### Nowcasting Indonesia's GDP Growth Using Dynamic Factor Model: Are Fiscal Data Useful?

- Research Article in Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
- Authors:
- Ardiana Alifatussaadah, Anindya Diva Primariesty, Agus Mohamad Soleh, Andriansyah Andriansyah
- Abstract:
Since introduced by Giannone et. al., GDP nowcasting models have been used in many countries, including Indonesia. Variables to select usually include housing and construction, income, manufacturing, labor, surveys, international trade, retails and consumptions. Interestingly, fiscal variables are …

more »Since introduced by Giannone et. al., GDP nowcasting models have been used in many countries, including Indonesia. Variables to select usually include housing and construction, income, manufacturing, labor, surveys, international trade, retails and consumptions. Interestingly, fiscal variables are excluded even though government expenditure is an integral part of the basic GDP identity. By employing the previous journal of quarter-to-quarter real GDP growth nowcasting technique by Bok et. al., this paper is aimed at testing the usefulness of inclusion of fiscal variables, in addition to 61 non-fiscal variables, in nowcasting Indonesia GDP. The results show, even though based on the fact that fiscal data have low correlation coefficients to GDP, the inclusion of fiscal data may help to produce a better early estimate of GDP growth based on a better RMSEP value.

### Kernel Regression on Reflectance of Lithium Niobate in various Concentrations of Ruthenium Oxide

- Research Article in Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
- Authors:
- Anne Mudya Yolanda, Muhammad Nur Aidi, Indahwati Indahwati, Irzaman Irzaman
- Abstract:
Research on LiNbO3doped with RuO2has been widely developed. This study aimed to use kernel regression to measure the influence of wavelength to the percentage of reflectance of LiNbO3 doped with RuO2 and compare estimated results among all concentrations. A hundred candidates of bandwidth were trie…

more »Research on LiNbO3doped with RuO2has been widely developed. This study aimed to use kernel regression to measure the influence of wavelength to the percentage of reflectance of LiNbO3 doped with RuO2 and compare estimated results among all concentrations. A hundred candidates of bandwidth were tried to find the optimum bandwidth. For the percentage of reflectance with wavelength around 450.2 to 900.9, the results show that the optimum bandwidth is 11.56. The kernel regression performs smoothing and estimating at each data point based on the optimum bandwidth used so that the fitted data is closely following the observed data. The kernel regression model produced adjusted R-square as 0.9629, 0.9590, 0.9871, and 0.9840, respectively for LiNbO3doped with concentration 0, 2, 4, and 6%. For the same wavelength, the percentage of reflectance of a material made of LiNbO3doped with various concentration is higher than material made of LiNbO3only.

### Simulation Studyfor Comparison of Maximum Likelihood and Bayesian Method in Spatial Autoregressive Models with Heteroskedasticity

- Research Article in Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia
- Authors:
- Fitri Ramadhini, Anik Djuraidah, Aji Hamim Wigena
- Abstract:
Generally spatialregressionconsiders onlyone of the spatial effects, namely spatial dependence or heteroskedasticity between areas. Spatial autoregressive(SAR) models take only into account thedependence on the response variable. Most of SAR estimators are valid if there is no violation in the erro…

more »Generally spatialregressionconsiders onlyone of the spatial effects, namely spatial dependence or heteroskedasticity between areas. Spatial autoregressive(SAR) models take only into account thedependence on the response variable. Most of SAR estimators are valid if there is no violation in the error assumption. Estimation of SAR parameters with heteroskedasticity using maximum likelihood (ML)method gives bias and inconsistent estimators. An alternative method that can be used is Bayesian method. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. Simulation data is used to evaluate the Bayesian method in estimating parameters of SAR model with heteroskedasticity. The results indicate that Bayesian method provides bias parameter estimates relatively small and consistent compared to the ML method.

### VARIABLE SELECTION IN ANALYZING LIFE INFANT BIRTH IN INDONESIA USING GROUP LASSO AND GROUP SCAD

- Authors:
- Ita Wulandari, Khairil Anwar Notodiputro, Bagus Sartono
- Abstract:
Regression analysis often requires a selection of explanatory variables X1, X2, ... Xp so shrinkage coefficients can occur that can facilitate the interpretation of the regression equation obtained. In this context, the explanatory variable often has a grouping structure so that a more relevant pro…

more »Regression analysis often requires a selection of explanatory variables X1, X2, ... Xp so shrinkage coefficients can occur that can facilitate the interpretation of the regression equation obtained. In this context, the explanatory variable often has a grouping structure so that a more relevant problem is how to choose groups rather than individuals. Group LASSO and group SCAD are techniques for selecting groups of variables which in many works of literature appear to have advantages over LASSO. In this study, the percentage of live born children in the province of Bali, East Nusa Tenggara and other Indonesia provinces were analyzed and linked to the explanatory variables using the group LASSO and group SCAD methods. The classification of available explanatory variables is grouped based on the theory and results of previous studies. The results show that the best model is the group SCAD method with the smallest AIC, BIC and GCV values. Factors included in the model for Bali province are demographic factors, women's status, and autonomy and the economy. For East Nusa Tenggara province the factors that enter the model are demographics and economics, while generally for Indonesia the factors that are included in the model are demography, women's status, and autonomy and family planning.

### Efficiency of Several Complex Survey Design using EBLUP in Small Area Estimation

- Authors:
- Nadra Yudelsa Ratu, Ika Yuni Wulansari
- Abstract:
Thedissemination of data from the survey was carried out by estimating the parameter of the survey results. The implementation of the survey at BPS now is getting more complex where direct estimation results are presented into small areas. However, the sample size of direct estimation in small area…

more »Thedissemination of data from the survey was carried out by estimating the parameter of the survey results. The implementation of the survey at BPS now is getting more complex where direct estimation results are presented into small areas. However, the sample size of direct estimation in small area has a relatively small size so that it is not reliable enough, not efficient and has low precision. Therefore, other statistics are needed that can accommodate the dissemination from total household expenditure data in the small area. In this study of small area, it was carried out by applying the Small Area Estimation (SAE) method, which is Empirical Best Linear Unbiased Prediction (EBLUP) by involving a complex survey design. The sampling method in complex survey design that used are Simple Random Sampling Without Replacement called SRSWOR, One Stage Cluster (SRSWOR), Two Stage Cluster (SRSWOR-SRSWOR) and Two Stage Cluster (Probability Proportional to Size called PPSWR-SRSWOR). The efficiency of estimation result is evaluated based on MSE and RRMSE values that obtained in each method of the complex survey design. According to the calculation results, the largest MSE and RRMSE value of the estimation was obtained from Two Stage Cluster (SRSWOR-SRSWOR) sampling method. Besides, the smallest MSE and RRMSE value was obtained from the SRSWOR sampling method that seem to have distinct advantage over the other sampling method.

### Comparison of Maximum Likelihood and Generalized Method of Moments in Spatial Autoregressive Model with Heteroskedasticity

- Authors:
- Rohimatul Anwar, Anik Djuraidah, Aji Hamim Wigena
- Abstract:
Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containingheteroskedasticityusing the maximum likelihood estimation (MLE) method provides biased and…

more »Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containingheteroskedasticityusing the maximum likelihood estimation (MLE) method provides biased and inconsistent. The alternative method is the generalized method of moments (GMM). GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. The bias is used to evaluate the GMM in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM provides the bias of parameter estimates relatively consistent and smaller compared to the MLE method.

### Confidence Interval for Multivariate Process Capability indices in Statistical Inventory Control

- Authors:
- Mustafid Mustafid, Dwi Ispriyanti, Sugito Sugito, Diah Safitri
- Abstract:
Multivariate process capability indices (MPCI) has important role in the analysis of statistical inventory control determined by several consumer demand as quality characteristics that are correlated. In the inventory control management is also needed confidence interval for MPCI to overcome the un…

more »Multivariate process capability indices (MPCI) has important role in the analysis of statistical inventory control determined by several consumer demand as quality characteristics that are correlated. In the inventory control management is also needed confidence interval for MPCI to overcome the uncertain from consumer demand. The research aims to apply the confidence interval for MPCI in statistical inventory control. The case studies conducted on the apparel industry to implement the confidence interval for the MPCI using several types of apparel which is used as the quality characteristics. The upper and lower limits for the intervals from the MPCI are obtained using sample data assuming multivariate normal distribution and stable. Process sample data in stable conditions are obtained by using analysis of multivariate control diagram designed by T2 Hotelling. The MPCI confidence interval can be used as the indicator in determining the number of products provided in inventory based on the number of consumer demand.

### Evaluation of Proportional Odds and Continuation Ratio Models for Smoker in Indonesia

- Authors:
- Rini Warti, Anang Kurnia, Kusman Sadik
- Abstract:
The polytomous model is a model used for more than two categorical response data. Some models that can use for ordinal scale responses are the Proportional Odds Model, Continuation Model, Partial Proportional Odds Model, and Adjacent Model. The Proportional Odds model has the assumption of "proport…

more »The polytomous model is a model used for more than two categorical response data. Some models that can use for ordinal scale responses are the Proportional Odds Model, Continuation Model, Partial Proportional Odds Model, and Adjacent Model. The Proportional Odds model has the assumption of "proportionality" or parallelity to the cumulative logit. If the parallel logits assumption not fulfilled, the alternative models that can use are Adjacent Model and Continuation-Ratio. The purpose of this study is to evaluate the proportional Odds (PO) model and Continuation-Ratio (CR) for smokers in Indonesia. The data used was taken from 2017 Indonesian Demographic and Health Survey (IDHS) by classifying smokers in ordinal categories (mild, moderate, and severe). The results show there was a violation of the assumptions in the PO Model so that the CR Model was an alternative to use. Gender is a factor that has a significant influence on all response categories. Based on the value of Goodness of fit, deviance, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mac Fadden R2 indicate that the CR Model is better to use than the Model PO.

### Implementation Extreme Learning Machine for Rainfall Forecasting

- Authors:
- Laksmita Puspaningrum, Ayundyah Kesumawati
- Abstract:
Indonesia is one of country that have a large number of rainfall days in a year. So, make any sense that forecasting is important to get any strategies to overcome the problem of erratic rainfall. There are a lot of method that can conduct forecast rainfall, the new one is Extreme Machine Learni…

more »Indonesia is one of country that have a large number of rainfall days in a year. So, make any sense that forecasting is important to get any strategies to overcome the problem of erratic rainfall. There are a lot of method that can conduct forecast rainfall, the new one is Extreme Machine Learning. Extreme Learning Machine (ELM) is a new learning method of artificial neural networks. ELM is an easy-to use and effective learning algorithm of single-hidden layer feed-forward neural networks (SLFNs). Therefore, ELM has the advantages of fast learning speed and good generalization performance. This research conducted by using rainfall data in Sleman city to get forecasting in one year. It found that the ELM method has a smaller value compared to the GARCH method for all six rainfall station stations in Sleman region.

### BAYESIAN QUANTILE REGRESSION MODELING TO ESTIMATE EXTREME RAINFALL IN INDRAMAYU

- Authors:
- Eko Primadi Hendri, Aji Hamim Wigena, Anik Djuraidah
- Abstract:
Quantile regression can be used to analyze symmetric or asymmetric data. Estimates of quantile regression parameters are obtained by the simplex method. Another approach is the Bayesian method based on Laplace's asymmetric distribution using MCMC. MCMC is used numerically to estimate parameters fro…

more »Quantile regression can be used to analyze symmetric or asymmetric data. Estimates of quantile regression parameters are obtained by the simplex method. Another approach is the Bayesian method based on Laplace's asymmetric distribution using MCMC. MCMC is used numerically to estimate parameters from each posterior distribution. The Bayesian quantile regression and the quantile regression can be used for statistical downscaling in extreme rainfall cases. This study used statistical downscaling to obtain relationship between global-scale data and local-scale data. The data used were monthly rainfall data in Indramayu and GCM output data. LASSO regularization was used to overcome multicollinearity problems in GCM output data. The purpose of this study was to compare Bayesian quantile regression models with quantile regression. The Bayesian quantile regression and the quantile regression couldpredict extreme rainfallmore accurate and consistent in one year ahead. The Bayesian quantile regression model is relatively better than the quantile regression.