MODELING AND FORECASTING OF INDIA’S DEFENSE EXPENDITURES USING BOX-JENKINS ARIMA MODEL

Many developed and developing countries are at the core of the security and peace agenda concerning rising defense expenditure and its enduring sustainability. The unremitting upsurge in defense expenditure pressurizes the government to rationally manage the resources so as to provide security and peace services in the most efficient, effective and equitable way. It is necessary to forecast the defense expenditure in India which leads the policy makers to execute reforms in order to detract burdens on these resources, as well as introduce appropriate plan strategies on the basis of rational decision making for the issues that may arise. The purpose of this study is to investigate the appropriate type of model based on the Box–Jenkins methodology to forecast defense expenditure in India. The present study applies the one-step ahead forecasting method for annual data over the period 1961 to 2020. The results show that ARIMA (1,1,1) model with static forecasting being the most appropriate to forecast the India’s defense expenditure.

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The Devil Is in the Detail—Understanding Divergence between Intention and Implementation of Health Policy for Undocumented Migrants in Thailand

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16061016 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1016 ◽

Cited By ~ 7

Author(s):

Rapeepong Suphanchaimat ◽

Nareerut Pudpong ◽

Phusit Prakongsai ◽

Weerasak Putthasri ◽

Johanna Hanefeld ◽

...

Keyword(s):

Insurance Coverage ◽

Undocumented Migrants ◽

Framework Analysis ◽

Policy Makers ◽

One Stop ◽

Policy Objectives ◽

Insurance Card ◽

The Government ◽

The One ◽

The Devil

Migrants’ access to healthcare has attracted attention from policy makers in Thailand for many years. The most relevant policies have been (i) the Health Insurance Card Scheme (HICS) and (ii) the One Stop Service (OSS) registration measure, targeting undocumented migrants from neighbouring countries. This study sought to examine gaps and dissonance between de jure policy intention and de facto implementation through qualitative methods. In-depth interviews with policy makers and local implementers and document reviews of migrant-related laws and regulations were undertaken. Framework analysis with inductive and deductive coding was undertaken. Ranong province was chosen as the study area as it had the largest proportion of migrants. Though the government required undocumented migrants to buy the insurance card and undertake nationality verification (NV) through the OSS, in reality a large number of migrants were left uninsured and the NV made limited progress. Unclear policy messages, bureaucratic hurdles, and inadequate inter-ministerial coordination were key challenges. Some frontline implementers adapted the policies to cope with their routine problems resulting in divergence from the initial policy objectives. The study highlighted that though Thailand has been recognized for its success in expanding insurance coverage to undocumented migrants, there were still unsolved operational challenges. To tackle these, in the short term the government should resolve policy ambiguities and promote inter-ministerial coordination. In the long-term the government should explore the feasibility of facilitating lawful cross-border travel and streamlining health system functions between Thailand and its neighbours.

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Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

BMC Infectious Diseases ◽

10.1186/s12879-021-06503-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Cai-Xia Lv ◽

Shu-Yi An ◽

Bao-Jun Qiao ◽

Wei Wu

Keyword(s):

Prediction Model ◽

Prediction Models ◽

Arima Model ◽

Mainland China ◽

Hemorrhagic Fever ◽

Prediction Performance ◽

Forecasting Model ◽

Incidence Data ◽

One Step ◽

The One

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

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DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

Agrienvi, Jurnal Ilmu Pertanian ◽

10.36873/aev.v13i02.723 ◽

2020 ◽

Vol 13 (02) ◽

pp. 1-8

Author(s):

Agrienvi

Keyword(s):

Time Series ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Production Data ◽

Policy Makers ◽

Strategic Value ◽

Modeling And Forecasting ◽

The Mean ◽

Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

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Fiskalischer Föderalismus und Staatstätigkeit

Zeitschrift für Wirtschaftspolitik ◽

10.1515/zfwp-2003-0106 ◽

2003 ◽

Vol 52 (1) ◽

Cited By ~ 3

Author(s):

Christoph A. Schaltegger

Keyword(s):

Economies Of Scale ◽

Point Of View ◽

Annual Data ◽

Theoretical Point ◽

Size Of Government ◽

Policy Makers ◽

Voter Preferences ◽

Swiss Cantons ◽

The One ◽

The Impact

AbstractWhat is the impact of federalism on the size of government? On the one hand, various externalities and unexploited economies of scale caused by federalist institutions favour a larger size of government. On the other hand, some authors argue that federalism represents an institutional barrier for policy makers eventually restricting their policy discretion to deviate from voter preferences. Consequently, the net effect of federalism is open from a theoretical point of view. This paper evaluates the different aspects of federalism empirically for Swiss cantons with a panel of annual data from 1980 to 1998. The results indicate that federalism in Switzerland rather favours a smaller size of government.

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Time Series Analysis of Hemorrhagic Fever with Renal Syndrome in Mainland China by using XGBoost Forecasting Model

10.21203/rs.3.rs-107730/v1 ◽

2020 ◽

Author(s):

Cai-Xia Lv ◽

Shu-Yi An ◽

Bao-Jun Qiao ◽

Wei Wu

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Prediction Models ◽

Arima Model ◽

Mainland China ◽

Hemorrhagic Fever ◽

Public Attention ◽

Incidence Data ◽

One Step ◽

The One

Abstract Background: Hemorrhagic fever with renal syndrome is still attracting public attention because of its outbreak in various cities in China. It is one of the effective preventive measures to predict the peak incidence rate in the future based on the past incidence data, and implement targeted actions. In this study, we propose a multi-step prediction strategy based on XGBoost for hemorrhagic fever with renal syndrome as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of XGBoost model will be compared with seasonal ARIMA model by different evaluation indicators. Methods: We collected monthly hemorrhagic fever with renal syndrome incidence data from 2004 to 2018 in mainland China .The part from 2004 to 2017 was divided as training set to establish the seasonal ARIMA model and XGBoost model. The rest 2018 data was used to test the prediction outcomes. In multi-step forecasting XGBoost model, one-hot encoding was used to handle seasonal features. Furthermore, series of evaluation index(MAE,MPE,MAPE,RMSE,MASE, ACF1, Theil’s U) were performed to evaluate the accuracy of multi-step forecast XGBoost model.Results: There were totally 200237 HFRS cases in China from 2004 to 2018. A slightly long-term downward trend and obvious bimodal peak seasonal character were identified in the original time series. According to the minimum CAIC value, the optimal ARIMA (3,1,0) × (1,1,0)12 model is selected. the ME , MAE, MPE, MAPE, MASE of XGBoost were higher than ARIMA model in the fitting part, whereas the RMSE of XGBoost was lower. The evaluation indicators (MAE, MPE, MAPE, RMSE , MASE) of the one-step prediction and multi-step prediction XGBoost model are all notably lower than the ARIMA model in prediction performance. Conclusions: The multi-step prediction XGBoost model showed a much better prediction accuracy and model stability in HFRS disease . In general, compared to the seasonal ARIMA model, the XGBoost model performs better when predicting complicated and non-linear data like Hemorrhagic fever with renal syndrome. Additionally Multi-step prediction models are more practical than one-step prediction in forecasting infectious diseases.

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A review of the One Health concept and its application as a tool for policy-makers

International Journal of One Health ◽

10.14202/ijoh.2020.83-89 ◽

2020 ◽

Vol 6 (1) ◽

pp. 83-89

Author(s):

Rodrigo de Macedo Couto ◽

Danie Friguglietti Brandespim

Keyword(s):

Public Health ◽

Interpersonal Violence ◽

One Health ◽

Current Knowledge ◽

Health Policies ◽

Policy Makers ◽

Public Health Policies ◽

Health Concept ◽

The Government ◽

The One

The One Health concept represents the inseparability of human, animal, and environmental health through a unified view of health care. This article addressed the topic of public health policies from the One Health perspective, demonstrating its inclusion in various health agendas such as emerging and reemerging infectious diseases, basic sanitation, mental health, chronic non-communicable diseases, interpersonal violence, and food safety. The results showed that the application of the One Health concept to the development and implementation of policies is associated with a growing need to involve transdisciplinary teams for solving complex problems to improve communication and to ensure the relevance and acceptability of public policies, thus guaranteeing governance. According to the principle of efficiency, the government must be aware of the evolution of technical knowledge and should use the One Health approach to improve the efficacy of already existing systems. We, therefore, conducted this review to contextualize current knowledge in this topic which is becoming an essential tool for public health policy-makers and practitioners around the world promoting a reflection on the importance of multiprofessional articulation in the implementation of intersectoral public health policies.

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The Relationship between Economic Growth and Misery Index: Evidence from Transition Countries

International Conference on Eurasian Economies 2020 ◽

10.36880/c12.02359 ◽

2020 ◽

Author(s):

Ebru Çağlayan Akay ◽

Zamira Oskonbaeva

Keyword(s):

Economic Growth ◽

Transition Countries ◽

Annual Data ◽

Policy Makers ◽

Panel Ardl ◽

Long Run ◽

Misery Index ◽

The Government ◽

Main Components

Unemployment and inflation, the main components of the misery index, continue to be vital macroeconomic problems, which draw researchers’ attention both in developed and developing countries. The study investigates the interaction among economic growth and misery index in the selected transition countries using Panel ARDL. In the study, annual data for the period of 1996-2017 of selected 16 transition countries are used. The findings of the study show that there is a long-run relationship between the misery index and economic growth. In other words, it can be concluded that economic misery deteriorates economic growth. If the economy is to be sustainably improved, the misery index should be taken into account. The government needs a policy of decreasing inflation and unemployment, which is one of the fundamental macroeconomic policy priorities. This study may provide policy-makers with new insights to evaluate the role of economic misery in enhancing economic growth in transition countries.

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PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH

Jurnal Gaussian ◽

10.14710/j.gauss.v8i2.26648 ◽

2019 ◽

Vol 8 (2) ◽

pp. 194-207

Author(s):

Riski Arum Pitaloka ◽

Sugito Sugito ◽

Rita Rahmawati

Keyword(s):

Time Series ◽

Trade Balance ◽

Arima Model ◽

Ensemble Averaging ◽

High Increase ◽

Forecasting Accuracy ◽

Central Java ◽

Forecasting Method ◽

The Ensemble Method ◽

The Government

Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

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USING EXPONENTIAL SMOOTHING METHOD IN FORECASTING DOMESTIC CREDIT TO PRIVATE SECTOR OF GHANA

Journal on Innovation and Sustainability RISUS ISSN 2179-3565 ◽

10.23925/2179-3565.2019v10i3p66-74 ◽

2019 ◽

Vol 10 (3) ◽

Author(s):

William Obeng-Amponsah ◽

Sun Zehou ◽

Elias Augustine Dey

Keyword(s):

Private Sector ◽

Exponential Smoothing ◽

Smoothing Method ◽

Annual Data ◽

Best Interest ◽

Policy Makers ◽

Domestic Credit ◽

The Government ◽

Local Credit Market ◽

Exponential Smoothing Method

The private sector of Ghana faces many problems with respect to raising capital for their operations; this is largely due to government relying heavily on the local credit market for funds for developmental projects. This study uses exponential smoothing method (ESM) in EViews to build a single sample model to forecast future domestic credit to private sector (DCPS) values in Ghana. Secondary annual data on DCPS spanning the period from 1982 to 2016 is used. The findings show that an exponential smoothing model with multiplicative error, additive trend and no seasonality fits the data best. The model had very small residual measures, which demonstrates a good model for forecasting. The estimated model is used to forecast the DCPS values for Ghana from the year 2017 to 2020. The results of this study will help private business people plan for the future. The results will also help policy makers to make informed decisions and formulate policies to improve the DCPS figures, since the private sector is the engine of growth, and crowding out would not be in the best interest of the government and the nation as a whole.

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Frits Fahridws Damanik

Agrienvi, Jurnal Ilmu Pertanian ◽

10.36873/aev.v13i02.657 ◽

2020 ◽

Vol 13 (02) ◽

pp. 1-8

Author(s):

Agrienvi

Keyword(s):

Time Series ◽

Time Series Data ◽

Arima Model ◽

Series Data ◽

Production Data ◽

Policy Makers ◽

Strategic Value ◽

Modeling And Forecasting ◽

The Mean ◽

Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be appliedbased on the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in CentralKalimantan Province is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

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