Pub Date : 2021-04-16DOI: 10.14710/medstat.14.1.21-32
D. Nugroho, Agus Priyono, B. Susanto
The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.
{"title":"SKEW NORMAL AND SKEW STUDENT-T DISTRIBUTIONS ON GARCH(1,1) MODEL","authors":"D. Nugroho, Agus Priyono, B. Susanto","doi":"10.14710/medstat.14.1.21-32","DOIUrl":"https://doi.org/10.14710/medstat.14.1.21-32","url":null,"abstract":"The Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) type models have become important tools in financial application since their ability to estimate the volatility of financial time series data. In the empirical financial literature, the presence of skewness and heavy-tails have impacts on how well the GARCH-type models able to capture the financial market volatility sufficiently. This study estimates the volatility of financial asset returns based on the GARCH(1,1) model assuming Skew Normal and Skew Student-t distributions for the returns errors. The models are applied to daily returns of FTSE100 and IBEX35 stock indices from January 2000 to December 2017. The model parameters are estimated by using the Generalized Reduced Gradient Non-Linear method in Excel’s Solver and also the Adaptive Random Walk Metropolis method implemented in Matlab. The estimation results from fitting the models to real data demonstrate that Excel’s Solver is a promising way for estimating the parameters of the GARCH(1,1) models with non-Normal distribution, indicated by the accuracy of the estimation of Excel’s Solver. The fitting performance of models is evaluated by using log-likelihood ratio test and it indicates that the GARCH(1,1) model with Skew Student-t distribution provides the best fitting, followed by Student-t, Skew-Normal, and Normal distributions.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"14 1","pages":"21-32"},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47863105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.14710/medstat.13.2.136-148
Epha Diana Supandi
Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.
{"title":"STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS ON THE RELATIONSHIP BETWEEN RENUMERATION AND MOTIVATION ON EMPLOYEE PERFORMANCE AT UIN SUNAN KALIJAGA YOGYAKARTA","authors":"Epha Diana Supandi","doi":"10.14710/medstat.13.2.136-148","DOIUrl":"https://doi.org/10.14710/medstat.13.2.136-148","url":null,"abstract":"Structural equation modeling (SEM) is a multivariate statistical analysis technique that is used to analyze the structural relationships between observed variables and latent constructs. SEM has several methods one of which is Generalized Structured Component Analysis (GSCA). An empirical application concerning the relationship between renumeration and work motivation on employee performance is presented to illustrate the usefulness of the GSCA method. Data were collected by a questionnaire distributed to lecturers and staffs at UIN Sunan Kalijaga Yogyakarta. The result showed that the remuneration variable had a significant and positive impact on work motivation. Also, the work motivation variable had a significant and positive effect on employee performance.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43967288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.14710/medstat.13.2.149-160
Khaerun Nisa', I. Budiantara
Life expectancy is one of the indicators used to evaluate the government’s performance in improving the well-being of the population. High life expectancy in an area indicates that people in the area have been assured of health and poverty has been well overcome, and vice versa. Based on national socioeconomic survey (SUSENAS) data, showing life expectancy in East Java Province from 2009 to 2013 increased by 69.15 years to 70,19 years. Although overall life expectancy in East Java province has increased, there are still some areas that have life expectancy below 65 years. This is not from the different characteristics of each religion. Therefore, the main purpose of this study is to model life expectancy in East Java using semiparametric regression with a mixed estimator of Spline Truncated and Fourier Series. Based on the research that has been done, the results that modeling the data of life expectancy using mixed estimator of Spline Truncated and Fourier Series produced a value of R2 of 99,62% which means that the predictor variables are able to explain the response variabel life expectancy of 99.62%.
{"title":"MODELING EAST JAVA INDONESIA LIFE EXPECTANCY USING SEMIPARAMETRIC REGRESSION MIXED SPLINE TRUNCATED AND FOURIER SERIES","authors":"Khaerun Nisa', I. Budiantara","doi":"10.14710/medstat.13.2.149-160","DOIUrl":"https://doi.org/10.14710/medstat.13.2.149-160","url":null,"abstract":"Life expectancy is one of the indicators used to evaluate the government’s performance in improving the well-being of the population. High life expectancy in an area indicates that people in the area have been assured of health and poverty has been well overcome, and vice versa. Based on national socioeconomic survey (SUSENAS) data, showing life expectancy in East Java Province from 2009 to 2013 increased by 69.15 years to 70,19 years. Although overall life expectancy in East Java province has increased, there are still some areas that have life expectancy below 65 years. This is not from the different characteristics of each religion. Therefore, the main purpose of this study is to model life expectancy in East Java using semiparametric regression with a mixed estimator of Spline Truncated and Fourier Series. Based on the research that has been done, the results that modeling the data of life expectancy using mixed estimator of Spline Truncated and Fourier Series produced a value of R2 of 99,62% which means that the predictor variables are able to explain the response variabel life expectancy of 99.62%.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46250744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.14710/medstat.13.2.206-217
I. Setyowati, K. Notodiputro, A. Kurnia
In linear models, panel data often violates the assumption that the error terms should be independent. As a result, the estimated variance is usually large and the standard inferential methods are not appropriate. The previous research developed an inference method to solve this problem using a variance estimator namely the Heteroskedasticity Autocorrelation Consistent of the Cross-Section Averages (HACSC), with some improvements. The test statistic of this method converges to the fixed-b asymptotic distribution. In this paper, the performance of the proposed inferential method is evaluated by means of simulation and compared with the standard method using plm package in R. Several comparisons regarding the Type I Error of these two methods have been carried out. The results showed that the statistical inference based on fixed-b asymptotic distribution out-perform the standard method, especially for the panel data with small number of individual and time dimension.
{"title":"A SIMULATION STUDY OF FIXED-B ASYMPTOTIC DISTRIBUTIONS IN LINEAR PANEL MODELS WITH FIXED EFFECTS","authors":"I. Setyowati, K. Notodiputro, A. Kurnia","doi":"10.14710/medstat.13.2.206-217","DOIUrl":"https://doi.org/10.14710/medstat.13.2.206-217","url":null,"abstract":"In linear models, panel data often violates the assumption that the error terms should be independent. As a result, the estimated variance is usually large and the standard inferential methods are not appropriate. The previous research developed an inference method to solve this problem using a variance estimator namely the Heteroskedasticity Autocorrelation Consistent of the Cross-Section Averages (HACSC), with some improvements. The test statistic of this method converges to the fixed-b asymptotic distribution. In this paper, the performance of the proposed inferential method is evaluated by means of simulation and compared with the standard method using plm package in R. Several comparisons regarding the Type I Error of these two methods have been carried out. The results showed that the statistical inference based on fixed-b asymptotic distribution out-perform the standard method, especially for the panel data with small number of individual and time dimension.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46935144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.14710/medstat.13.2.218-224
S. Sugito, A. Prahutama
The transportation sector is one sector that plays an essential role in economic growth. The transportation sector can increase economic growth. Semarang City is one of the provincial capitals in Central Java. The Srondol-Jatingaleh toll road is one of the toll roads in the city of Semarang that has implemented the Automatic Toll Gate. Based on the results of the analysis, so that the queue model is (logistic/logistic/ 4) :( FIFO / ∞ / ∞). It shows that the distribution of the queuing system of the number of arrivals and the number of vehicle services are Logistic-Distribution. The number of service facilities is 4, the service discipline used is First In First Out (FIFO), the size in the queue, and the source of calls are unlimited.
{"title":"ANALYSIS OF SRONDOL-JATINGALEH TOLL QUEUE SYSTEM AT SEMARANG CITY IN THE END OF YEAR 2018 WITH AUTOMATIC TOLL GATE SYSTEM USING LOGISTIC DISTRIBUTION APPROACH","authors":"S. Sugito, A. Prahutama","doi":"10.14710/medstat.13.2.218-224","DOIUrl":"https://doi.org/10.14710/medstat.13.2.218-224","url":null,"abstract":"The transportation sector is one sector that plays an essential role in economic growth. The transportation sector can increase economic growth. Semarang City is one of the provincial capitals in Central Java. The Srondol-Jatingaleh toll road is one of the toll roads in the city of Semarang that has implemented the Automatic Toll Gate. Based on the results of the analysis, so that the queue model is (logistic/logistic/ 4) :( FIFO / ∞ / ∞). It shows that the distribution of the queuing system of the number of arrivals and the number of vehicle services are Logistic-Distribution. The number of service facilities is 4, the service discipline used is First In First Out (FIFO), the size in the queue, and the source of calls are unlimited.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41598561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-28DOI: 10.14710/medstat.13.2.125-135
Alfi Fadliana, H. Pramoedyo, Rahma Fitriani
East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).
{"title":"IMPLEMENTATION OF LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION MODEL IN SPATIAL DATA WITH MULTICOLLINEARITY PROBLEMS (Case Study: Stunting among Children Aged under Five Years in East Nusa Tenggara Province)","authors":"Alfi Fadliana, H. Pramoedyo, Rahma Fitriani","doi":"10.14710/medstat.13.2.125-135","DOIUrl":"https://doi.org/10.14710/medstat.13.2.125-135","url":null,"abstract":"East Nusa Tenggara Province, according to the findings of 2013 Baseline Health Research and 2016 and 2017 Nutritional Status Surveys, was recorded as the province with the highest prevalence of stunting in Indonesia. Efforts should be made to formulate policies that are integrated with spatial aspects in order to reduce the prevalence of stunting. The LCR-GWR model approach is used by using locally compensated ridge, which were meant to adjusts to the effect of collinearity between predictor variables (i.e., the factors affecting the prevalence of stunting) in each area. Results of the analysis showed that factors affecting the prevalence of stunting in all districts/cities in East Nusa Tenggara Province are the percentage of children aged under five who were weighed ≥ 4 times, the percentage of children aged under five who receive complete basic immunization, the percentage of households consuming iodized salt, the percentage of households with decent source of drinking water and the real per capita expenditure. The analysis showed that LCR-GWR is able to produce a better model than the GWR model in overcoming local multicollinearity problems in stunting in East Nusa Tenggara Province, with lower RMSE value (0.0344) than the GWR RMSE model (3.8899).","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43473931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-26DOI: 10.14710/medstat.13.1.13-24
Irwan Susanto, S. Handajani
In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., gamma, lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-lognormal mixture model.
{"title":"PENGELOMPOKAN RUMAH TANGGA DI INDONESIA BERDASARKAN PENDAPATAN PER KAPITA DENGAN MODEL FINITE MIXTURE","authors":"Irwan Susanto, S. Handajani","doi":"10.14710/medstat.13.1.13-24","DOIUrl":"https://doi.org/10.14710/medstat.13.1.13-24","url":null,"abstract":"In the statistical modeling framework, the form of the income distribution can be approaching based on certain statistical distributions. The use of the finite mixture model is relatively flexible in the modeling of the income distribution that has a multimodal pattern. The multimodal pattern can be indicated as the existence of different cluster on the data. The different clusters which can reflect the economic homogeneity of income are represented by the mixture components of the finite mixture model. In this paper, the finite mixture model is implemented for modeling the distribution of household income per capita in Indonesia based on The Fifth Wave of the Indonesia Family Life Survey (IFLS5) 2014-2015. The mixture components of the finite mixture model have been build based on the heavy-tailed statistical distributions, i.e., gamma, lognormal, and Weibull distributions. The estimation of the fitting finite mixture model was conducted using the maximum-likelihood estimation method through the expectation-maximization (EM) algorithm. The suitable finite mixture models were verified with the bootstrap likelihood ratio statistics test, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Based on the results, the distribution of household income per capita in Indonesia can be modeled by the four components-lognormal mixture model.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44657502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-19DOI: 10.14710/medstat.13.1.47-59
Dea Astri Titi, Heri Kuswanto, Suhartono Suhartono
Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.
根据BPS的数据，运输业对印尼经济增长的贡献率约为8.01%。运输业的快速增长也伴随着印尼汽车业的发展。2017年4月，Astra International集团的独家Lisencee代理商赢得了57%的市场份额。PT。Astra大发汽车是其子公司之一，其销量每年都有15%的快速增长，直到大发汽车的市场份额上升到17.3%。来自Gabungan Industri Kendaran Bermotor Indonesia（Gaijindo）的数据显示，在Idul Fitri之前的一个月，汽车销量呈上升趋势。本研究使用ARIMAX进行了大发公司的直接和间接市场份额预测，包括各种日历效应，包括趋势、月度季节效应和Idul-Fitri效应。结果表明，使用ARIMAX预测每个品牌和整个市场的汽车销量的间接预测优于其他预测，能够捕捉测试数据的模式。ARIMAX的SMAPE值小于使用ARIMA的直接预测和间接预测。
{"title":"PERAMALAN LANGSUNG DAN TIDAK LANGSUNG MARKET SHARE MOBIL MENGGUNAKAN ARIMAX DENGAN EFEK VARIASI KALENDER","authors":"Dea Astri Titi, Heri Kuswanto, Suhartono Suhartono","doi":"10.14710/medstat.13.1.47-59","DOIUrl":"https://doi.org/10.14710/medstat.13.1.47-59","url":null,"abstract":"Based on BPS data, the transportation industry sector contributed to about 8.01% of Indonesia's economic growth. The rapid growth of the transportation industry is also followed by the development of the automotive industry in Indonesia. The Exclusive Lisencee Agent of the Astra International group won a market share of 57% in April 2017. PT. Astra Daihatsu Motor, which is one of its subsidiaries, has a very rapid sales increase of 15% every year until Daihatsu's market share rises to 17.3%. Data from the Gabungan Industri Kendaraan Bermotor Indonesia (Gaikindo) shows an upward trend in car sales a month before Idul Fitri. This study carried out Daihatsu's direct and indirect market share forecasting using ARIMAX with a variety of calendar effects consisting of trends, monthly seasonal effects and Idul Fitri effects. The results indicated that indirect forecasting through forecasting the car sales for each brand and total market using ARIMAX outperforms the others and is able to capture the pattern of the testing data. The resulting SMAPE value of ARIMAX is smaller than direct forecasting and indirect forecasting using ARIMA.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/medstat.13.1.47-59","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46346274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-19DOI: 10.14710/medstat.13.1.1-12
Taly Purwa, Ulin Nafngiyana, S. Suhartono
The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.
{"title":"COMPARISON OF ARIMA, TRANSFER FUNCTION AND VAR MODELS FOR FORECASTING CPI, STOCK PRICES, AND INDONESIAN EXCHANGE RATE: ACCURACY VS. EXPLAINABILITY","authors":"Taly Purwa, Ulin Nafngiyana, S. Suhartono","doi":"10.14710/medstat.13.1.1-12","DOIUrl":"https://doi.org/10.14710/medstat.13.1.1-12","url":null,"abstract":"The Consumer Price Index (CPI), stock prices and the rupiah exchange rate to the US dollar are important macroeconomic variables which their movements show the economic performance and can affect the monetary and fiscal policies of Indonesia. This makes forecasting effort of these variables become important for policy planning. While many previous studies only focus on examining the effect among macroeconomic variables, this study uses ARIMA (univariate method), transfer function and VAR (multivariate methods) to measure the forecasting accuracy and also observing the effect between these macroeconomic variables. The results showed that the multivariate methods gave better explanation about the relationship between variables than the simple one. Otherwise, the results of accuracy comparison showed that the multivariate methods did not always yield better forecast than the simple one, and these conditions in line with the results and conclusions of M3 and M4 competition.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47228436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-24DOI: 10.14710/MEDSTAT.12.1.26-38
Novri Suhermi, Retno Puspitaningrum, Agus Suharsono
In this study, we aim to build a multivariate control chart for autocorrelated data. We use MEWMA and MEWMV control charts which are free of normality assumption. Time series model is then applied to tackle autocorrelation problem in the data where the control charts require independence assumption. The real dataset used is the quality characteristics of white crystal sugar, also called gula kristal putih (GKP). There are 3 quality characteristics of GKP, namely moisture (%), color of solution (IU), and grain type (mm). It is considered that these quality characteristics are correlated each other. Our results show that the variability process is out of control where there are 5 observations outside the control limits. Meanwhile the mean process is also out of control. The factors causing the out of control include the workers, the raw materials, the measurement, the machines, and the methods. The process capability indices result in the values less than 1 which means the process is not sufficiently capable.
{"title":"DIAGRAM KENDALI MEWMV DAN MEWMA BERBASIS MODEL TIME SERIES PADA DATA BERAUTOKORELASI: STUDI KASUS GULA KRISTAL PUTIH","authors":"Novri Suhermi, Retno Puspitaningrum, Agus Suharsono","doi":"10.14710/MEDSTAT.12.1.26-38","DOIUrl":"https://doi.org/10.14710/MEDSTAT.12.1.26-38","url":null,"abstract":"In this study, we aim to build a multivariate control chart for autocorrelated data. We use MEWMA and MEWMV control charts which are free of normality assumption. Time series model is then applied to tackle autocorrelation problem in the data where the control charts require independence assumption. The real dataset used is the quality characteristics of white crystal sugar, also called gula kristal putih (GKP). There are 3 quality characteristics of GKP, namely moisture (%), color of solution (IU), and grain type (mm). It is considered that these quality characteristics are correlated each other. Our results show that the variability process is out of control where there are 5 observations outside the control limits. Meanwhile the mean process is also out of control. The factors causing the out of control include the workers, the raw materials, the measurement, the machines, and the methods. The process capability indices result in the values less than 1 which means the process is not sufficiently capable.","PeriodicalId":34146,"journal":{"name":"Media Statistika","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/MEDSTAT.12.1.26-38","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41438656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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