Search

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To capture the conditional correlations between bivariate financial responses at different quantile levels, this paper considers the Bayesian quantile regression for bivariate vector autoregressive models. With the well known location-scale mixture representation for the asymmetric Laplace distribution, a working likelihood is obtained. By introducing the latent variables, a new Gibbs sampling algorithm is developed for drawing the posterior samples for the parameters and latent variables. The numerical simulation implies that the Gibbs sampling algorithm converges fast and the Bayesian quantile estimators perform well. Finally, a real example is given to discuss the relationship between the Canadian dollar to U.S. dollar exchange rate and long term annual interest rate of Canada. [ABSTRACT FROM AUTHOR]

Summary: Cointegration has shown useful for modeling non‐stationary data with long‐run equilibrium relationships among variables, with applications in many fields such as econometrics, climate research and biology. However, the analyses of vector autoregressive models are becoming more difficult as data sets of higher dimensions are becoming available, in particular because the number of parameters is quadratic in the number of variables. This leads to lack of statistical robustness, and regularisation methods are paramount for obtaining valid estimates. In the last decade, many papers have appeared suggesting different penalisation approaches to the inference problem. Here, we make a comprehensive review of different penalisation methods adapted to the specific structure of vector cointegrated models suggested in the literature, with relevant references to software packages. The methods are evaluated and compared according to a range of error measures in a simulation study, considering combinations of low and high dimension of the system and small and large sample sizes. [ABSTRACT FROM AUTHOR]

As life expectancy continues to increase, there is a growing concern that the same pace of health improvement may not follow. An ageing population spending more years in disability and long-term sickness can place a significant financial burden on society. It is therefore crucial for governments to accurately forecast not just life expectancy but also healthy life expectancy. In particular, examining the highest healthy life expectancy can provide valuable information, as it represents the current best experience worldwide. Although there have been numerous studies on forecasting life expectancy, relatively few authors have investigated the forecasting of healthy life expectancy, often due to health data limitations. In this paper, we propose a Bayesian approach to co-model the highest healthy life expectancy and the highest life expectancy. The resulting forecasts would offer useful insights for governments in shaping healthcare and social policies to improve the wellbeing of seniors and retirees. [ABSTRACT FROM AUTHOR]

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros. [ABSTRACT FROM AUTHOR]

In many sectors of the entertainment industry, a few employees attract the public spotlight when performing the core service. For example, in professional team sports, a team of players competes in games, and in TV shows, a cast of artists acts in different episodes. These employees, coined "spotlight personnel," are an essential but expensive element of ongoing service delivery. Despite their importance and cost, very little is known about how changes in spotlight personnel affect service performance and demand. To address this gap, this article uses unique data on professional German soccer teams, tracking the quantity (number of players) and quality (average transfer price) of spotlight personnel hiring (incoming transfers) and turnover (outgoing transfers), objective service performance (winning percentage), and demand (ticket sales) across four decades, using both traditional and novel time series methods. The results show that service performance and demand are primarily affected by spotlight personnel hiring rather than by turnover. Hiring quantity decreases service performance yet increases demand, whereas hiring quality benefits both service performance and demand. The analysis further uncovers that these effects are subject to dynamic interactions and nonlinearities. Investment scenarios showcase how understanding these effects can substantially improve managerial decision making. [ABSTRACT FROM AUTHOR]

This study proposes a new approach to reduce the information redundancy at Air Pollution Monitoring Networks (APMNs) and costs required for monitoring them. Proposed approach is based on Vector Autoregressive (VAR) model which describes the relationship between multivariate time series and consists of three main steps: In the first step, VAR model between two or more than two time series consisting of air pollutant observations is estimated. This step is repeated as the number of monitoring stations (n) under study and thus, n parameter vectors are obtained. In the second step, parameters vectors are divided into homogenous groups by using clustering analysis. The objective of this step is to identify the similar monitoring stations in terms of the relationship. Last step is to calculate the reduced information redundancy and the monitoring costs. To evaluate the efficiency of proposed approach, data sets consisting of PM10 and SO2 time series obtained from 116 APMNs at Türkiye are used. Fuzzy K-Medoids (FKM) as clustering method Xie-Beni (XB) index as cluster validity index are preferred. Experimental results showed that information redundancy and monitoring cost in PM10 and SO2 stations can reduced at the rate of 63.36 by following proposed approach. [ABSTRACT FROM AUTHOR]

Knowledge of causal relationships is fundamental for understanding the dynamic mechanisms of ecological systems. To detect such relationships from multivariate time series, Granger causality, an idea first developed in econometrics, has been formulated in terms of vector autoregressive (VAR) models. Granger causality for count time series, often seen in ecology, has rarely been explored, and this may be due to the difficulty in estimating autoregressive models on multivariate count time series. The present research investigates the appropriateness of VAR-based Granger causality for ecological count time series by conducting a simulation study using several systems of different numbers of variables and time series lengths. VAR-based Granger causality for count time series (DVAR) seems to be estimated efficiently even for two counts in long time series. For all the studied time series lengths, DVAR for more than eight counts matches the Granger causality effects obtained by VAR on the continuous-valued time series well. The positive results, also in two ecological time series, suggest the use of VAR-based Granger causality for assessing causal relationships in real-world count time series even with few distinct integer values or many zeros.

Does internet search intensity (ISI) for a company's product affect the company's stock returns? How about the ISI for its rival product? How does ISI for the company's product affect the ISI for the rival and vice versa? How is the evolution and persistence of these effects over time? To answer these questions, this study examines three pairs of rival products: Apple's iPhone versus Samsung Galaxy, Intel versus AMD processors, and Netflix versus Hulu. Guided by psychological and marketing theories, Vector Autoregressive models were constructed to estimate the effects of ISI for the rival products (1) on the stock returns, and (2) on each other. Results showed that (1) ISI for the products significantly impacts the stock returns, and (2) the effects of ISI for one product on the other are not only significant but also asymmetrical. This multidisciplinary study integrates marketing analytics and financial phenomena and thus contributes to multiple research streams. It also finds that companies' stock returns can be affected by the consumer online information search, which is responsive to marketing activities. Thus, marketers stand to benefit from leveraging this study's findings to elevate their role in enhancing one of their companies' most critical performance metrics—stock returns. [ABSTRACT FROM AUTHOR]

We develop an LM test for Granger causality in high-dimensional (HD) vector autoregressive (VAR) models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in HD compared to standard low-dimensional VARs. [ABSTRACT FROM AUTHOR]

In polder-type land, water dynamics are heavily influenced by the artificial maintenance of water levels. Polders are low-lying areas of land that have been reclaimed from the sea or from freshwater bodies and are protected from flooding by dikes or other types of flood-protection structures. The water regime in polders is typically managed using a system of canals, pumps, and sluices to control the flow of water in and out of the area. In this study, the temporal changes in water salinity in the polder-type agricultural floodplain within the Neretva River Delta (NRD), Croatia, were analyzed by applying multivariate statistics and forecast modelling. The main aim of the study was to test the model that can be used in practice to forecast, primarily, water suitability for irrigation in a coastal low-lying agricultural catchment. The specific aim of this study was to use hydrochemistry data series to explain processes in water salinity dynamics and to test the model which may provide accurate salinity prediction, or finally select the conditions in which the model can be applied. We considered the accuracy of the model, and it was validated using independent data sets. To describe different patterns of chemical changes in different water classes due to their complex hydrological connectivity, multivariate statistics (PCA) were coupled with time-series analysis and Vector Autoregression (VAR) model forecasting. The multivariate statistics applied here did not indicate a clear connection between water salinity of the surface-water bodies and groundwater. The lack of correlation lies in the complex hydrological dynamics and interconnectivity of the water bodies highly affected by the artificial maintenance of the groundwater level within the polder area, as well as interventions in the temporal release of freshwater into the drainage canal network. Not all individual water classes contributed equally to the dominant patterns of ionic species identified by PCA. Apparently, land use and agricultural management practices in the different polders lead to uneven water chemistry and the predominant contributions of specific ions, especially nutrients. After applying the Granger causality test to reveal the causal information and explain hidden relationships among the variables, only two surface-water and two groundwater monitoring locations displayed a strong causal relationship between water electrical conductivity (ECw) as an effect and sea level as a possible cause. The developed models can be used to evaluate and emphasize the unique characteristics and phenomena of low-lying land and to communicate their importance and influence to management authorities and agricultural producers in managing and planning irrigation management in the wider Mediterranean area. [ABSTRACT FROM AUTHOR]

Purpose – This article examines whether deviations from fundamental value or closed-end country fund's discounts or premiums forecast future share price returns or net asset returns. Design/methodology/approach – The main empirical (econometric) tool is a vector autoregressive (VAR) model. The authors model share price returns and net asset returns as a function of their lagged values, the discounts or premiums, and a control variable for local market returns. The authors also conduct Dickey Fuller and Granger causality tests as well as impulse response functions. Findings – It was found that deviations from fundamental value do predict share price returns. This predictability is contrary to weak-form market efficiency. Premiums or discounts predict net asset returns but weakly. Originality/value – The findings point to the idea that the closed-end fund market is somewhat predictable and inefficient (in its weak form) since the market appears to be able to anticipate a fund's future returns using information contained in the premiums (or discounts). In particular, the market has the ability to anticipate future behaviour because growing premiums forecast declining share price returns for one or two periods ahead.

Investigating the relationship between Gross Domestic Product and unemployment is one of the most important challenges in macroeconomics. In this paper, we compare French and German economies in terms of the dynamic linkage between these variables. In particular, we use an empirical methodology to investigate how much the relationship between Gross Domestic Product and unemployment growth rates are dynamically different in the two major European economies over the period 2003–2019. To this aim, a Vector Autoregressive model is specified for each country to jointly model the growth rate of the two variables. Then a new statistical test is proposed to assess the distance between the two estimated models. Results indicate that the dynamic linkage between Gross Domestic Product and unemployment is very similar in the two countries. This empirical evidence does not imply identical product and labor markets in France and Germany, but it ensures that in these markets there are common dynamics. This could favor the process of economic convergence between the two countries. [ABSTRACT FROM AUTHOR]

In polder-type land, water dynamics are heavily influenced by the artificial maintenance of water levels. Polders are low-lying areas of land that have been reclaimed from the sea or from freshwater bodies and are protected from flooding by dikes or other types of flood-protection structures. The water regime in polders is typically managed using a system of canals, pumps, and sluices to control the flow of water in and out of the area. In this study, the temporal changes in water salinity in the polder-type agricultural floodplain within the Neretva River Delta (NRD), Croatia, were analyzed by applying multivariate statistics and forecast modelling. The main aim of the study was to test the model that can be used in practice to forecast, primarily, water suitability for irrigation in a coastal low-lying agricultural catchment. The specific aim of this study was to use hydrochemistry data series to explain processes in water salinity dynamics and to test the model which may provide accurate salinity prediction, or finally select the conditions in which the model can be applied. We considered the accuracy of the model, and it was validated using independent data sets. To describe different patterns of chemical changes in different water classes due to their complex hydrological connectivity, multivariate statistics (PCA) were coupled with time-series analysis and Vector Autoregression (VAR) model forecasting. The multivariate statistics applied here did not indicate a clear connection between water salinity of the surface-water bodies and groundwater. The lack of correlation lies in the complex hydrological dynamics and interconnectivity of the water bodies highly affected by the artificial maintenance of the groundwater level within the polder area, as well as interventions in the temporal release of freshwater into the drainage canal network. Not all individual water classes contributed equally to the dominant patterns of ionic species identified by PCA. Apparently, land use and agricultural management practices in the different polders lead to uneven water chemistry and the predominant contributions of specific ions, especially nutrients. After applying the Granger causality test to reveal the causal information and explain hidden relationships among the variables, only two surface-water and two groundwater monitoring locations displayed a strong causal relationship between water electrical conductivity (ECw) as an effect and sea level as a possible cause. The developed models can be used to evaluate and emphasize the unique characteristics and phenomena of low-lying land and to communicate their importance and influence to management authorities and agricultural producers in managing and planning irrigation management in the wider Mediterranean area.

A multivariate vector autoregressive model is used to construct the distribution of the impulse-response functions of macroeconomics shocks. In particular, the paper studies the distribution of the short-, medium-, and long-term effects after a shock. Structural and reduced form quantile vector autoregressive models are developed where heterogeneity in conditional effects can be evaluated through multivariate quantile processes. The distribution of the responses can then be obtained by using uniformly distributed random vectors. An empirical example of exchange rate pass-through in Argentina is presented. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Public Finance Studies / Maliye Çalismalari Dergisi is the property of Journal of Public Finance Studies / Maliye Calismalari Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Previous‐30 treatments of multivariate non‐causal time series have assumed stationarity. In this article, we consider integrated processes in a non‐causal setting. We generalize the Johansen–Granger representation for causal vector autoregressive (VAR) models to allow for dependence on future errors and discuss how the parameters can be estimated. The asymptotic distribution of the trace statistic is also considered. Some Monte Carlo simulations are presented. [ABSTRACT FROM AUTHOR]

Daily banking practice suggests that there may be contagion effects between portfolios, a fact that has been explicitly recognized through current regulation. This paper describes a model that distinguishes between delinquency in each portfolio and allows inter-portfolio credit risk contagion, including macroeconomic and financial factors. Also, multivariate scenarios regarding portfolios' credit risks were simulated. The private banking system of Ecuador was explored from January 2005 to December 2018. Delinquency among consumers, microcredit, and housing portfolios and their exogenous determinants were simultaneously quantified using a Bayesian vector autoregressive model. The results show that shocks in exogenous variables are often transmitted immediately in all portfolios. Simultaneously, the autoregressive terms take up to six months to affect these variables, but not cumulatively. A unit of shock in consumer delinquency causes an increase in microcredit delinquency immediately, and this effect is maintained until one month later, where it stabilizes and disappears at the tenth month. Furthermore, a unit of shock in microcredit delinquency produces an increase in consumer delinquency only in the medium term, after seven months. Including inter-portfolio linkages in credit, risk quantification allows to understand the micro-dynamics of absolute risk better and to evaluate the systemic importance of individual shocks since their impact spreads among portfolios with a domino effect. Further research in this area might focus on how to use these techniques as operational tools to incorporate financial stability considerations into control risk policy decision-making. [ABSTRACT FROM AUTHOR]

We present the R package mgm for the estimation of k-order mixed graphical models (MGMs) and mixed vector autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions of MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package.

The study focuses on revealing key monetary policy instruments that can influence stock market development and elaborating whether shocks from financial markets and other macroeconomic conditions are further transferred to the real sector, as expected under the monetary transmission mechanism. This paper is an extension of our previous theoretical and empirical research expanding to ten developed and eight developing economies for the period of 1999-2020 using panel data and vector autoregressive models, impulse response functions, and scenario analysis. Firstly, it was examined that actions of monetary policymakers were efficient for stimulating the development of stock markets mostly for developed countries, whereas stock indices in most developing countries seemed not to be sensitive to changes in monetary conditions. Using scenario analysis and impulse response functions, it was discovered that in developing countries, including Poland and Ukraine, an expansionary policy focused on increasing money supply would mitigate deceleration and facilitate the growth of stock indices in the next four quarters, whereas, in developed countries, including the USA, a decline in interest rates under expansionary regime would stimulate the development of stock markets. Finally, the evolution of financial markets together with macroeconomic, social, and political conditions was concluded to be a statistically important factor of economic growth, as initially expected. [ABSTRACT FROM AUTHOR]

Copyright of Scientific Papers NaUKMA. Economics is the property of National University of Kyiv-Mohyla Academy, Faculty of Humanities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Interconnectedness between economic institution and sectors, already recognised as a trigger of the great financial crisis in 2008–2009, is assuming growing importance in financial systems. In this article, we study contagion effects between corporate sectors using financial network models, in which the significant links are identified through conditional independence testing. While the existing financial network literature is mostly focused on Gaussian processes, our approach is based on discrete data. We indeed test dependence in the conditional mean (and volatility) of default counts in different economic sector estimated from Poisson autoregressive models, and in their shocks. Our empirical application to Italian corporate defaults in the 1996–2018 period reveals evidence of a high inter-sector vulnerability, especially at the onset of the global financial crisis in 2008 and in the following years. Many contagion effects between corporate sectors are indeed found in the shock component of the default count dynamics. [ABSTRACT FROM AUTHOR]

We present a comprehensive modelling framework aimed at quantifying the response of agricultural commodity prices to changes in their potential determinants. The problem of model uncertainty is assessed explicitly by concentrating on specification selection based on the quality of short‐term out‐of‐sample forecasts (1 to 12 months ahead) for the price of wheat, soybeans and corn. Univariate and multivariate autoregressive models (autoregressive [AR], vector autoregressive [VAR] and vector error correction [VEC] specifications, estimated using frequentist and Bayesian methods), specifications with heteroskedastic errors (AR conditional heteroskedastic [ARCH] and generalized AR conditional heteroskedastic [GARCH] models) and combinations of these are entertained, including information about market fundamentals, macroeconomic and financial developments, and climatic variables. In addition, we assess potential non‐linearities in the commodity price dynamics along the business cycle. Our results indicate that variables measuring market fundamentals and macroeconomic developments (and, to a lesser extent, financial developments) contain systematic predictive information for out‐of‐sample forecasting of commodity prices and that agricultural commodity prices react robustly to shocks in international competitiveness, as measured by changes in the real exchange rate. [ABSTRACT FROM AUTHOR]

Head movement is an important but often overlooked component of emotion and social interaction. Examination of regularity and differences in head movements of infant-mother dyads over time and across dyads can shed light on whether and how mothers and infants alter their dynamics over the course of an interaction to adapt to each others. One way to study these emergent differences in dynamics is to allow parameters that govern the patterns of interactions to change over time, and according to person- and dyad-specific characteristics. Using two estimation approaches to implement variations of a vector-autoregressive model with time-varying coefficients, we investigated the dynamics of automatically-tracked head movements in mothers and infants during the Face-Face/Still-Face Procedure (SFP) with 24 infant-mother dyads. The first approach requires specification of a confirmatory model for the time-varying parameters as part of a state-space model, whereas the second approach handles the time-varying parameters in a semi-parametric ("mostly" model-free) fashion within a generalized additive modeling framework. Results suggested that infant-mother head movement dynamics varied in time both within and across episodes of the SFP, and varied based on infants' subsequently-assessed attachment security. Code for implementing the time-varying vector-autoregressive model using two R packages, dynr and mgcv, is provided. [ABSTRACT FROM AUTHOR]

This paper proposes a three-step method for detecting multiple structural breaks for piecewise stationary vector autoregressive processes. The number of structural breaks can be large and unknown with the locations of the breaks being different among different components. The proposed method is established via a link between a structural break problem and a high-dimensional regression problem. By means of this connection, a group orthogonal greedy algorithm, originated from the high-dimensional variable selection context, is developed for efficiently screening out potential break-points in the first step. A high-dimensional information criterion is proposed for consistent structural breaks estimation in the second step. In the third step, the information criterion further determines the specific components in which structural breaks occur. Monte Carlo experiments are conducted to demonstrate the finite sample performance, and applications to stock data are provided to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

We discover that in each shipping segment the price of scrap, earnings, and the fleet size are jointly determined. Deploying a Vector Error Correction model, we find that international steel-scrap prices explain ship scrap prices, but the price of nickel, crude oil, and seaborne trade have an even higher positive explanatory power on them. This dependence is mainly attributed to the economic nature of the major ship-breaking countries: they are all emerging economies, heavily relying on steel as well as nickel in their development process. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espacios is the property of Talleres de Impresos Oma and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Higher sales and margins are key goals for retailers promoting emerging products, such as organics, but little is known about their marketing effectiveness and their cross-effects on conventional product sales. Extant research reports conflicting results about price and promotional sensitivity for organic products and does not address the impact of organic assortment. This article calculates long-term own- and cross-elasticities of organic and conventional product sales in response to changes in assortment, price, and promotions. Using a rich data set of 56 categories, the authors test hypotheses on how different costs and benefits of organic products affect these elasticities. They find that enduring actions, such as assortment and regular price changes, have a higher elasticity for organics than for conventional products. In contrast with common wisdom, even "core" organic consumers are sensitive to these actions. Increasing organic assortment and promotion breadth yields higher profits for the total category, as do more frequent promotions on conventional products. The category comparison yields specific advice with regard to where larger assortment and lower prices versus more and deeper promotions are most effective. [ABSTRACT FROM AUTHOR]

Demonstrations of marketing effectiveness currently proceed along two parallel tracks: Quantitative researchers model the direct sales effects of the marketing mix, and advertising and branding experts trace customer mind-set metrics (e.g., awareness, affect). The authors merge the two tracks and analyze the added explanatory value of including customer mind-set metrics in a sales response model that already accounts for short- and long-term effects of advertising, price, distribution, and promotion. Vector autoregressive modeling of the metrics for more than 60 brands of four consumer goods shows that advertising awareness, brand consideration, and brand liking account for almost one-third of explained sales variance. Competitive and own mind-set metrics make a similar contribution. Wear-in times reveal that mind-set metrics can be used as advance warning signals that allow enough time for managerial action before market performance itself is affected. Specific marketing actions affect specific mind-set metrics, with the strongest overall impact for distribution. The findings suggest that modelers should include mind-set metrics in sales response models and branding experts should include competition in their tracking research. [ABSTRACT FROM AUTHOR]

We followed up the interplay between antibiotic use and resistance over time in a tertiary-care hospital in Hungary. Dynamic relationships between monthly time-series of antibiotic consumption data (defined daily doses per 100 bed-days) and of incidence densities of Gram-negative bacteria (Escherichia coli, Klebsiella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii) resistant to cephalosporins or carbapenems were followed using vector autoregressive models sequentially built of time-series ending in 2015, 2016, 2017, 2018, and 2019. Relationships with Gram-negative bacteria as a group were fairly stable across years. At species level, association of cephalosporin use and cephalosporin resistance of E. coli was shown in 2015–2017, leading to increased carbapenem use in these years. Association of carbapenem use and carbapenem resistance, as well as of carbapenem resistance and colistin use in case of A. baumannii, were consistent throughout; associations in case of Klebsiella spp. were rarely found; associations in case of P. aeruginosa varied highly across years. This highlights the importance of temporal variations in the interplay between changes in selection pressure and occurrence of competing resistant species.

This paper presents Granger mediation analysis, a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The independent observation assumption is thus unrealistic for this type of time‐series data. To address this challenge, our framework integrates two types of models: causal mediation analysis across the mediation variables, and vector autoregressive (VAR) models across the temporal observations. We use "Granger" to refer to VAR correlations modeled in this paper. We further extend this framework to handle multilevel data, in order to model individual variability and correlated errors between the mediator and the outcome variables. Using Rubin's potential outcome framework, we show that the causal mediation effects are identifiable under our time‐series model. We further develop computationally efficient algorithms to maximize our likelihood‐based estimation criteria. Simulation studies show that our method reduces the estimation bias and improves statistical power, compared with existing approaches. On a real fMRI data set, our approach quantifies the causal effects through a brain pathway, while capturing the dynamic dependence between two brain regions. [ABSTRACT FROM AUTHOR]

This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspecification. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model fit. Any (sufficiently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspecified parameters. In particular, we show that the inability to consistently estimate locally misspecified parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims. [ABSTRACT FROM AUTHOR]

Vector autoregressive (VAR) models aim to capture linear temporal interdependencies among multiple time series. They have been widely used in macroeconomics and financial econometrics and more recently have found novel applications in functional genomics and neuroscience. These applications have also accentuated the need to investigate the behavior of the VAR model in a high-dimensional regime, which provides novel insights into the role of temporal dependence for regularized estimates of the model's parameters. However, hardly anything is known regarding properties of the posterior distribution for Bayesian VAR models in such regimes. In this work, we consider a VAR model with two prior choices for the autoregressive coefficient matrix: a nonhierarchical matrix-normal prior and a hierarchical prior, which corresponds to an arbitrary scale mixture of normals. We establish posterior consistency for both these priors under standard regularity assumptions, when the dimension p of the VAR model grows with the sample size n (but still remains smaller than n). A special case corresponds to a shrinkage prior that introduces (group) sparsity in the columns of the model coefficient matrices. The performance of the model estimates are illustrated on synthetic and real macroeconomic datasets. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Existing methods for structural health monitoring are limited due to their sensitivity to changes in environmental and operational conditions, which can obscure the indications of damage by introducing nonlinearities and other types of noise into the structural response. In this article, we introduce a novel approach using state-space probability models to infer the conditions underlying each time step, allowing the definition of a damage metric robust to environmental and operational variation. We define algorithms for training and prediction, describe how the algorithm can be applied in both the presence and absence of measurements for external conditions, and demonstrate the method's performance on data acquired from a laboratory structure that simulates the effects of damage and environmental and operational variation on bridges. [ABSTRACT FROM AUTHOR]