Pub Date : 2023-05-31DOI: 10.26714/jsunimus.11.1.2023.34-44
Layla Fickri Amalia, Putu Gita Suari Miranti
Bali is one of the most popular tourist destinations by domestic and foreign tourists in Indonesia. Because many tourists visit, many Balinese people are looking for a livelihood in the tourism sector such as being a tour guide, working in the hospitality sector, culinary, tourist trips etc. During the COVID-19 pandemic, many workers in the tourism sector lost their jobs, increasing the open unemployment rate in Bali Province. With a high unemployment rate, people's welfare decreases so that it can affect economic growth in Bali Province. This study aims to see the Effect of the Tourism Sector and Open Unemployment on Economic Growth in Bali Province. The variables of the independent of this study are the number of tourists visiting, the number of hotel, the number of travel agencies and the open unemployment rate. Meanwhile, the dependent variable used is the economic growth of Bali province. The analysis tool used is Panel Data Regression, from the test obtained the value of the coefficient of determination R2 of 65.80%, this shows the magnitude of the influence of independent variables on dependent variables. The results of the study concluded that simultaneously the number of tourists, the number of restaurants, the number of tourist travel agencies, and the unemployment rate influenced economic growth. This is seen from the prob value of F-statistics of 0.0000. Meanwhile, the results of the t test show that the results are influential and significant for each independent variable against the dependent variable.
巴厘岛是印尼最受国内外游客欢迎的旅游目的地之一。由于许多游客来访，许多巴厘人都在旅游业寻找生计，如当导游，在酒店部门工作，烹饪，旅游等。在2019冠状病毒病大流行期间，旅游部门的许多工人失去了工作，增加了巴厘岛省的公开失业率。由于失业率高，人们的福利减少，从而影响巴厘岛省的经济增长。本研究旨在探讨旅游业与开放失业对峇里省经济成长的影响。本研究的独立变量为游客数量、酒店数量、旅行社数量和开放失业率。同时，使用的因变量为巴厘省的经济增长。使用的分析工具是Panel Data Regression，从检验中得到决定系数R2的值为65.80%，由此可见自变量对因变量的影响程度。研究结果表明，游客数量、餐馆数量、旅游旅行社数量和失业率同时影响经济增长。这可以从f统计量的probb值0.0000看出。同时，t检验结果表明，各自变量对因变量均有影响且显著。
{"title":"ANALYSIS OF THE EFFECT TOURISM SECTOR AND OPEN UNEMPLOYMENT ON ECONOMIC GROWTH IN BALI PROVINCE","authors":"Layla Fickri Amalia, Putu Gita Suari Miranti","doi":"10.26714/jsunimus.11.1.2023.34-44","DOIUrl":"https://doi.org/10.26714/jsunimus.11.1.2023.34-44","url":null,"abstract":"Bali is one of the most popular tourist destinations by domestic and foreign tourists in Indonesia. Because many tourists visit, many Balinese people are looking for a livelihood in the tourism sector such as being a tour guide, working in the hospitality sector, culinary, tourist trips etc. During the COVID-19 pandemic, many workers in the tourism sector lost their jobs, increasing the open unemployment rate in Bali Province. With a high unemployment rate, people's welfare decreases so that it can affect economic growth in Bali Province. This study aims to see the Effect of the Tourism Sector and Open Unemployment on Economic Growth in Bali Province. The variables of the independent of this study are the number of tourists visiting, the number of hotel, the number of travel agencies and the open unemployment rate. Meanwhile, the dependent variable used is the economic growth of Bali province. The analysis tool used is Panel Data Regression, from the test obtained the value of the coefficient of determination R2 of 65.80%, this shows the magnitude of the influence of independent variables on dependent variables. The results of the study concluded that simultaneously the number of tourists, the number of restaurants, the number of tourist travel agencies, and the unemployment rate influenced economic growth. This is seen from the prob value of F-statistics of 0.0000. Meanwhile, the results of the t test show that the results are influential and significant for each independent variable against the dependent variable.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123845926","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 : 2023-05-31DOI: 10.26714/jsunimus.11.1.2023.1-9
A. Dani, Fachrian Bimantoro Putra, Muhammad Aldani Zen, V. Ratnasari, Qonita Qurrota A’yun
In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword "Chocolate" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.
{"title":"FOURIER SERIES APPLICATION FOR MODELING “CHOCOLATE” KEYWORD SEARCH TRENDS IN GOOGLE TRENDS DATA","authors":"A. Dani, Fachrian Bimantoro Putra, Muhammad Aldani Zen, V. Ratnasari, Qonita Qurrota A’yun","doi":"10.26714/jsunimus.11.1.2023.1-9","DOIUrl":"https://doi.org/10.26714/jsunimus.11.1.2023.1-9","url":null,"abstract":"In some cases of regression modeling, it is very common to find a repeating pattern. To model this, of course, the approach used must be in accordance with the characteristics of the data. The Fourier series is one of the proposed approaches, because it has advantages in modeling relationship patterns that tend to repeat, such as cosine sine waves. The Fourier series is a subset of nonparametric regression, which has good flexibility in modeling. In this study, the Fourier series approach was applied to model search trend data for the keyword \"Chocolate\" sourced from Google Trends. Generalized Cross-Validation (GCV) is used as model evaluation criteria. Based on the results of the analysis, the best Fourier series nonparametric regression model is obtained with the number of oscillations of five, which is indicated by the minimum GCV value.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134112533","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 : 2023-05-31DOI: 10.26714/jsunimus.11.1.2023.45-60
M. Fathurahman
Poisson regression is commonly used in modeling count data in various research fields. An essential assumption must be met when using Poisson regression, which is that the count data of the response has the mean and variance must be equal, namely equi-dispersion. This assumption is often unmet because many data for the response that the variance is greater than the mean, called over-dispersion. If the Poisson regression model contains the over-dispersion, then will be produced an invalid model can under-estimate standard errors and misleading inference for regression parameters. Therefore, an approach is needed to overcome the over-dispersion problem in Poisson regression. The generalized Poisson regression can handle the over-dispersion in Poisson regression. This study aims to obtain the generalized Poisson regression model and the factors affecting the low birth weight in Indonesia in 2021. The result shows that the factors affecting the low birth weight in Indonesia based on the generalized Poisson regression model were: poverty rate, percentage of households with access to appropriate sanitation, percentage of pregnant women at risk of chronic energy deficiency receiving additional food, percentage of pregnant women who received blood-boosting tablets, and percentage of antenatal care.
{"title":"MODELING COUNT DATA WITH OVER-DISPERSION USING GENERALIZED POISSON REGRESSION: A CASE STUDY OF LOW BIRTH WEIGHT IN INDONESIA","authors":"M. Fathurahman","doi":"10.26714/jsunimus.11.1.2023.45-60","DOIUrl":"https://doi.org/10.26714/jsunimus.11.1.2023.45-60","url":null,"abstract":"Poisson regression is commonly used in modeling count data in various research fields. An essential assumption must be met when using Poisson regression, which is that the count data of the response has the mean and variance must be equal, namely equi-dispersion. This assumption is often unmet because many data for the response that the variance is greater than the mean, called over-dispersion. If the Poisson regression model contains the over-dispersion, then will be produced an invalid model can under-estimate standard errors and misleading inference for regression parameters. Therefore, an approach is needed to overcome the over-dispersion problem in Poisson regression. The generalized Poisson regression can handle the over-dispersion in Poisson regression. This study aims to obtain the generalized Poisson regression model and the factors affecting the low birth weight in Indonesia in 2021. The result shows that the factors affecting the low birth weight in Indonesia based on the generalized Poisson regression model were: poverty rate, percentage of households with access to appropriate sanitation, percentage of pregnant women at risk of chronic energy deficiency receiving additional food, percentage of pregnant women who received blood-boosting tablets, and percentage of antenatal care.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411900","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 : 2023-05-31DOI: 10.26714/jsunimus.11.1.2023.22-33
Tresiani Yunitasari, M. A. Haris, Prizka Rismawati Arum
Transportation was an important sector of supporting the economic growth of a country. The impact of the Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted in the movement of the number of passengers decreasing quite drastically, but in mid-2020 the movement of the number of passengers had slowly increased. Forecasting was done to determine the flow of movement of the number of passengers in the future using the Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. The SSA method was expected to be able to decompose various patterns in the data into trend, seasonality and noise. Furthermore, the NN method was used to analyze nonlinear patterns in the data. The results showed that the best method was a combination of the SSA method with a window length of 40 and the NN method with a 6-8-1 network architecture (6 input neurons, 8 hidden neurons and 1 output neuron) for the trend component, 11-15-1 (11 neurons input, 15 hidden neurons and 1 output neuron) for the seasonal component, and 10-15-1 (10 input neurons, 15 hidden neurons and 1 output neuron) for the noise component. The model produces a prediction error based on a MAPE value of 0.54% or an accuracy rate of 99.46%.
{"title":"FORECASTING THE NUMBER OF PASSENGER AT JENDERAL AHMAD YANI SEMARANG INTERNATIONAL AIRPORT USING HYBRID SINGULAR SPECTRUM ANALYSIS-NEURAL NETWORK (SSA-NN) METHOD","authors":"Tresiani Yunitasari, M. A. Haris, Prizka Rismawati Arum","doi":"10.26714/jsunimus.11.1.2023.22-33","DOIUrl":"https://doi.org/10.26714/jsunimus.11.1.2023.22-33","url":null,"abstract":"Transportation was an important sector of supporting the economic growth of a country. The impact of the Covid-19 2020 pandemic at Achmad Yani International Airport in Semarang resulted in the movement of the number of passengers decreasing quite drastically, but in mid-2020 the movement of the number of passengers had slowly increased. Forecasting was done to determine the flow of movement of the number of passengers in the future using the Hybrid Singular Spectrum Analysis (SSA)-Neural Network (NN) method. The SSA method was expected to be able to decompose various patterns in the data into trend, seasonality and noise. Furthermore, the NN method was used to analyze nonlinear patterns in the data. The results showed that the best method was a combination of the SSA method with a window length of 40 and the NN method with a 6-8-1 network architecture (6 input neurons, 8 hidden neurons and 1 output neuron) for the trend component, 11-15-1 (11 neurons input, 15 hidden neurons and 1 output neuron) for the seasonal component, and 10-15-1 (10 input neurons, 15 hidden neurons and 1 output neuron) for the noise component. The model produces a prediction error based on a MAPE value of 0.54% or an accuracy rate of 99.46%.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125452391","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}
The univariate mean process monitoring is only used the information from the study variable. One of the univariate control chart that used to monitor the mean process is GWMA control chart. But, in this research, we need to monitor process mean using the information from the study variable and information on the adding or auxiliary variable. The enhanced control chart in this research named AIB-GWMA control chart. In this research, we also made a comparison between the GWMA and AIB-GWMA to know the sensitivity and effectiveness of these control chart. The comparison is used to know the effect of the auxiliary variable in process monitoring. The performance of these control chart is evaluated using Average Run Length with help of Monte Carlo simulation. The result of this study is AIB-GWMA has a smaller ARL than the GWMA control chart. It showed that AIB-GWMA is faster to detect a shift in mean process. In further study, we recommended to enhance the performance of the AIB-GWMA by extending the current work to the AIB-MaxGWMA, so it is possible to monitor process mean and variance simultaneously.
{"title":"AUXILIARY INFORMATION BASED GENERALLY WEIGHTED MOVING AVERAGE FOR PROCESS MEAN","authors":"Istin Fitriana Aziza, Wirajaya Kusuma, Siti Soraya","doi":"10.26714/jsunimus.11.1.2023.10-21","DOIUrl":"https://doi.org/10.26714/jsunimus.11.1.2023.10-21","url":null,"abstract":"The univariate mean process monitoring is only used the information from the study variable. One of the univariate control chart that used to monitor the mean process is GWMA control chart. But, in this research, we need to monitor process mean using the information from the study variable and information on the adding or auxiliary variable. The enhanced control chart in this research named AIB-GWMA control chart. In this research, we also made a comparison between the GWMA and AIB-GWMA to know the sensitivity and effectiveness of these control chart. The comparison is used to know the effect of the auxiliary variable in process monitoring. The performance of these control chart is evaluated using Average Run Length with help of Monte Carlo simulation. The result of this study is AIB-GWMA has a smaller ARL than the GWMA control chart. It showed that AIB-GWMA is faster to detect a shift in mean process. In further study, we recommended to enhance the performance of the AIB-GWMA by extending the current work to the AIB-MaxGWMA, so it is possible to monitor process mean and variance simultaneously.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122486890","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 : 2023-04-13DOI: 10.26714/jsunimus.10.2.2022.43-53
E. Setyowati
The study aims to determine the effect of the Human Development Index (HDI) variable and investment variables which include Domestic Investment and Foreign Investment on the Gross Regional Domestic Product (GRDP) of 34 provinces in Indonesia in 2015-2019. The method used in this research is panel regression analysis. The results of the study indicate that the best panel data regression model for modeling GRDP is the Random Effect Model (REM). Based on the model formed, it is known that the variables HDI, Domestic Investment, and Foreign Investment have a significant positive effect on GRDP. These results are consistent with the theory and hypothesis that the higher the value of the HDI, Domestic Investment, and Foreign Investment, the higher the value of the GRDP variable. The coefficient of determination shows a moderate value of 58.9%, so it is suspected that there are other variables that can affect GRDP. Based on the regression model formed, if all independent variables have the same value for all provinces, then the province with the highest GRDP value is Jakarta.
{"title":"ANALYSIS OF INDONESIA ECONOMIC GROWTH BASED ON INVESTMENT VALUE AND HUMAN DEVELOPMENT INDEX USING AN ECONOMETRIC APPROACH","authors":"E. Setyowati","doi":"10.26714/jsunimus.10.2.2022.43-53","DOIUrl":"https://doi.org/10.26714/jsunimus.10.2.2022.43-53","url":null,"abstract":"The study aims to determine the effect of the Human Development Index (HDI) variable and investment variables which include Domestic Investment and Foreign Investment on the Gross Regional Domestic Product (GRDP) of 34 provinces in Indonesia in 2015-2019. The method used in this research is panel regression analysis. The results of the study indicate that the best panel data regression model for modeling GRDP is the Random Effect Model (REM). Based on the model formed, it is known that the variables HDI, Domestic Investment, and Foreign Investment have a significant positive effect on GRDP. These results are consistent with the theory and hypothesis that the higher the value of the HDI, Domestic Investment, and Foreign Investment, the higher the value of the GRDP variable. The coefficient of determination shows a moderate value of 58.9%, so it is suspected that there are other variables that can affect GRDP. Based on the regression model formed, if all independent variables have the same value for all provinces, then the province with the highest GRDP value is Jakarta.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128140435","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 : 2023-04-13DOI: 10.26714/jsunimus.10.2.2022.1-12
R. Permatasari, K. Anam, Nuari Anisa Sivi, I. Iskandar
Foreign exchange reserves can be an important indicator to see how far a country can carry out international trade and to show the strength of a country's economic fundamentals. The size of the foreign exchange reserves is influenced by several factors, one of which is export and import activities. This study aims to identify the effect of exports and imports on the position of Indonesia's foreign exchange reserves. The data used are secondary data from the Central Statistics Agency and Bank Indonesia. The object used in this study is Indonesia with Time Series, which is 17 years from 2005 to 2021. This study uses a quantitative approach with the Multiple Linear Regression Analysis method using software the IBM-SPSS Version 25.0. Based on the results of the regression, it is known that the value of exports has a positive and significant effect on Indonesia's foreign exchange reserves, this is indicated by the value of sig 0.000 < 0.050. Meanwhile, imports have a negative and significant effect on Indonesia's foreign exchange reserves, this is indicated by the value of Sig 0.567. sig value 0.567 > 0.050. Export and Import variables together on Indonesia's Foreign Exchange Reserves. This can be seen from the results of the analysis of the significance value (Sig.) 0.00 < 0.050.
{"title":"MULTIPLE LINEAR REGRESSION ANALYSIS ON THE EFFECT OF EXPORTS AND IMPORTS ON INDONESIA’S FOREIGN EXCHANGE RESERVE 2005-2021","authors":"R. Permatasari, K. Anam, Nuari Anisa Sivi, I. Iskandar","doi":"10.26714/jsunimus.10.2.2022.1-12","DOIUrl":"https://doi.org/10.26714/jsunimus.10.2.2022.1-12","url":null,"abstract":"Foreign exchange reserves can be an important indicator to see how far a country can carry out international trade and to show the strength of a country's economic fundamentals. The size of the foreign exchange reserves is influenced by several factors, one of which is export and import activities. This study aims to identify the effect of exports and imports on the position of Indonesia's foreign exchange reserves. The data used are secondary data from the Central Statistics Agency and Bank Indonesia. The object used in this study is Indonesia with Time Series, which is 17 years from 2005 to 2021. This study uses a quantitative approach with the Multiple Linear Regression Analysis method using software the IBM-SPSS Version 25.0. Based on the results of the regression, it is known that the value of exports has a positive and significant effect on Indonesia's foreign exchange reserves, this is indicated by the value of sig 0.000 < 0.050. Meanwhile, imports have a negative and significant effect on Indonesia's foreign exchange reserves, this is indicated by the value of Sig 0.567. sig value 0.567 > 0.050. Export and Import variables together on Indonesia's Foreign Exchange Reserves. This can be seen from the results of the analysis of the significance value (Sig.) 0.00 < 0.050.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115056954","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}
Abstract: Rainfall is the height of rainwater in a rain gauge on a flat place that does not seep and flow, where rainfall is measured in millimeters (mm). This study aims to estimate and model the rainfall for DKI Jakarta Province from January 2016 to December 2021 using the Fourier series estimation. Based on the results of the study, a model with a minimum GCV value of 21909,4, at the 7th 𝝀 43,78972. This model shows that the predictor variable can explain the diversity of response variables by 94,14%.
{"title":"PREDICTION OF RAINFALL IN DKI JAKARTA PROVINCE BASED ON THE FOURIER SERIES ESTIMATOR","authors":"Zidni Ilmatun Nurrohmah, Diana Ulya, Qumadha Zainal Abidin, Syifaun Nadhiro, N. Chamidah","doi":"10.26714/jsunimus.10.2.2022.34-42","DOIUrl":"https://doi.org/10.26714/jsunimus.10.2.2022.34-42","url":null,"abstract":"Abstract: Rainfall is the height of rainwater in a rain gauge on a flat place that does not seep and flow, where rainfall is measured in millimeters (mm). This study aims to estimate and model the rainfall for DKI Jakarta Province from January 2016 to December 2021 using the Fourier series estimation. Based on the results of the study, a model with a minimum GCV value of 21909,4, at the 7th 𝝀 43,78972. This model shows that the predictor variable can explain the diversity of response variables by 94,14%.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128728074","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 : 2023-04-13DOI: 10.26714/jsunimus.10.2.2022.25-33
F. Rahmawati, S. Subanar
Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis. Parameters are assumed to follow a certain distribution called the prior distribution. Using the sample information along with the prior distribution, the conditional posterior distribution is searched using the Box-Tiao rule. Computational solutions are solved by the MCMC Gibbs Sampling algorithm. Gibbs Sampling can generate samples based on the conditional posterior distribution of each parameter in order to obtain a posterior joint distribution. Tobit Quantile Regression with Adaptive Lasso Penalty was applied to data on Household Expenditure for Cigarette Consumption in 2011. As a comparison for data analysis, Tobit Quantile Regression was used. The results of data analysis show that the Tobit Quantile Regression model with Adaptive Lasso Penalty is better than the Tobit Quantile Regression.
{"title":"BAYESIAN ANALYSIS OF TOBIT QUANTILE REGRESSION WITH ADAPTIVE LASSO PENALTY IN HOUSEHOLD EXPENDITURE FOR CIGARETTE CONSUMPTION","authors":"F. Rahmawati, S. Subanar","doi":"10.26714/jsunimus.10.2.2022.25-33","DOIUrl":"https://doi.org/10.26714/jsunimus.10.2.2022.25-33","url":null,"abstract":"Tobit Quantile Regression with Adaptive Lasso Penalty is a quantile regression model on censored data that adds Lasso's adaptive penalty to its parameter estimation. The estimation of the regression parameters is solved by Bayesian analysis. Parameters are assumed to follow a certain distribution called the prior distribution. Using the sample information along with the prior distribution, the conditional posterior distribution is searched using the Box-Tiao rule. Computational solutions are solved by the MCMC Gibbs Sampling algorithm. Gibbs Sampling can generate samples based on the conditional posterior distribution of each parameter in order to obtain a posterior joint distribution. Tobit Quantile Regression with Adaptive Lasso Penalty was applied to data on Household Expenditure for Cigarette Consumption in 2011. As a comparison for data analysis, Tobit Quantile Regression was used. The results of data analysis show that the Tobit Quantile Regression model with Adaptive Lasso Penalty is better than the Tobit Quantile Regression.","PeriodicalId":183562,"journal":{"name":"Jurnal Statistika Universitas Muhammadiyah Semarang","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134550443","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 : 2022-12-31DOI: 10.26714/jsunimus.10.2.2022.13-24
Uqwatul Alma Wizsa, Wikasanti Dwi Rahayu, Septria Susanti
The Covid-19 pandemic that entered Indonesia in early 2020 has more or less had an impact on Indonesia's economic growth. One of the important factors that are indicators of the ups and downs of the economy, especially in Indonesia, is export activities. The Covid-19 pandemic has had quite an impact on the total value of Indonesia's exports, especially from 2020 to 2021. The fluctuation in the export value has made researchers interested in forecasting the total export value, especially after the Covid-19 pandemic. Forecasting of the total value of exports can certainly be used as a reference for the government to determine the direction of policies toward export activities to increase Indonesia's economic growth. Export values usually have seasonal patterns. One of the time series analyses that can be applied to data on total export values is the SARIMA model. Especially after Covid-19, no related studies have been found that use the SARIMA model in predicting the total value of exports in Indonesia. Using reference data on the total export value of Indonesia from January 2019 to March 2022, the best model was obtained and met the assumptions of residual normality and residual freedom, namely the ARIMA model (0,1,1)(0,0,1)12 without an intercept with an AICc value of 675.5562. Forecasting the total export value from April 2022 to March 2023 using this model indicates that the export value will increase slowly but decrease in September 2022 and January 2023.
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