that we shall call the copula coefficients. A data-driven method is proposed to select the number of copula coefficients
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Nearest neighbor matching: M-out-of-N bootstrapping without bias correction vs. the naive bootstrap Econometrics and Statistics Pub Date : 2023-04-28 Christopher Walsh, Carsten Jentsch
It is well known that the limiting variance of nearest neighbor matching estimators cannot be consistently estimated by a naive Efron-type bootstrap as the conditional variance of the bootstrap estimator does not generally converge to the correct limit in expectation. In essence this is caused by the fact that the bootstrap sample contains ties with positive probability even when the sample size becomes
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Multivariate outlier explanations using Shapley values and Mahalanobis distances Econometrics and Statistics Pub Date : 2023-04-28 Marcus Mayrhofer, Peter Filzmoser
For the purpose of explaining multivariate outlyingness, it is shown that the squared Mahalanobis distance of an observation can be decomposed into outlyingness contributions originating from single variables. The decomposition is obtained using the Shapley value, a well-known concept from game theory that became popular in the context of Explainable AI. In addition to outlier explanation, this concept
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Robust nonparametric regression: review and practical considerations Econometrics and Statistics Pub Date : 2023-04-25 Matias Salibian-Barrera
Nonparametric regression models offer a way to understand and quantify relationships between variables without having to identify an appropriate family of possible regression functions. Although many estimation methods for these models have been proposed in the literature, most of them can be highly sensitive to the presence of a small proportion of atypical observations in the training set. A review
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Addressing robust estimation in covariate–specific ROC curves Econometrics and Statistics Pub Date : 2023-04-14 Ana M. Bianco, Graciela Boente
Proposals given in the field of ROC curves focusing on their robust aspects and contributions are considered. The motivation is the extended belief that ROC curves are robust. Without being exhaustive, some recent advances in the area are mentioned. The attention is placed on those situations where the presence of covariates related to the diagnostic marker may increase the discriminating power of
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The Influence Function of Graphical Lasso Estimators Econometrics and Statistics Pub Date : 2023-04-06 Gaëtan Louvet, Jakob Raymaekers, Germain Van Bever, Ines Wilms
The precision matrix that encodes conditional linear dependency relations among a set of variables forms an important object of interest in multivariate analysis. Sparse estimation procedures for precision matrices such as the graphical lasso (Glasso) gained popularity as they facilitate interpretability, thereby separating pairs of variables that are conditionally dependent from those that are independent
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Highly Efficient Estimators with High Breakdown Point for Linear Models with Structured Covariance Matrices Econometrics and Statistics Pub Date : 2023-04-02 Hendrik Paul Lopuhaä
A unified approach is provided for a method of estimation of the regression parameter in balanced linear models with a structured covariance matrix that combines a high breakdown point with high asymptotic efficiency at models with multivariate normal errors. Of main interest are linear mixed effects models, but our approach also includes several other standard multivariate models, such as multiple
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Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data Econometrics and Statistics Pub Date : 2023-03-28 Zhuo Qu, Wenlin Dai, Marc G. Genton
A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this proposed distance, the new approach not only can detect correct clusters for sparse multivariate functional data under outlier settings but also can detect those outliers that do not belong to any clusters. Classical distance-based
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Editorial Special issues on the 20th anniversary of the CMStatistics (Computational and Methodological Statistics) Econometrics and Statistics Pub Date : 2023-03-11 Ana Colubi, Erricos Kontoghiorghes
Abstract not available
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Risk-Return Trade-off in International Stock Returns: Skewness and Business Cycles Econometrics and Statistics Pub Date : 2023-03-04 Henri Nyberg, Christos Savva
The fundamental risk-return relation is examined with a flexible regime switching model combining the impact of skewness and business cycle regimes in stock returns. Key methodological and empirical findings point out the need for a highly nonlinear and non-Gaussian model to get a reliable picture on the risk-return relationship. With an international dataset of major countries to global financial
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Bayesian analysis of seasonally cointegrated VAR models Econometrics and Statistics Pub Date : 2023-02-21 Justyna Wróblewska
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
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A nonparametric spatial regression model using partitioning estimators Econometrics and Statistics Pub Date : 2023-02-20 Jose Olmo, Marcos Sanso-Navarro
Conventional spatial regression models are extended by modelling the spatial effects of the exogenous regressor model (SLX) as a functional coefficient. This coefficient is estimated by partitioning the domain of the spatial variable into a set of disjoint intervals and approximating the function using local Taylor expansions. The asymptotic properties of the proposed partitioning estimator are derived
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Analyzing cellwise weighted data Econometrics and Statistics Pub Date : 2023-02-06 Peter J. Rousseeuw
Often the rows (cases, objects) of a dataset have weights. For instance, the weight of a case may reflect the number of times it has been observed, or its reliability. For analyzing such data many rowwise weighted techniques are available, the most well known being the weighted average. But there are also situations where the individual cells (entries) of the data matrix have weights assigned to them
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A Review of Outlier Detection and Robust Estimation Methods for High Dimensional Time Series Data Econometrics and Statistics Pub Date : 2023-02-05 Daniel Peña, Víctor J. Yohai
Diagnostic procedures for finding outliers in high dimensional multivariate time series and robust estimation methods for these data are reviewed. First, methods for searching for outliers assuming that the data have been generated by a Dynamic Factor Model are presented. Then, other existing methods for detecting different types of multivariate time series outliers are analyzed. They include identifying
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A new test for common breaks in heterogeneous panel data models Econometrics and Statistics Pub Date : 2023-02-01
A new test is proposed to detect whether break points are common in heterogeneous panel data models where the time series dimension T could be large relative to cross-section dimension N. The error process is assumed to be cross-sectionally independent. The test is based on the cumulative sum (CUSUM) of ordinary least squares (OLS) residuals. The asymptotic distribution of the detecting statistic is
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Center-outward Rank- and Sign-based VARMA Portmanteau Tests: Chitturi, Hosking, and Li–McLeod revisited Econometrics and Statistics Pub Date : 2023-02-01 Marc Hallin, Hang Liu
The pseudo-Gaussian portmanteau tests of Chitturi, Hosking, and Li and McLeod for VARMA models are revisited from a Le Cam perspective, providing a precise and more rigorous description of the asymptotic behavior of the multivariate portmanteau test statistic, which depends on the dimension d of the observations, the number m of lags involved, and the length n of the observation period. Then, based