Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

Analysis of Urban Residents’ Willingness to Pay for Forest Ecological Services Based on the Multilayer Linear Model

This study takes 8 cities in Shaanxi province as the research object and uses the multilayer linear model specifically for nested structure data to introduce the urban macroexplanatory variables on the basis of individual level of residents and influence the willingness of urban residents to pay for forest ecological services. The factors are analyzed in multiple layers to find out the prediction effect on ecological payment, and on this basis, corresponding countermeasures and suggestions are put forward. The results show that regional differences have a significant impact on residents’ willingness to pay for forest ecological services; individual characteristics and regional characteristics can independently have a significant impact on residents’ willingness to pay; after introducing macrolevel variables, individual-level environmental awareness and per capita income, five variables, such as education level, place of residence, and age, have significant predictive effects on residents’ willingness to pay; among them, the interaction between consumer price index and environmental awareness is the largest, followed by the interaction between consumer price index and age. Per capita social security is the interaction between expenditure and environmental awareness. Finally, that is the interaction between the per capita social security expenditure and age and the interaction between the average salary of employees and the monthly per capita income.

Two-year clinical and economic burden, risk and outcomes following application of software-assisted hexapod ring fixation systems

Background Multiplanar external fixation systems that employ software-assisted deformity correction consist of rings connected by angled struts, defined as hexapod ring fixators (HRF). Costs and outcomes associated with the application of HRFs are not well documented. This study was designed to provide a nationwide baseline understanding of the clinical presentation, risks, outcomes and payer costs, and healthcare resource utilization (HCU) of patients requiring application of an HRF, from the day of, and up to 2 years, post-application. Methods Patients with HRF application (“index”) between 2007 and 2019 within the IBM Marketscan® Commercial Claims database were identified and categorized based on diagnosis: acquired deformity, arthropathy, congenital deformity, deep infection, nonunion, fracture, and other post-operative fracture sequelae. Demographics, comorbidities at index, complications post-index, HCU, and payments were analyzed. Payments were estimated using a generalized linear model and were adjusted for inflation to the 2020 consumer price index. Rates of deep infection and amputation were estimated up to 2 years post-index using Poisson regressions, and risk factors for each were estimated using logistic regression models. Results Six hundred ninety-five patients were included in our study (including 219 fractures, 168 congenital deformities, 68 deep infections, 103 acquired deformities). Comorbidities at index were significantly different across groups: less than 2% pediatrics vs 18% adults had 3 or more comorbidities, < 1% pediatric vs 29% adults had diabetes. Index payments ranged from $39,250–$75,350, with 12-months post-index payments ranging from $14,350 to $43,108. The duration of the HRF application ranged from 96 days to 174 days. Amputation was observed in patients with deep infection (8.9, 95% confidence interval (CI): 3.2–23.9%), nonunion (5.0, 95%CI: 1.6–15.4%) or fracture (2.7, 95%CI: 0.9–7.6%) at index. Complicated diabetes was the main predictor for deep infection (odds ratio (OR): 5.14, 95%CI: 2.50–10.54) and amputation (OR: 5.26, 95%CI: 1.79–15.51). Conclusions Findings from this longitudinal analysis demonstrate the significant heterogeneity in patients treated with HRF, and the wide range in treatment intensity, payments, and outcomes. Risks for deep infection and amputation were primarily linked to the presence of complicated diabetes at the time of HRF application, suggesting a need for careful management of comorbid chronic conditions in patients requiring HRF for orthopedic care.

Forecasting and Technical Comparison of Inflation in Turkey With Box-Jenkins (ARIMA) Models and the Artificial Neural Network

Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.

Analysis of financial resources for public health in Brazilian capitals: a time trend ecological study

This study aimed to analyze part of the financial resources used to fund public health actions in the 26-Brazilian capitals, from 2008 to 2018. This is a time-trend ecological study involving revenue and expenditure indicators provided by the Information System on Public Budget for Health (SIOPS). The values were deflated based on the Extended National Consumer Price Index of 2018 in Brazil to allow the comparison over the years. The mean annual variation of health investments, in Brazilian Reais (BRL) was assessed using linear regressions. Pearson’s correlation coefficients were estimated between federal revenues and expenditures with the capitals’ resources. All capitals presented statistically significant positive correlations for the origin of the budget resource invested in health. The lowest coefficient was found in the capital city of Macapá (Amapá State) (r = 0.860) and the highest, in Fortaleza (Ceará State) (r = 0.997). Belo Horizonte (Minas Gerais State) was the capital with the highest annual increase in federal transfers (about BRL 67.91 per year) and Teresina (Piauí State) presented the highest annual increase in health expenditures among the capitals (about BRL 55.42 per year). We found a increase in the transfers of the Brazilian Unified National Health System (SUS) and municipal resources in almost all capitals, but there are still inequalities in the distribution of financial resources among Brazilian capitals from different regions. Health funding is affected by the municipalization of SUS and it is not the single factor affecting the access and quality of health services.

Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network

This study examines the performance of seasonal autoregressive integrated moving average (SARIMA), multilayer perceptron neural networks (MLPNN), and hybrid SARIMA-MLPNN model(s) in modelling and forecasting inflation rate using the monthly consumer price index (CPI) data from 2010 to 2019 obtained from the South African Reserve Bank (SARB). The forecast errors in inflation rate forecasting are analyzed and compared. The study employed root mean squared error (RMSE) and mean absolute error (MAE) as performance measures. The results indicate that significant improvements in forecasting accuracy are obtained with the hybrid model (SARIMA-MLPNN) compared to the SARIMA and MLPNN. The MLPNN model outperformed the SARIMA model. However, the hybrid SARIMA-MLPNN model outperformed both the SARIMA and MLPNN in terms of forecasting accuracy/accuracy performance.

FORECASTING THE CONSUMER PRICE INDEX IN YOGYAKARTA BY USING THE DOUBLE EXPONENTIAL SMOOTHING METHOD

The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

DETERMINANTS OF PERSONAL BANKRUPTCY DISCHARGE IN MALAYSIA: A SOCIAL CAPITAL APPROACH OF AL-NAHD AND TA’AWUN

This study investigates the relationship between Gross Domestic Product (GDP), Consumer Price Index (CPI), Unemployment Rate (UE), Interest rate (IR), Household Debt (HD) on bankruptcy discharge and test the application of an Islamic concept of mutual cooperation, Social Relief Fund (SRF) to enhance the bankruptcy discharge. The study period is from 2000 to 2019. The autoregressive distributed lag (ARDL) was used in the study. Two models were tested; Model 1 consists of the macroeconomic variables without SRF and Model 2, with SRF. Model 1 shows none of the variables has significant effect on bankruptcy discharge for long run relationship. However, Model 2 shows GDP, CPI and SRF have significant positive long run relationship with bankruptcy discharge. This provides statistical evidence that SRF has a beneficial long-run relationship to enhance bankruptcy discharge in Malaysia. For short run relationship, Model 1 reveals GDP, CPI, and UE as significant variables to discharge. Model 2 shows stronger short run relationships in which GDP, IR, HD, SRF are positive and CPI is negative to bankruptcy discharge. These variables are significant at 1 percent level. The findings contribute new knowledge on determinants of bankruptcy discharge in Malaysia. The study provides empirical evidence that SRF is a potential component as a social safety net in providing financial assistance among distressed debtors from bankruptcy. We recommend the use of SRF as the current bankruptcy reform is being viewed from the legislative lens and lacks the social capital component to assist debtors achieve financial restitution.

Macroeconomic Factors Determining the Growth of Pakistan

This study attempts to explore the relation ofExternal Debt, Terms of Trade, Education,Military expenditures, Consumer Price Index, and GrossDomestic Production of Pakistan throughout 1997-2019. Toestimate the targeted objectives of this research AutoRegressive Distributive Lags (ARDL) technique was used. Theresults revealed some facts that Military expenditures andeducation were essential to achieve the goal of the high growthrate of the economy in the form of Gross Domestic Production.So policymakers should adopt strong strategies. Educationshould be as skilled and technical as possible and producemilitary equipment to save foreign exchange. CPI, TOT, andExd should be properly regulated because of their negativeimpact on GDP. CPI affects the people, so fiscal policy shouldbe adopted. Without external debt, governments feel helpless,so breaking this trap is essential for dignity and development.The model has a dampening convergence towards equilibrium