Carter Model Research Articles

Impact of Service Quality on Customer Satisfaction in Islamic Banks of Qatar

Setting a consistent norm to satisfy consumers is incredibly challenging in the service sector. As customers become increasingly aware of the services banks provide, their expectations for service quality rise as well. Due to the specialization of services, customers are loyal to a particular company rather than a particular service, making it difficult to retain customers (Milan, 2004). Customer satisfaction is the basis for ensuring customer loyalty, which in turn ensures future customer retention (?uki? & Kijev?anin, 2012). In the banking sector, evaluating a bank's performance is highly dependent on the quality of its services. Therefore, this research aims to study the impact of service quality on customer satisfaction in Islamic banks of Qatar using the CARTER model. A structured questionnaire is the survey method adopted to collect the data. The data will be analyzed using SPSS software to test the hypothesis about the association of each variable. Data were collected from 162 respondents of five Islamic banks in Qatar. The findings revealed that only two of the components of CARTER, assurance, and responsiveness, proved to be positive and significant and four of the other components, i.e., compliance, tangibles, empathy, and reliability were positive but insignificant to customer satisfaction. The findings of this study will lead to a better understanding of the areas of emphasis for the provision of high-quality service by Islamic banks in Qatar.

Modeling and Forecasting the Death Trend Due to Respiratory System Diseases using the LeeCarter model in Northern Iran

"Background and Aim: Respiratory system diseases (RSDs) represent the major burden of disease, including death worldwide. The main purpose of this study was to model and forecast the death rate due to diseases of the respiratory system Based on Lee Carter's model during the years 2011-2028. Methods: The type of study was a secondary analysis based on available data. The statistical population included all deaths registered in the health department of Golestan University of Medical Sciences during the years 2011-2018. Data analysis was performed using Demography 18.1 and StMoMo packages in R3.6.2 software. Results: The death rate due to RSDs in 2011 in the total population was 0.22, in women 0.19, and in men 0.25 per 1000 population. The death rate due to these diseases shows an increasing trend in 2018 to 0.33 in the total population, 0.29 in women, and 0.36 in men per 1000 population. In general, the death rate due to these diseases increased by approximately 0.10 per 1000 population from the years 2011 to 2018. It is predicted that the increasing trend of deaths due to RSDs will continue and in 2028 it will reach 4.88 in the total population, 3.56 in women, and 2.27 in men per 1000 population. Conclusions: Our findings show a significant increase in respiratory disease mortality over the past eight years. A combination of prevention and treatment strategies through urban planning, including the control of environmental pollutants and environmental health programs, is essential for the effective control of respiratory diseases "

The Effect of Service Quality Based on The Carter Model on Bank Customer Loyalty

The Effect of Service Quality Based on The Carter Model on Bank Customer Loyalty

Deep Learning Incorporated Bühlmann Credibility in the Modified Lee–Carter Mortality Model

This research study proposes the inclusion of randomness or an error term in the modified Lee–Carter model, which improves the traditional Lee–Carter model for modeling and forecasting mortality risk for years in the actuarial science field. While the modified Lee–Carter model points out some of its common shortcomings , it has no distributional assumption that has been placed on the error/disturbance term. Incorporating a Gaussian distributional assumption on the error term is proposed, and then, the deep learning technique is used to obtain the parameter estimates. It is a departure from the traditional singular value decomposition estimation technique for estimating the parameters in the model. Finally, the Bühlmann credibility approach is incorporated into the model to determine its forecasting precision compared to the classical Lee–Carter model before applied in actuarial valuations.

Modelling mortality: A bayesian factor-augmented var (favar) approach

AbstractLongevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field’s workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee–Carter models as special cases and inherits both frameworks’ advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method’s efficacy in both in-sample and out-of-sample performance.

Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model

This paper proposes a two-step LASSO based vector autoregressive (2-LVAR) model to forecast mortality rates. Within the VAR framework, recent studies have developed a spatial–temporal autoregressive (STAR) model, in which age-specific mortality rates are related to their own historical values (temporality) and the rates of the neighboring cohorts (spatiality). Despite its desirable age coherence property and the improved forecasting accuracy over the widely used Lee–Carter (LC) model, STAR employs a rather restrictive structure that only allows for non-zero cohort effects of the same cohorts and the neighboring cohorts. To address this limitation, the proposed 2-LVAR model adopts a data-driven principle, as in a sparse VAR (SVAR) model, to offer more flexibility in the parametric structure. A two-step estimation strategy is developed accordingly to resolve the challenging objective function of 2-LVAR, which consists of non-standard L2 and LASSO-type penalties with constraints. Using empirical data from Australia, the United Kingdom, France, and Switzerland, we show that the 2-LVAR model outperforms the LC, STAR, and SVAR models in most of our forecasting results. Further simulation studies confirm this outperformance, and analyses based on life expectancy at birth empirically support the existence of age coherence. The results of this paper will help researchers understand the mortality projections in the long run and improve the reserving/ratemaking accuracy for life insurers.

KEPUASAN NASABAH MELALUI PELAYANAN MODEL CARTER PADA BANK MEGA SYARIAH CABANG KOTA BENGKULU

The purpose of this study was to determine the forms of service of the CARTER model in increasing customer satisfaction at Bank Mega Syariah Bengkulu City Branch. The researcher used a qualitative descriptive approach with the technique of determining the sample using purposive sampling, which consisted of 2 employees and 4 customers. The data collection technique in this study used observation techniques, direct interviews with informants, namely Bank Mega Syariah employees and customers, and documentation. This study uses explanatory data analysis techniques or explanatory explanations or explanations that have a function to describe data with explanations in the form of narratives. From the results of the research and discussion, it was found that the CARTER model according to Othman and Owen which consists of Compliance, Assurance, Realibility, Tangible, Empathy and Responsiveness in increasing customer satisfaction at Bank Mega Syariah Bengkulu City Branch as a whole is quite good in terms of service, but in terms of Tangible it is necessary attention and further improvement of services.

Comparative Study of Mortality Rate Prediction Using Data-Driven Recurrent Neural Networks and the Lee–Carter Model

The Lee–Carter model could be considered as one of the most important mortality prediction models among stochastic models in the field of mortality. With the recent developments of machine learning and deep learning, many studies have applied deep learning approaches to time series mortality rate predictions, but most of them only focus on a comparison between the Long Short-Term Memory and the traditional models. In this study, three different recurrent neural networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Unit, are proposed for the task of mortality rate prediction. Different from the standard country level mortality rate comparison, this study compares the three deep learning models and the classic Lee–Carter model on nine divisions’ yearly mortality data by gender from 1966 to 2015 in the United States. With the out-of-sample testing, we found that the Gated Recurrent Unit model showed better average MAE and RMSE values than the Lee–Carter model on 72.2% (13/18) and 67.7% (12/18) of the database, respectively, while the same measure for the Long Short-Term Memory model and Bidirectional Long Short-Term Memory model are 50%/38.9% (MAE/RMSE) and 61.1%/61.1% (MAE/RMSE), respectively. If we consider forecasting accuracy, computing expense, and interpretability, the Lee–Carter model with ARIMA exhibits the best overall performance, but the recurrent neural networks could also be good candidates for mortality forecasting for divisions in the United States.

Extensions of the Lee–Carter model to project the data‐driven rotation of age‐specific mortality decline and forecast coherent mortality rates

AbstractAccurate forecasts and analyses of mortality rates are essential to many operational practices, such as the designing of pension schemes. Recent studies have focused on the age coherent forecasts, such that long‐term predictions will not diverge infinitely among ages. Although intuitively and biologically reasonable, this age coherence property is lost when the seminal Lee–Carter (LC) model and almost all its existing extensions are employed. Those include the famous Li–Lee (LL) model, which is argued to produce coherent forecasts of mortality rates across populations, but not ages. In this paper, we progressively explore two effective frameworks with three extensions, by applying the LL specification to the single‐population model (LC‐S) and by introducing time‐dependent age patterns of mortality declines in the forecasting steps with fixed (LC‐VF) and variant (LC‐V) intercepts when modeling those patterns. In particular, the recommended LC‐V model is motivated from the intermediate LC‐S and LC‐VF and resolves all remaining issues of both models. Also, the final LC‐V model is easy to implement, incorporates the dynamic and rotating age‐specific patterns of mortality decline, provides age‐coherent forecasts of mortality rates in the long run, and is straightforwardly extensible to multi‐population cases. Using a large sample of 15 countries, we show that the LC‐V model and its multipopulation counterpart can consistently improve the forecasting accuracy of the competing LC, LL and other extensions in both single‐population and multipopulation scenarios. Long‐term analyses up to 2100 further demonstrate the existence of age coherence when forecasting with the proposed LC‐V model.

A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting

Mortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality and longevity risks. There is an abundance of literature on the topic of modelling and forecasting mortality, which often leads to confusion in determining a particular model to be adopted as a reliable tool. In this study, we conducted a bibliometric analysis with a focus on citation and co-citation analyses and co-occurrences of keywords to determine the most widely used stochastic mortality model. We found that the Lee–Carter model has remained one of the most relevant mortality models since its development in the 1990s. Furthermore, we also aimed to identify emerging topics and trends relating to mortality modelling and forecasting based on an analysis of authors’ keywords. This study contributes to the literature by providing a comprehensive overview and evolution of publications in stochastic mortality modelling and forecasting. Researchers can benefit from the present work in determining and exploring emerging trends and topics for future studies.

EXTENDING THE LEE–CARTER MODEL WITH VARIATIONAL AUTOENCODER: A FUSION OF NEURAL NETWORK AND BAYESIAN APPROACH

AbstractIn this study, we propose a nonlinear Bayesian extension of the Lee–Carter (LC) model using a single-stage procedure with a dimensionality reduction neural network (NN). LC is originally estimated using a two-stage procedure: dimensionality reduction of data by singular value decomposition followed by a time series model fitting. To address the limitations of LC, which are attributed to the two-stage estimation and insufficient model fitness to data, single-stage procedures using the Bayesian state-space (BSS) approaches and extensions of flexibility in modeling by NNs have been proposed. As a fusion of these two approaches, we propose a NN extension of LC with a variational autoencoder that performs the variational Bayesian estimation of a state-space model and dimensionality reduction by autoencoding. Despite being a NN model that performs single-stage estimation of parameters, our model has excellent interpretability and the ability to forecast with confidence intervals, as with the BSS models, without using Markov chain Monte Carlo methods.

The Effect of CARTER Model on Customer Satisfaction Towards Loyalty: An Investigation on Muslim Customers of Islamic Banks in Bangladesh

The objective of this study was to investigate the effects of CARTER model on customer satisfaction towards loyalty among Muslim customers of Islamic banks in Bangladesh. Initially, a theoretical model was established based on detailed literature review. Afterwards, a self-administered structured questionnaire was developed, and data were collected from 254 Muslim customers of Islamic banks in Bangladesh using purposive sampling technique. The collected data were later analyzed through EFA. Structural Equation Modelling (SEM) technique was performed to verify the model of the study and testing the hypotheses. The findings of the study revealed that compliance with Shariah, assurance, reliability, tangibility, empathy, and responsiveness are significant factors for customer satisfaction and also customer satisfaction was integral for attaining customer loyalty. The study focused on Islamic banking customers only from two of the largest cities in the country, particularly, Dhaka and Chittagong. This, in turn, opens the door for future researchers to contribute to this area of research through employing a larger sample and extending the current model through the incorporation of new variables. The study will be a meaningful addition to the literature of service quality, customer satisfaction and loyalty, particularly in the setting of Islamic banking in Bangladesh. This study has taken the initiative for identifying the underlying factors that would facilitate Islamic banks in satisfying their customers better and building customer loyalty.

Application of Continuous Non-Gaussian Mortality Models with Markov Switchings to Forecast Mortality Rates

The ongoing pandemic has resulted in the development of models dealing with the rate of virus spread and the modelling of mortality rates μx,t. A new method of modelling the mortality rates μx,t with different time intervals of higher and lower dispersion has been proposed. The modelling was based on the Milevski–Promislov class of stochastic mortality models with Markov switches, in which excitations are modelled by second-order polynomials of results from a linear non-Gaussian filter. In contrast to literature models where switches are deterministic, the Markov switches are proposed in this approach, which seems to be a new idea. The obtained results confirm that in the time intervals with a higher dispersion of μx,t, the proposed method approximates the empirical data more accurately than the commonly used the Lee–Carter model.

Kombinasi Peran Model TAM dan CARTER Terhadap Optimalisasi Kepuasan Nasabah Mobile Syariah Banking di Masa Pandemi Covid-19

Background - During the covid-19 pandemic, Islamic banking companies are currently utilizing technology, namely mobile banking by providing easy, effective, and efficient services in banking access to growing customer satisfaction of Islamic banks. Purpose - To determine the combination of the role of the TAM and CARTER models in optimizing customer satisfaction for Mobile Syariah Banking in Malang City during the Covid-19 Pandemic. Design/ Methodology/ Approach – Using quantitative methods by distributing research questionnaires to 100 customers of mobile Syariah banking in Malang City. Using cluster sampling technique by selecting areas in Malang City covering five sub-districts in Malang City, namely Kedungsaatan, Sukun, Klojen, Blimbing, and Lowokwaru. Results and Discussion - Based on the results of research on sharia mobile banking customers in Malang City during the Covid-19 pandemic, the aspects of Usability and Reliability were prioritized over other aspects, but overall the aspects of Ease, Usability, Compliance, Assurance, and Reliability also affect customer satisfaction in using mobile Syariah banking in Malang City during the Covid-19 Pandemic. Conclusion - the use of Islamic banking mobile banking customers can feel satisfaction, namely with the use and reliability. By providing benefits in using mobile banking and good service to provide fast, complete, and sustainable transactions, customers can feel the satisfaction of using Islamic bank mobile banking. Research Limitations - Considering that the discussion about the theory of the influence of this research variable is very broad, this discussion will address the variables of convenience, usability, compliance, reliability, and assurance of customer satisfaction

A New Class of Counting Distributions Embedded in the Lee–Carter Model for Mortality Projections: A Bayesian Approach

The Lee–Carter model, the dominant mortality projection modeling in the literature, was criticized for its homoscedastic error assumption. This was corrected in extensions to the model based on the assumption that the number of deaths follows Poisson or negative binomial distributions. We propose a new class of families of counting distributions, namely, the ABM class, which belongs to a wider class of natural exponential families. This class is characterized by its variance functions and contains the Poisson and the negative binomial distributions as special cases, offering an infinite class of additional counting distributions to be considered. We are guided by the principle that the choice of distribution should be made from a pool of distributions as large as possible. To this end, and following a data mining approach, a training set of historical mortality data of the population could be modeled using the ABM’s rich choice of distributions, and the chosen distribution should be the one that proved to offer superior projection results on a test set of mortality data. As an alternative to parameter estimation via the singular value decomposition used in the classical Lee–Carter model, we adopted Bayesian estimation, harnessing the Markov Chain Monte Carlo methodology. A numerical study demonstrates that when fitting mortality data using this new class of distributions, while traditional distributions may provide desirable projections for some populations, for others, alternative distributions within the ABM class can potentially produce superior results for the entire population or particular age groups, such as the oldest-old.

Catastrophe risk can accelerate unlikely evolutionary transitions.

Intelligent life has emerged late in Earth’s habitable lifetime, and required a preceding series of key evolutionary transitions. A simple model (the Carter model) explains the late arrival of intelligent life by positing these evolutionary transitions were exceptionally unlikely ‘critical steps’. An alternative model (the neocatastrophism hypothesis) proposes that intelligent life was delayed by frequent catastrophes that served to set back evolutionary innovation. Here, we generalize the Carter model and explore this hypothesis by including catastrophes that can ‘undo’ an evolutionary transition. Introducing catastrophes or evolutionary dead ends can create situations in which critical steps occur rapidly or in clusters, suggesting that past estimates of the number of critical steps could be underestimated. If catastrophes affect complex life more than simple life, the critical steps will also exhibit a pattern of acceleration towards the present, suggesting that the increase in biological complexity over the past 500 Myr could reflect previously overlooked evolutionary transitions. Furthermore, our results have implications for understanding the different explanations (critical steps versus neo-catastrophes) for the evolution of intelligent life and the so-called Fermi paradox—the observation that intelligent life appears rare in the observable Universe.

Catastrophic overwash and rapid retreat of a gravel barrier spit during storm events (Sillon de Talbert, North Brittany, France)

AbstractThe morphodynamic functioning of the Sillon de Talbert gravel barrier spit is analysed using a high‐frequency survey carried out between September 2012 and December 2019. It is based on beach profile measurements along two transects, modelling offshore wave data (WW3), tide gauge records, and shallow waves and water levels recorded in the intertidal zone. A barrier retreat of −23 to −30 m over the 7‐year survey (i.e. −3.3 to −4.3 m yr−1) is measured. This retreat is not related to long‐term sea level rise (macroscale of 102–103 yr), but to mesoscale (100–102 yr) morphogenic events combining storm wave and high spring tide. Over 87–90% of the barrier retreat is due to three significant events (1–2 February 2014, 9 February 2016, and 3 January 2018). The storm impact scale model of Orford and Carter is tested. The estimation of the wave runup for the calculation of extreme water levels [i.e. peak overflow elevation (Oe) component] is based on the calibration of an equation performed from in‐situ measurements of the swash elevation. The flow depth (Od,q) overtopping the crest of the barrier (Bh) is thresholded by taking into account the morphological response of the barrier in order to define regimes corresponding to overtopping, discrete overwash, and sluicing overwash. While the Orford and Carter model is generally successful in reproducing the morphodynamic evolution of Sillon de Talbert, the wave energy flux (F) must be considered as an additional parameter in order to improve the fit of the model, so far as it contributes in some cases to change the morphodynamic regime. Thus, the wave energy flux constitutes a key component in the quantification of the water flow across the barrier (Od,q) corresponding to the hydrodynamic forcing of the model, which becomes (Od,F).

The Influence of Service Quality on Customer Satisfaction in Islamic Rural Banks during the Covid-19 Pandemic: A CARTER Model

The Covid-19 virus has become a global pandemic and gave an adverse impact on various sectors including the financial sector. Islamic Rural Bank (BPRS) as a provider of financial services plays an essential to carry out the function of collecting and distributing funds for the economic development of the community. However, with the spread of the Covid-19 virus, BPRS is obliged to limit its services. This study aimed to investigate empirically the effect of service quality on customer satisfaction of BPRS Bangka Belitung during the Covid-19 period by utilizing the CARTER model consisting by six dimensions (compliance, assurance, reliability, tangibles, empathy, and responsiveness). The research method applied is the quantitative method using Multiple Regression Analysis as the technique of data analysis. Data was collected by distributing questionnaires and applying the Likert Scale as a variable measurement scale. A total of 77 questionnaires were collected using the purposive sampling technique with the characteristics of the respondents, namely customers who had used the services of BPRS Bangka Belitung during the Covid-19 pandemic. The results indicating that the dimensions of compliance and responsiveness influence significantly customer satisfaction during the Covid-19 pandemic, while the other dimensions show no significant effect on customer satisfaction.

Mortality trends and projections in Hong Kong

Hong Kong has experienced a steady decline in mortality during the past several decades, and Hong Kong now life expectancy are among the highest in the world. Given the very low mortality already achieved, is there much more room for further improvement in Hong Kong's mortality decline? This paper examined how age-specific mortality by sex in Hong Kong has changed over time, and applied the Lee–Carter model to mortality projections in Hong Kong for next 50 years. When using Lee–Carter model, we dealt with the possible divergence in mortality projection for males and females over time, and take a shorter projection base period to meet the assumption of the parameters describing the age pattern of mortality decline being invariant. If we compare the results with the most recent official forecasts, projections on mortality decline in this paper are more optimistic than the official forecasts.

POINT AND INTERVAL FORECASTS OF DEATH RATES USING NEURAL NETWORKS

AbstractThe Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.