Balita pendek atau biasa disebut dengan stunting merupakan salah satu permasalahan yang sedang dihadapi oleh Indonesia. Data mengenai kondisi stunting di Indonesia bersumber dari pendataan Riset Kesehatan Dasar (Riskesdas) pada tahun 2018 menempatkan Indonesia di peringkat ketiga negara dengan status stunting tertinggi di Asia. Penelitian ini dilakukan untuk mengkaji prevalensi balita pendek di tiap provinsi di Indonesia. Metode yang digunakan adalah regresi spasial. Pemilihan analisis spasial didasarkan pada adanya ketergantungan spasial angka prevalensi di satu wilayah dengan wilayah lainnya. Hal tersebut diperkuat oleh hasil uji Indeks Moran menunjukkan adanya efek dependensi spasial. Berdasarkan uji Lagrange Multiplier, depedensi spasial terjadi pada error sehingga model spasial yang cocok digunakan adalah Spatial Error Model (SEM). Hasil pengujian menunjukkan prevalensi balita pendek di suatu wilayah dipengaruhi oleh 5 variabel independen di wilayah tersebut dan residual spasial dari wilayah lain yang berdekatan dan memiliki karakteristik yang sama.
Vector Autoregressive (VAR) is a multivariate time series model for examining objects with two or more variables in which the variables affect each other under the stationarity assumption. This study aims to compare the parameter estimation procedure of VAR(1) model of Ordinary Least Square (OLS) and Maximum Likelihood Estimation (MLE) methods. The comparison is investigated by both theoretical and empirical approaches. This study uses daily data of the number of positive cases and the number of deaths caused by covid-19 in China. Result shows that theoretically, the parameter estimation of OLS and MLE give the same results. Empirically, it is proven that the parameter estimation done by both methods provide the same results either in the presence and absence of intercept. For the presence of intercept, the number of positive cases influenced by both the number of positive cases itself and the number of deaths from the preceding period. Meanwhile, the number of deaths is only explained by the number of deaths in the previous period. For the absence of intercept, there is a significant effect of the number of positive cases from the previous period toward the number of positive cases, but the effect of the number of deaths in the preceding period is not significant. Hence, there is no effect of interaction between the number of positive cases and the number of deaths cases and vice versa.
Gold is one of the most attractive commodities and popular investments. Investment experts often recommend investing in gold because gold is one of the safest investments. It is a stable classic hedge, although the conditions of currency volatility or global markets are depreciated. However, the gold price fluctuations can be influenced by some other factors, such as the USD Index, which reflect and measure the strength of the US Dollar currency, and the Index of Dow Jones Industrial Average (DJIA) or a reflection of the political and economic conditions of the stock market. In this study, we conduct a global gold price forecast (USD) based on the USD Index, the DJIA Index, and the influence of time trends. Based on the data's characteristics, we face the fact that the data is nonlinear, contains outliers, and its pattern is not easy to specify parametrically. Due to the complexity of the model, we then propose a more flexible, robust modeling technique called the Bayesian Nonparametric Quantile Generalized Additive Model method. According to the results for the median case, the proposed method shows an accurate forecasting category due to the value of the Mean Absolute Percentage Error, MAPE less than 10 percent.
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