Local Level Model Research Articles

Junior High School Need For Students’ Workbook Based on Local Wisdom in Merdeka Curriculum

This study aims to explore the need for students' workbooks based on local wisdom in the Merdeka Curriculum for junior high school students, particularly at SMPN 3 Air Besar. The study is motivated by the importance of local wisdom in shaping students' character and identity, as well as the need for appropriate teaching materials that can support the implementation of the Merdeka Curriculum. This study employed a survey research design to elicit information. To identify the students' needs, data was collected through questionnaires, interviews, and direct observations. The interviews were conducted with students, English teachers, and villagers to gather in-depth information about the students' needs in the materials design. The study proposes various positions for EFL teachers to perform, including visionaries, cultural mediators on a local level, intercultural educators, and role model practitioners. It offers strategies for EFL teachers to include local cultural elements into their classroom instruction, such as establishing a local culturally responsive pedagogy, cultural sensitivity and awareness, and leveraging technology and information to support teaching. Incorporating local culture into EFL teaching can benefit students by raising cultural awareness, developing cultural literacy, building intercultural competence, enhancing language learning experiences, and acquiring skills in eliciting meaning. By using a workbook that takes into account the specific requirements of the students, teachers can better address their learning needs and create a more effective and engaging learning environment. It is recommended that English language teachers utilize the workbook that is based on local wisdom, as it aligns with the Indonesian National Educational System and the current Merdeka Curriculum, which emphasizes the relation between teaching English and the learners' closest environment for meaningful language use. Overall, the study focuses on exploring the need for a supplementary workbook that integrates local wisdom and culture into English language teaching, aiming to enhance students' language proficiency and cross-cultural communication skills.

An epidemiological modeling framework to inform institutional-level response to infectious disease outbreaks: a Covid-19 case study

Institutions have an enhanced ability to implement tailored mitigation measures during infectious disease outbreaks. However, macro-level predictive models are inefficient for guiding institutional decision-making due to uncertainty in local-level model input parameters. We present an institutional-level modeling toolkit used to inform prediction, resource procurement and allocation, and policy implementation at Clemson University throughout the Covid-19 pandemic. Through incorporating real-time estimation of disease surveillance and epidemiological measures based on institutional data, we argue this approach helps minimize uncertainties in input parameters presented in the broader literature and increases prediction accuracy. We demonstrate this through case studies at Clemson and other university settings during the Omicron BA.1 and BA.4/BA.5 variant surges. The input parameters of our toolkit are easily adaptable to other institutional settings during future health emergencies. This methodological approach has potential to improve public health response through increasing the capability of institutions to make data-informed decisions that better prioritize the health and safety of their communities while minimizing operational disruptions.

Evaluating swine disease occurrence on farms using the state-space model based on meat inspection data: a time-series analysis

BackgroundData on abnormal health conditions in animals obtained from slaughter inspection are important for identifying problems in fattening management. However, methods to objectively evaluate diseases on farms using inspection data has not yet been well established. It is important to assess fattening management on farms using data obtained from slaughter inspection. In this study, we developed the state-space model to evaluate swine morbidity using slaughter inspection data.ResultsThe most appropriate model for each disease was constructed using the state-space model. Data on 11 diseases in slaughterhouses over the past 4 years were used to build the model. The model was validated using data from 14 farms. The local-level model (the simplest model) was the best model for all diseases. We found that the analysis of slaughter data using the state-space model could construct a model with greater accuracy and flexibility than the ARIMA model. In this study, no seasonality or trend model was selected for any disease. It is thought that models with seasonality were not selected because diseases in swine shipped to slaughterhouses were the result of illness at some point during the 6-month fattening period between birth and shipment.ConclusionEvaluation of previous diseases helps with the objective understanding of problems in fattening management. We believe that clarifying how farms manage fattening of their pigs will lead to improved farm profits. In that respect, it is important to use slaughterhouse data for fattening evaluation, and it is extremely useful to use mathematical models for slaughterhouse data. However, in this research, the model was constructed on the assumption of normality and linearity. In the future, we believe that we can build a more accurate model by considering models that assume non-normality and non-linearity.

Highly irregular serial correlation tests

Tests are developed for neglected serial correlation when the information matrix is repeatedly singular under the null hypothesis. Specifically, consideration is given to white noise against a multiplicative seasonal Ar model, and a local-level model against a nesting Ucarima one. The proposed tests, which involve higher-order derivatives, are asymptotically equivalent to the likelihood ratio test but only require estimation under the null. It is shown that the tests effectively check that certain autocorrelations of the observations are zero, so their asymptotic distribution is standard. Monte Carlo exercises examine finite sample size and power properties, with comparisons made to alternative approaches.

Outliers detection in state-space model using indicator saturation approach

Structural changes that occur due to outliers may reduce the accuracy of an estimated time series model, shifting the mean distribution and causing forecast failure. This study used general-to-specific approach to detect outliers via indicator saturation approach in the local level model framework. Focusing on impulse indicator saturation, performance recorded by the suggested approach was evaluated using Monte Carlo simulations. To tackle the issue of higher number of regressors compared to the number of observations, this research utilized the split-half approach algorithm. We found that the impulse indicator saturation performance relies heavily on the size of outlier, location of outlier and number of splits in the series examined. Detection of outliers using sequential and non-sequential algorithms is the most crucial issue in this study. The sequential searching algorithm was able to outperform the non-sequential searching algorithm in eliminating the non-significant indicators based on potency and gauge. The outliers captured using impulse indicator saturation in financial times stock exchange (FTSE) United States of America (USA) shariah index correspond to the financial crisis in 2008-2009.

A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-sec NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.

METODE STATE SPACE DALAM MERAMALKAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA

State space is an approach to model and predict together several time series data that are interconnected, and these variables have dynamic interactions. The purpose of this research is to model the number of train passengers in Java and find out the forecasting results using the state space method. The algorithm used to solve the state space model is the Kalman filter. In this research, a suitable final model is local level model with seasonal and produces MAPE value of 2%, this shows that the state space method is very accurately.

Outlier Detection in Local Level Model: Impulse Indicator Saturation Approach

The existence of outliers in financial time series may affect the estimation of economic indicators. Detection of outliers in structural time series framework by using indicator saturation approach has become our main interest in this study. The reference model used is local level model. We apply Monte Carlo simulations to assess the performance of impulse indicator saturation for detecting additive outliers in the reference model. It is found that the significance level, α = 0.001 (tiny) outperformed the other target size in detecting various size of additive outliers. Further, we apply the impulse indicator saturation to detection of outliers in FTSE Bursa Malaysia Emas (FBMEMAS) index. We discover that there were 14 outliers identified corresponding to several economic and financial events.

Nearly weighted risk minimal unbiased estimation

Consider a small-sample parametric estimation problem, such as the estimation of the coefficient in a Gaussian AR(1). We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We also apply our generic approach to the median unbiased estimation of the degree of time variation in a Gaussian local-level model, and to a quantile unbiased point forecast for a Gaussian AR(1) process.

The Gibbs sampler with particle efficient importance sampling for state-space models*

We consider Particle Gibbs (PG) for Bayesian analysis of non-linear non-Gaussian state-space models. As a Monte Carlo (MC) approximation of the Gibbs procedure, PG uses sequential MC (SMC) importance sampling inside the Gibbs to update the latent states. We propose to combine PG with the Particle Efficient Importance Sampling (PEIS). By using SMC sampling densities which are approximately globally fully adapted to the targeted density of the states, PEIS can substantially improve the simulation efficiency of the PG relative to existing PG implementations. The efficiency gains are illustrated in PG applications to a non-linear local-level model and stochastic volatility models.

From Tobacco to Obesity Prevention Policies: A Framework for Implementing Community-Driven Policy Change.

Efforts to reverse the obesity epidemic require policy, systems, and environmental (PSE) change strategies. Despite the availability of evidence-based and other promising PSE interventions, limited evidence exists on the "how-to" of transitioning them into practice. For the past 13 years, the Los Angeles County Department of Public Health has been building capacity among community residents and other stakeholders to create effective community coalitions and to implement well-designed policy strategy campaigns using an evidence-based approach to policy change, the policy adoption model (PAM). Implementing a phase-based approach to policy change, the PAM was initially used to support the passage of over 140 tobacco control and prevention policies in Los Angeles County. Following these successes, Los Angeles County Department of Public Health applied the PAM to obesity prevention, operationalizing the policy process by training community residents and other stakeholders on the use of the model. The PAM has shown to be helpful in promoting PSE change in tobacco control and obesity prevention, suggesting a local-level model potentially applicable to other fields of public health seeking sustainable, community-driven policy change.

Numerical and experimental analysis of residual stresses in an arc welded lap joint and mapping of the residual stress field to a simplified finite element model

In this paper, welding residual stresses in an arc welded lap joint are evaluated by Finite element (FE) analysis of a continuum model considering Transformation induced plasticity (TRIP). The constitutive model for the plastic behavior including TRIP is implemented in user material subroutine (UMAT) of Abaqus/Standard. Numerical results are compared with experimentally measured residual stresses by X-ray diffraction technique. Moreover, the welding residual stresses in a continuum FE model are mapped to a simplified FE model using shell elements by moving least square approximation. The methodologies and results in this study show that the welding residual stresses in a local-level FE model of a welded joint can be mapped successfully to a simplified FE model.

Numerical analysis and model development for laminar flame speed of stratified methane/air mixtures

Numerical simulations of stratified methane/air flames with various stratification configurations are conducted using an 1-D unsteady reacting flow solver. Among all the stratified flame cases investigated, rich-to-lean stratified flames show significant departures from homogeneous flames, i.e., up to 50% increase in fuel consumption speeds, primarily due to preferential diffusion of lighter species and radicals from rich burnt products. A sensitivity study of transport properties further reveals that preferential diffusion of molecular hydrogen H2 along with radicals H, OH is the dominant factor in increasing stratified flame speeds, compared to the influence of heat diffusion and diffusion of H2O. Larger departure of stratified flames from homogeneous flames is observed in those cases with higher degree of stratification. In order to model transient behaviors of stratified flames with arbitrary stratification configurations, a local stratification level (LSL) model is proposed. LSL incorporates the effect of preferential diffusion by introducing a transfer function from molecular hydrogen concentration gradients to equivalence ratio gradients, and the memory effect by solving the transient model equation of LSL. The model results match well with directly simulated results quantitatively with error less than ∼10% for both lean and rich conditions.

Interweaving Markov Chain Monte Carlo Strategies for Efficient Estimation of Dynamic Linear Models

ABSTRACTIn dynamic linear models (DLMs) with unknown fixed parameters, a standard Markov chain Monte Carlo (MCMC) sampling strategy is to alternate sampling of latent states conditional on fixed parameters and sampling of fixed parameters conditional on latent states. In some regions of the parameter space, this standard data augmentation (DA) algorithm can be inefficient. To improve efficiency, we apply the interweaving strategies of Yu and Meng to DLMs. For this, we introduce three novel alternative DAs for DLMs: the scaled errors, wrongly scaled errors, and wrongly scaled disturbances. With the latent states and the less well known scaled disturbances, this yields five unique DAs to employ in MCMC algorithms. Each DA implies a unique MCMC sampling strategy and they can be combined into interweaving and alternating strategies that improve MCMC efficiency. We assess these strategies using the local level model and demonstrate that several strategies improve efficiency relative to the standard approach and the most efficient strategy interweaves the scaled errors and scaled disturbances. Supplementary materials are available online for this article.

Price, it's a GAS! Modelling Ultra-High-Frequency Covariance Dynamics with an Observation-Driven Approach

The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant data reduction and may severely affect the estimation of the covariances if traditional methods for low-frequency data are employed. We propose to model intraday log-prices through a multivariate local-level model with score-driven covariance matrices and to treat asynchronicity as a missing value problem. The main advantages of this approach are: (i) all available data are used when filtering the covariances, (ii) market microstructure noise is taken into account, (iii) estimation is performed by standard maximum likelihood. Our empirical analysis, performed on 1-second NYSE data, shows that opening hours are dominated by idiosyncratic risk and that a market factor progressively emerges in the second part of the day. The method can be used as a nowcasting tool for high-frequency data, allowing to study the real-time response of covariances to macro-news announcements and to build intraday portfolios with very short optimization horizons.

State space versus SARIMA modeling of the Nigeria’s crude oil export

This paper analyzes the Nigeria’s crude oil export series using monthly data from January 1999 to December 2014. We employed the state space local level model with stochastic and deterministic seasonal to model the dynamic features in the Nigeria crude oil export. Our results clearly indicate that the local level model with deterministic seasonal is the most parsimonious model between the two state space models considered in this study. Also, a parsimonious SARIMA model is also fitted to the data. We compare the forecasting performance of the two parsimonious models and evaluate their forecasts using ex-post indicators such as mean absolute percentage error (MAPE), root mean square percentage error (RMSPE) and the Theil’s U statistic. The forecast analysis and evaluation results indicate that the state space local level model with deterministic seasonal outperforms the Box-Jenkins model in shorter and medium – range forecasting horizons. Howbeit, the forecast of the SARIMA model improves in the longer horizon. The Theil’s U statistic also indicates that the state space local level model with deterministic seasonal and SARIMA model outperform the naive model at most of the forecasting horizons. In conclusion, we recommend that the state space model with deterministic seasonal component should be used in shorter and medium range forecasting horizons of the Nigeria’s monthly crude oil export. Howbeit, for longer forecasting horizon, ten months and above, the seasonal ARIMA model should be considered.

Measuring productivity and efficiency: a Kalman filter approach

In the Kalman filter setting, one can model the inefficiency term of the standard stochastic frontier composed error as an unobserved state. In this study a panel data version of the local level model is used for estimating time-varying efficiencies of firms. We apply the Kalman filter to estimate average efficiencies of U.S. airlines and find that the technical efficiency of these carriers did not improve during the period 1999-2009. During this period the industry incurred substantial losses, and the efficiency gains from reorganized networks, code-sharing arrangements, and other best business practices apparently had already been realized.

News, volatility and jumps: the case of natural gas futures

We investigate the impact of news sentiment on the price dynamics of natural gas futures. We propose a Local News Sentiment Level model, based on the Local Level model of Durbin and Koopman [Time Series Analysis by State Space Methods, 2001], to construct a running series of news sentiment from irregularly observed news items’ sentiments. We construct several return and variation measures to proxy for the fine dynamics of the natural gas futures prices. We employ event studies, Granger causality tests and several state-of-the-art volatility models to assess the effect of news on the returns, price jumps and the volatility. We find significant relationships between news sentiment and the dynamic characteristics of natural gas futures prices. Our findings are, among others, that the arrival of news in non-trading periods causes overnight returns, that news sentiment is Granger caused by negative semi-volatility and that news sentiment is more sensitive to negative than to positive jumps. In addition to that we find strong evidence that news sentiment severely Granger causes price jumps and conclude that market participants trade futures as some function of aggregated news. We augment volatility models with news sentiment measures and conduct an out-of-sample volatility forecasting study. The first class of models is the generalized autoregressive conditional heteroskedasticity models the second class is the high-frequency-based volatility models of Shephard and Shephard [J. Appl. Econ., 2010, 25, 197–231] and Noureldin et al. [J. Appl. Econ., 2012, 27(6), 907–933]. We adapt both models to account for asymmetric volatility, leverage and time to maturity effects. By augmenting all models with news sentiment variables, we find that including news sentiment in volatility models significantly improves volatility forecasts.

Chronic Illness and Disasters: Development of a Theoretical Framework

In a disaster, individuals with chronic illnesses risk poor outcomes. This grounded theory study sought to develop a framework that describes how these individuals manage health-related challenges during disasters. The five phases of disaster response (non-disaster, pre-disaster, impact, emergency, reconstruction) and the individual, local, state, and federal level model served as conceptual frameworks. Using purposive sampling, 30 individuals with chronic illnesses and 10 lay caregivers were recruited from Florida and New Orleans. Data sources included interviews and media data. Constant comparative analysis techniques were used to build the theoretical framework. Transcript analysis suggested that participants used four unique ways to shift priorities from illness to disaster-related challenges. Each way related to media impact, evacuation, preparation, attention, and recovery. If evacuated over a week, many could address some health-related concerns from afar. Those remaining home were more apt to ignore health-related concerns to deal with home and family issues.

On real interest rate persistence: the role of breaks

The role of structural breaks in long spans of ex-post real interest rates for 10 industrialized countries is studied. First, the persistence of the real interest is assessed with newly proposed low-frequency tests of Müller and Watson (2008). Second, the test of Leybourne et al. (2007) for a change in persistence of a time series is applied to the real interest rate. The results show that real interest rates over the full sample period do not fit a covariance-stationary or unit-root model, nor a fractionally integrated, near-unit-root or local-level model. Instead, the persistence of real rates changes over time and there are periods when the real rate is covariance-stationary and other periods when it follows a unit-root process.