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We consider a wave equation with variable coefficients in time and space in a bounded domain Ω which has the smooth boundary Γ = Γ 0 ∪ Γ 1 such that Γ ¯ 0 ∩ Γ ¯ 1 ≠ ∅ . We study this system that has a homogeneous Dirichlet boundary on Γ 0 and a dynamic boundary on Γ 1 . The innovation of the paper lies in the coefficients which depends on the time variable and the singularities generated by changing the boundary conditions along the interface, thus we need some special techniques to deal with these difficulties. Under some geometric assumptions, the exponential decay result of the system is established by the Riemannian geometry method and the energy perturbation method. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation and simulation of a specific subset of bilinear time series models characterized by dynamic exponential coefficients. Employing an exponential framework, we delve into the implications of the exponential function for our estimation process. Our primary aim is to estimate the coefficients of the proposed model using exponential coefficients derived from time-varying parameters. Through this investigation, our goal is to shed light on the asymptotic behaviors of the estimators and scrutinize their existence and probabilistic traits, drawing upon the foundational theorem established by Klimko and Nilsen. The least squares approach is pivotal in both estimating coefficients and analyzing estimator behavior. Moreover, we present a practical application to underscore the real-world implications of our research. By offering concrete examples of applications and simulations, we endeavor to provide readers with a comprehensive understanding of the implications of our work within the realm of time series analysis, specifically focusing on bilinear models and time-varying exponential coefficients. This multifaceted approach underscores the potential impact and practical relevance of our findings, contributing to the advancement of the field of time series analysis. To discern the symmetry characteristics of the model, we estimate it using coefficients that sum to zero and conduct a brief comparative analysis of two bilinear models. [ABSTRACT FROM AUTHOR]

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia. [ABSTRACT FROM AUTHOR]

The discrete cosine transform is a commonly used technique in the field of signal processing that employs cosine basis functions for signal analysis. Traditionally, the regression coefficients of the cosine basis functions are solely based on frequency information. This paper extends the regression coefficients associated with the cosine basis functions to take into account both frequency and time information, not just frequency information alone. This modification results in an ill-posed linear system, which requires regularization to prevent overfitting. To address this, this paper uses shaping regularization, a technique used to stabilize ill-posed problems. By doing so, the absolute values of these extended coefficients, now exhibiting variations in both frequency and time domains, are defined as the time–frequency distribution of that input signal. The numerical experiments conducted to validate this approach demonstrate that the proposed method yields a commendable time–frequency resolution. Consequently, it proves valuable for interpreting seismic data, showcasing its potential for applications in this field. [ABSTRACT FROM AUTHOR]

The relationship between energy technology R&D and energy consumption has remained an unsettled empirical issue. This study investigates whether accumulative energy technology R&D investments have contributed to decreases in final energy and fossil fuel consumption in 19 OECD countries over the period 1975–2020. We ask whether an increase in energy technology R&D stocks has contributed to decreases in final energy and fossil fuel consumption and hence may effect energy savings. Methodologically, we treat the accumulation and depreciation of energy technology R&D investments as R&D stocks, and we use state-of-the-art estimation methods for dealing with cross-sectional dependence, nonstationarity, heterogeneity and time-varying coefficients that often plague panel-time-series models. Across our heterogeneous dynamic models, we find those estimators that properly account for cross-sectional dependence yield negative and significant coefficients on energy technology R&D stocks. Our time-varying estimates on energy technology R&D stocks confirm the above findings and feature two turning points—i.e., the 1979 oil shock, the Fukushima accident—in effecting energy savings. These two turning points provide strong evidence that the sample countries are subject to common shocks. The evidence we present supports the environmental sustainability orientated view that energy technology R&D is playing a prominent role in making energy savings. [ABSTRACT FROM AUTHOR]

This paper adds to the existing literature by examining the macroeconomic, political and institutional determinants of tax policy cyclicality conditional on financial development. We find that an increase in trade and financial openness leads to pro-cyclical VAT and counter-cyclical CIT rate response in high financially developed economies, while an increase in financial openness is associated with counter-cyclical VAT and PIT responses when the levels of financial development are low. A high public debt ratio leads to a counter-cyclical VAT rate response in economies with low financial development. Political power and fiscal institutions are factors that affect the tax policy cyclicality only in less financially developed economies. [ABSTRACT FROM AUTHOR]

The emergence of large datasets with long time spans has cast doubt on the assumption of constant loadings in conventional factor models. Being a potential solution, the time-varying factor model (TVFM) has attracted enormous interest in the literature. However, TVFM also suffers from the well-known problem of nonidentifiability. This article considers the situations under which both the factors and factor loadings can be estimated without rotations asymptotically. Asymptotic distributions of the proposed estimators are derived. Theoretical findings are supported by simulations. Finally, we evaluate the forecasting performance of the estimated factors subject to different identification restrictions using an extensive dataset of the U.S. macroeconomic variables. Substantial differences are found among the choices of identification restrictions. [ABSTRACT FROM AUTHOR]

Globalization is an undeniable phenomenon in the current era and different theories of globalization-economic growth show that there is no theoretical agreement in this regard and there are many supporters and oppositions in this field. Considering that, firstly, globalization has different aspects including economic, cultural and political. Second, the effects of globalization are different in developing and developed countries and thirdly, according to the theoretical point of view, the effect of globalization on economic growth varies over time. Therefore, in the present study, using the time-varying non-parametric panel data model and applying three different aspects of economic, cultural and political globalization KOF index, and considering the de facto and de jure effects of the mentioned index. It has been investigated the different effects of globalization over time on the GDP per capita of countries with high per capita income (28 countries) and middle per capita income (36 countries). The results of estimating the model used in this research based on the local linear dummy variable method for time-varying non-parametric panel data showed that except for the first few years in the period from 1980 to 2019, the economic globalization index has increased the per capita income of countries with high per capita income, However, the index of economic globalization in countries with middle per capita income had a positive effect on per capita income only in the years 1996 to 2008, and had a negative effect on it in the rest of the years. Also by dividing globalization into de facto and de jure aspects determined Both de facto and de jure aspects of economic globalization during the period from 1980 to 2019 on average have led to economic growth in countries with high per capita income. But de jure aspects of economic globalization almost had a positive effect on the economic growth of countries with middle per capita income especially after 1995 to 2019 And economic globalization has a negative effect on the economic growth of these types of countries from an de facto aspect in most of the investigated years. [ABSTRACT FROM AUTHOR]

Using two measures of the fiscal position, the cyclically adjusted primary budget balance (CAPB) and the total budget balance, we assess the Twin Deficit Hypothesis for the Euro Area in the period 1995–2020. Furthermore, we estimate time-varying coefficients of the current account balance responses to changes in the CAPB and in the government balance and we identify the determinants of these responses. The CAPB and the government balance, in addition to being determinants of the current account balance, are also determinants of the time-varying responses of the current account balance. The government balance, current account balance and public debt, and the temporal period also influence these responses. [ABSTRACT FROM AUTHOR]

This paper focuses on the estimation and simulation of a specific subset of bilinear time series models characterized by dynamic exponential coefficients. Employing an exponential framework, we delve into the implications of the exponential function for our estimation process. Our primary aim is to estimate the coefficients of the proposed model using exponential coefficients derived from time-varying parameters. Through this investigation, our goal is to shed light on the asymptotic behaviors of the estimators and scrutinize their existence and probabilistic traits, drawing upon the foundational theorem established by Klimko and Nilsen. The least squares approach is pivotal in both estimating coefficients and analyzing estimator behavior. Moreover, we present a practical application to underscore the real-world implications of our research. By offering concrete examples of applications and simulations, we endeavor to provide readers with a comprehensive understanding of the implications of our work within the realm of time series analysis, specifically focusing on bilinear models and time-varying exponential coefficients. This multifaceted approach underscores the potential impact and practical relevance of our findings, contributing to the advancement of the field of time series analysis. To discern the symmetry characteristics of the model, we estimate it using coefficients that sum to zero and conduct a brief comparative analysis of two bilinear models.

This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks (SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time, effectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results. [ABSTRACT FROM AUTHOR]

Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee–Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950–2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li–Lee models in both single- and multi-population scenarios. [ABSTRACT FROM AUTHOR]

Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer's disease neuroimaging initiative study further illustrates the utility of the methodology. [ABSTRACT FROM AUTHOR]

In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time‐varying coefficients, time‐independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study. [ABSTRACT FROM AUTHOR]

The conventional Cox proportional hazards (PH) model typically assumes fully observed predictors and constant regression coefficients. However, some predictors are latent variables, each of which must be characterized by multiple observed indicators from various perspectives. Moreover, the predictor effects may vary with time in practice. Accommodating such latent variables and identifying temporal covariate effects are frequently of primary interest. This study proposes a generalized structural equation model to investigate the temporal effects of observed and latent risk factors on the hazards of interest. The proposed model comprises a confirmatory factor analysis model as the measurement equation and a varying-coefficient PH model with observed and latent predictors as the structural equation. A hybrid procedure that combines the expectation-maximization (EM) algorithm and the corrected estimating equation approach is developed to estimate unknown parameters and coefficient functions. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to a health survey study reveals insights into risk factors for elders' life expectancy. [ABSTRACT FROM AUTHOR]

This article is devoted to the investigation of the p th moment synchronization problem for stochastic impulsive neural networks (SINNs) with time-varying coefficients and unbounded delays. First of all, one novel impulse generation rule is proposed, which generates the more general impulsive sequences. In order to cope with time-varying coefficients, unbounded delays and impulsive disturbances, some impulsive differential inequalities are developed by utilizing the comparison principle. With the help of the established impulsive inequalities, the synchronization of SINNs is analyzed in detail, and both p th moment exponential synchronization and asymptotical synchronization are realized by designing appropriate feedback controllers. Finally, several simulation examples are provided to illustrate the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

Recurrent event data with a terminal event commonly arise in many longitudinal follow‐up studies. This article proposes a class of dynamic semiparametric transformation models for the marginal mean functions of the recurrent events with a terminal event, where some covariate effects may be time‐varying. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. In addition, relevant significance tests are suggested for examining whether or not covariate effects vary with time, and a model checking procedure is presented for assessing the adequacy of the proposed models. The finite sample performance of the proposed estimators is examined through simulation studies, and an application to a medical cost study of chronic heart failure patients is provided. [ABSTRACT FROM AUTHOR]

Time-varying factor models have gained much popularity in recent years. However, the newly developed local estimator is susceptible to a time-varying rotation matrix that may exhibit jumps. This letter proposes a simple method to avoid the jumps. We show by simulations that the proposed procedure can effectively eliminate jumps in the rotation matrix, leading to a substantially lower mean-square forecasting error. • The rotation matrix of the local PCA estimator may exhibit jumps. • A simple rotation method is proposed to smooth the variation of the rotation matrix. • Simulations demonstrate improved forecasting performance of the rotated factors. [ABSTRACT FROM AUTHOR]

This study considers a time-varying coefficient additive hazards model with latent variables to examine potential observed and latent risk factors for survival of interest. The model consists of two parts: confirmatory factor analysis to measure each latent factor through multiple observable variables and a varying coefficient additive hazards model to examine the time-varying effects of the observed and latent risk factors on the hazard function. A hybrid estimation procedure that combines the expectation-maximum algorithm and corrected estimating equation method is developed to estimate the unknown parameters and nonparametric cumulative hazard functions. The consistency and asymptotic normality of the proposed estimators are established, and the pointwise confidence intervals and general confidence bands of the nonparametric functions are constructed accordingly. A satisfactory performance of the proposed method is demonstrated through simulation studies. An application to a study of chronic kidney disease for Chinese type 2 diabetes patients is presented. [ABSTRACT FROM AUTHOR]

Influential existing research has suggested that rather than being static, mortality declines decelerate at young ages and accelerate at old ages. Without accounting for this feature, the forecast mortality rates of the popular Lee–Carter (LC) model are less reliable in the long run. To provide more accurate mortality forecasting, we introduce a time-varying coefficients extension of the LC model by adopting the effective kernel methods. With two frequently used kernel functions, Epanechnikov (LC-E) and Gaussian (LC-G), we demonstrate that the proposed extension is easy to implement, incorporates the rotating patterns of mortality decline and is straightforwardly extensible to multi-population cases. Using a large sample of 15 countries over 1950–2019, we show that LC-E and LC-G, as well as their multi-population counterparts, can consistently improve the forecasting accuracy of the competing LC and Li–Lee models in both single- and multi-population scenarios.

Governments are responsible for economic policy implementation, and their actions affect financial and capital market outcomes. Specifically, the way fiscal policy is conducted matters when credit agencies have to decide on how to rate a sovereign. This paper empirically assesses the effect of a new time‐varying measure of fiscal counter‐cyclicality on the sovereign credit ratings of the main agencies: Fitch, Standard & Poor's, and Moody's. I focus on a heterogeneous sample of 63 advanced and developing economies between 1980 and 2015. First, we find that the degree of fiscal counter‐cyclicality is generally positive and has been increasing over time, being larger in advanced economies. Second, the more counter‐cyclical a fiscal policy is, the better the assessment a rating agency gives to that country, particularly if it is an advanced one. This suggests that fiscal prudence and stabilization concerns are rewarded. Our results are robust to several sensitivity and robustness checks. [ABSTRACT FROM AUTHOR]

This work aims at jointly modelling longitudinal and survival HIV data by considering the sharing of a set of parameters of interest. For the CD4 longitudinal stochastic process we propose a regression model where individual heterogeneity is allowed to vary in terms of the mean and the variance, relaxing the usual assumption of a common variance for the longitudinal residuals. Along, we will be considering a hazard regression model to analyse the time between HIV/AIDS diagnostic and death. For introducing enough flexibility in the structure linking the longitudinal and survival processes, we consider time-varying coefficients. That is achieved using Penalized Splines and allows the relationship to vary in time. The CD4 residuals standard deviation is considered as a covariate in the hazard model, thus enabling to study the effect of the CD4 counts' stability on the survival. The proposed framework surpasses the performance of the most "traditional" joint models, which generally consider a common variance and a time-invariant link. [ABSTRACT FROM AUTHOR]

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. The method calculates the conditional expectations of the coefficients, given the observations. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The estimates are moments estimates. They do not require the disturbances to be Gaussian, but if they are, the estimates are asymptotically equivalent to maximum likelihood estimates. In contrast to Kalman filtering, no specification of an initial state or an initial covariance matrix is required. While the Kalman filter is one sided, the filter proposed here is two sided and therefore uses more of the available information for estimating intermediate states. Further, the proposed filter has a clear descriptive interpretation. [ABSTRACT FROM AUTHOR]

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to approximate the non-linear traffic states. Unlike a static linear regression model, the DLMs assume that their parameters are changing across time. We design a DLM with model parameters defined at each time unit to describe the spatio-temporal characteristics of time-series traffic data. Based on our DLM and its model parameters analytically trained using historical data, we suggest an optimal linear predictor in the minimum mean square error (MMSE) sense. We compare our prediction accuracy of travel time for freeways in California (I210-E and I5-S) under highly congested traffic conditions with those of other methods: the instantaneous travel time, k-nearest neighbor, support vector regression, and artificial neural network. We show significant improvements in the accuracy, especially for short-term prediction. [ABSTRACT FROM AUTHOR]

In this paper, with the quaternion number and time-varying coefficients introduced into traditional BAMNNs, the model of quaternion-valued BAMNNs are formulated. For the first time, fixed-time stabilization of time-varying quaternion-valued BAMNNs is investigated. A novel fixedtime control method is adopted, in which the choice of the Lyapunov function is more general than in most previous results. To cope with the noncommutativity of the quaternion multiplication, two different fixed-time control methods are provided, a decomposition method and a non-decomposition method. Furthermore, to reduce the control strength and improve control efficiency, an adaptive fixed-time control strategy is proposed. Lastly, numerical examples are presented to demonstrate the effectiveness of the theoretical results.

Tracking subjects with disease risks at multiple time points is an important objective for disease prevention and preventive medicine. Appropriate statistical tracking models are essential for identifying risk factors that remain persistent over time and the early detection of subjects with high disease risks. Because disease risks are often defined by multivariate response variables, we propose a class of bivariate risk-predictive probability models that quantify the likelihood of an individual's future disease risk. These models describe the relationships between bivariate risk outcomes at a later time point and covariates at an early time point using a class of conditional quantile-based joint distribution functions. We develop a simulation-based procedure under the stratified bivariate time-varying quantile regression framework to estimate the conditional joint distributions and risk-predictive probabilities. In addition, we use theoretical and simulation studies to show that the estimation procedure yields consistent estimates, and propose a statistical quantity that measures the relative risk to identify high-risk individuals. Finally, we apply the proposed models and procedures to data from the National Growth and Health Study to identify early adolescent girls who are more likely to be diagnosed with hypertension at late adolescence. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we deal with a class of cellular neural networks with time-varying delays. Applying differential inequality strategies without assuming the boundedness conditions on the activation functions, we obtain a new sufficient condition that ensures that all solutions of the considered neural networks converge exponentially to the zero equilibrium point. We give an example to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies of Tang (Appl. Math. Lett. 21:872–876, 2008).

Abstract Background For brown bears (Ursus arctos), hibernation is a critical part of the annual life cycle because energy savings during hibernation can be crucial for overwintering, and females give birth to cubs at that time. For hibernation to be a useful strategy, timing is critical. However, environmental conditions vary greatly, which might have a negative effect on the functionality of the evolved biological time-keeping. Here, we used a long-term dataset (69 years) on brown bear denning phenology recorded in 12 Russian protected areas and quantified the phenological responses to variation in temperature and snow depth. Previous studies analyzing the relationship between climate and denning behavior did not consider that the brown bear response to variation in climatic factors might vary through a period preceding den entry and exit. We hypothesized that there is a seasonal sensitivity pattern of bear denning phenology in response to variation in climatic conditions, such that the effect of climatic variability will be pronounced only when it occurs close to den exit and entry dates. Results We found that brown bears are most sensitive to climatic variations around the observed first den exit and last entry dates, such that an increase/decrease in temperature in the periods closer to the first den exit and last entry dates have a greater influence on the denning dates than in other periods. Conclusions Our study shows that climatic factors are modulating brown bear hibernation phenology and provide a further structuring of this modulation. The sensitivity of brown bears to changes in climatic factors during hibernation might affect their ability to cope with global climate change. Therefore, understanding these processes will be essential for informed management of biodiversity in a changing world.

The forecasting literature shows that when a number of different forecasters produce forecasts of the same variable it is almost always possible to produce a better forecast by linearly combining the individual forecasts. Moreover, it is often argued that a simple average of the forecasts will outperform more complex combination methods. This paper shows that, analytically, nonlinear combinations of forecasts are superior to linear combinations. Empirical results, based on comparisons of real GDP growth projections with outturns for the euro area using time-varying-coefficient estimation, confirm that analytical result, especially for periods marked by structural changes. [ABSTRACT FROM AUTHOR]

This paper revisits, by means of both time series and panel data analyses, the empirical regularity identified by Okun's (in: Proceedings of the business and economics statistics section, American Statistical Association, Washington, DC, 98–103, 1962) seminal paper. Based on a sample of 85 advanced and developing economies between 1978 and 2014, we confirm the existence of an average negative and statistically significant Okun's relationship. At the same time, results suggest that the relation varies substantially across countries and times. Finally, we identify several factors affecting the variation in Okun's coefficient across and within countries. Across countries, the relationship is stronger in countries with higher average unemployment, a larger share of public employment, lower informality and smaller agricultural sectors, and one that is more diversified. Within countries, in addition to some of these factors, we find that deregulation in labor and product markets and recessions have strengthened the response of unemployment to the business cycle. [ABSTRACT FROM AUTHOR]

In time to event studies, censoring often occurs and models that take this into account are wide-spread. In the presence of outliers, standard estimators of model parameters may be affected such that results and conclusions are not reliable anymore. This in turn also hampers the detection of these outliers due to masking effects. To cope with outliers when using proportional hazard models, we propose to use the Brier score as a loss function. Since the coefficients often vary over time, we focus on the piecewise constant hazard model, which can flexibly model time-varying coefficients if a large number of cut-points is used. To prevent overfitting, we add a penalty term that potentially shrinks time-varying effects to constant effects. By fitting the coefficients of the piecewise constant hazard model using a penalized Brier score loss, we obtain a robust model that can handle time-varying coefficients. Its good performance is illustrated in a simulation study and using two datasets from practice. [ABSTRACT FROM AUTHOR]

In recent years, the nature-inspired optimization algorithms known as intelligent optimization methods have been applied successfully for solving the different problems, along with the well-known mathematical methods. One of the new evolutionary algorithms presented lately is the imperialist competitive algorithm (ICA). This algorithm is based on the behaviour of imperialists in their attempt to conquer the colonies. In this paper, the original ICA has been extended and a new version of ICA has been proposed entitled "MICA". The proposed MICA uses an efficient assimilation strategy to enhance the global exploration ability and to preserve a premature convergence. This new assimilation scheme uses the most powerful imperialist's information to update. To validate the efficiency of the proposed algorithm, MICA is tested on a set of 28 non-linear benchmark functions with various dimensions and complexities. The results demonstrate that the proposed strategy enables the modified ICA to have better or at least comparable outcomes in comparison with the original ICA and the other state-of-the-art approaches at handing different types of problems. [ABSTRACT FROM AUTHOR]

Hazard models are popular tools for the modeling of discrete time-to-event data. In particular two approaches for modeling time dependent effects are in common use. The more traditional one assumes a linear predictor with effects of explanatory variables being constant over time. The more flexible approach uses the class of semiparametric models that allow the effects of the explanatory variables to vary smoothly over time. The approach considered here is in between these modeling strategies. It assumes that the effects of the explanatory variables are piecewise constant. It allows, in particular, to evaluate at which time points the effect strength changes and is able to approximate quite complex variations of the change of effects in a simple way. A tree-based method is proposed for modeling the piecewise constant time-varying coefficients, which is embedded into the framework of varying-coefficient models. One important feature of the approach is that it automatically selects the relevant explanatory variables and no separate variable selection procedure is needed. The properties of the method are investigated in several simulation studies and its usefulness is demonstrated by considering two real-world applications. [ABSTRACT FROM AUTHOR]

We present a new dataset of time-varying measures of social spending cyclicality in a sample of 26 advanced countries between 1982 and 2012. More specifically, we focus on five categories of government social expenditure: health, social protection, pensions, education and welfare. Results show that health and education spending is generally acyclical, while pensions are procyclical and social protection and welfare spending are counter-cyclical. That said, sample averages hide serious heterogeneity across countries. Our findings suggest that the higher the degree of countercyclicality of government's social spending, the lower output volatility will be. Results are robust to several specifications, the use of alternative dependent variables, and estimators (including those accounting for endogeneity). [ABSTRACT FROM AUTHOR]

A number of recent papers have proposed a time-varying-coefficient (TVC) procedure that, in theory, yields consistent parameter estimates in the presence of measurement errors, omitted variables, incorrect functional forms, and simultaneity. The key element of the procedure is the selection of a set of driver variables. With an ideal driver set the procedure is both consistent and efficient. However, in practice it is not possible to know if a perfect driver set exists. We construct a number of Monte Carlo experiments to examine the performance of the methodology under (i) clearly-defined conditions and (ii) a range of model misspecifications. We also propose a new Bayesian search technique for the set of driver variables underlying the TVC methodology. Experiments are performed to allow for incorrectly specified functional form, omitted variables, measurement errors, unknown nonlinearity and endogeneity. In all cases except the last, the technique works well in reasonably small samples. [ABSTRACT FROM AUTHOR]

This paper compiles a novel data set of time‐varying measures of government‐consumption cyclicality for a panel of 46 African economies between 1960 and 2014. Government consumption has, generally, been highly procyclical over time in this group of countries. However, sample averages hide serious heterogeneity across countries with the majority of them showing procyclical behaviour despite some positive signs of graduation from the "procyclicality trap" in a few cases. By means of weighted least squares regressions, we find that more developed African economies tend to have a smaller degree of government‐consumption procyclicality. Countries with higher social fragmentation, and those that are more reliant on foreign aid inflows, tend to have a more procyclical government‐consumption policy. Better governance promotes countercyclical‐fiscal policy while increased democracy dampens it. Finally, some fiscal rules are important in curbing the procyclical behaviour of government consumption. [ABSTRACT FROM AUTHOR]

Using monthly data for 10 euro area countries between 1999:01 and 2015:12, we take a new three-step methodological approach: first, we inspect the key determinants of 10-year government bond yield spreads; second, we compute country-specific time-varying coefficient models of spreads' determinants; third, we use these estimates as explanatory variables in panel regressions using output volatility as the dependent variable. We find that better fiscal positions or higher-than-expected economic growth prospects reduce the yield spreads, while increases in the VIX, bid-ask spread, debt-to-GDP ratio or real effective exchange rate appreciation increase the spreads. Moreover, the responsiveness of the yield spread determinants increased in the run-up to the global financial crisis. Finally, for the case of the budget balance and real growth (bid-ask spread, debt-to-GDP ratio, real effective exchange rate and VIX), the larger (higher) in absolute value the corresponding spread's responsiveness, the lower (higher) the economic volatility. [ABSTRACT FROM AUTHOR]

The missing data problem is common in longitudinal or repeated measurements data. When the missingness mechanism is nonignorable, the distribution of the observed response and indicators of missingness should be modelled jointly using either 'shared random-effects model' or 'correlated random-effects model'. However, computational challenges arise in the model fitting due to intractable numerical integration involved in the log-likelihood function. We provide alternative modeling of 'correlated random-effects model' using latent variables and propose a simple algorithm based on Gibbs sampling for estimation of associated parameters. The method is illustrated through simulation and the analysis of a real data set arising from an autism study. [ABSTRACT FROM AUTHOR]

The Environmental Kuznets Curve (EKC) hypothesis has been extensively analyzed as a long-term relationship between the economic development stages and related environmental impacts. Most of the existing literature has however produced unreliable results as extensive databases have been used without considering the length of the time span. In this study, the EKC hypothesis is re-investigated for the United Kingdom for the time period 1850–2018. The objective is to conduct an innovative methodological approach that, contrary to the conventional estimation methods, uses time-varying techniques⸺the time-varying cointegration test of Bierens and Martins (2010) and the time-varying causality test of Shi et al. (2018, 2020)⸺to account for the incidence of unexpected historical events, such as socio-economic and policy crises. Results show that, for the considered years, the EKC hypothesis is valid for the UK. In addition, by including income, carbon dioxide emissions, and energy consumption data, the present study also analyses the environmental impacts of energy use and the environmental quality changes that have taken place during the considered period of time. This result proves that energy consumption pollutes the environment significantly; however, the magnitude of its impact can be affected by many shocks. According to the empirical findings, policymakers could adhere to current policies because environmental quality has started to increase for several decades in the United Kingdom. The employed methodology, and the related results, can support the definition of policies and the development of additional research initiatives. [Display omitted] • The EKC hypothesis is tested for the United Kingdom. • Data covers the period from 1850 to 2018. • Time-varying cointegration and causality tests are utilized. • Results support the validity of the EKC hypothesis. [ABSTRACT FROM AUTHOR]

• We re-examine whether the Kimchi premium is a bubble by the resale option theory. • Time-varying relationship between the premium, volume, and volatility is tested. • Nonparametric regressions and bootstrap confidence intervals are adopted. • Positive relationship is not robust over time, when the size of premium increases. This study investigates whether the Kimchi premium, the phenomenon in which the Bitcoin price in Korea is persistently higher than the United States price, reflects a speculative bubble. Eom (2021) argued that the Kimchi premium is a bubble, evidenced by its positive relationship with trading volume and price volatility estimated from unconditional regressions. We re-examine this evidence by estimating and testing time-varying coefficients using nonparametric regressions and bootstrap confidence intervals. Our results show that the positive relationship is not robust over time, suggesting that we do not yet have clear evidence to conclude that the Kimchi premium is a bubble. [ABSTRACT FROM AUTHOR]

Driver fatigue is a major safety concern for commercial truck drivers and is directly related to the total hours of sleep prior to a working shift. To evaluate changes in driving performance over a long on‐duty driving period, we propose a mixed Poisson process recurrent‐event model with time‐varying coefficients. We use data from 96 commercial truck drivers whose trucks were instrumented with an advanced in situ data acquisition system. The driving performance is measured by unintentional lane deviation events, a known performance deterioration related to fatigue. Driver sleep time and other activities are extracted from a detailed activity register. The time‐varying coefficients are used to model the baseline intensity and difference among three cohorts of shifts in which the driver slept less than 7 hours, between 7 to 9 hours, and more than 9 hours prior to driving. We use the penalized B‐splines approach to model the time‐varying coefficients and an expectation‐maximization algorithm with embedded penalized quasi‐likelihood approximation for parameter estimation. Simulation studies show that the proposed model fits low and high event rate data well. The results show a significantly higher intensity after 8 hours of on‐duty driving for shifts with less than 7 hours of sleep prior to work. The study also shows drivers tend to self‐adjust sleep duration, total driving hours, and breaks. This study provides crucial insight into the impact of sleep time on driving performance for commercial truck drivers and highlights the on‐road safety implications of insufficient sleep and breaks while driving. [ABSTRACT FROM AUTHOR]