The aim of this study is to propose appropriate models to forecast Return on Asset (ROA) and financing of Indonesia Islamic Commercial Banks during COVID-19 pandemic. In particular, we study the models which involve reciprocal relation between ROA and financing and incorporate COVID-19 pandemic’s impact. It is crucial because the government would benefit from forecasting results to formulate the policy for the banks related to ROA and financing. We consider two models: Vector Autoregressive with exogenous variable (VARX) and spline regression, since both models are able to exploit the multivariate structure of ROA and financing and to include COVID-19 impact as predictor. The results show that the VARX outperforms spline regression in terms of RMSE. Using VARX, we deduce that ROA and financing have a positive reciprocal relationship, meaning that when ROA increases, financing would increase, and vice versa. In addition, the pandemic has significant impact on the decline of the ROA. We recommend that banks conduct an in-depth analysis to determine the appropriate form of restructuring for debtors so that it does not have a significant impact on the decrease in ROA.

BBLR diartikan kejadian berat bayi lahir kurang dari 2500 gram. BBLR bisa menyebabkan kematian dan stunting pada bayi sehingga variabel apa saja yang berpengaruh terhadap BBLR perlu diteliti. Metode analisis yang digunakan adalah analisis deskriptif dan inferensia. Karakteristik rumah tangga (RT) yang memiliki baduta BBLR, yaitu RT dengan sumber air minum dan sanitasi tidak layak, status ekonomi miskin, pendidikan ibu SMP ke bawah, status tempat tinggal pedesaan, penolong persalinan non medis atau tidak ada, dan status ibu perokok. Variabel-variabel yang signifikan mempengaruhi BBLR, adalah usia hamil pertama ibu, kelayakan sanitasi, pendidikan tertinggi ibu, status tempat tinggal, dan jenis penolong persalinan.

Inflation is one of the economic problem because it can give bad impact to the other macro economic variables. The economic of Jakarta has important role to increase economic development of Indonesia. Because of that, inflation of Jakarta is very important to be controlled. The goverment policy about increasing and decreasing fuel price can affect inflation of Jakarta. Province goverment of Jakarta must have a knowledege about how the impact of the goverment oil price policy. It is useful for goverment of Jakarta to anticipate the bad impact that can be occured to inflation if central goverment increase the oil price in the future. The model that used in this research is intervention multi-input intervention model with pulse function. The intervention model is a time series model that was initially widely used to explore unexpected external impacts on variables that became the object of observation. The highest effect of fuel price increasing to Jakarta inflation is 7,18 percent occured on october 2005. The policy of decreasing fuel price didn’t give any impact to the decreasing of Jakarta inflation. Multi-input intervention model with pulse function is better than ARIMA in modeling inflation of DKI Jakarta.

Model nonlinear in economic and finance riset are often found. One model that can be used to capture nonlinear relationships in data is Threshold Vector Autoregressive (TVAR) model. TVAR model is generalization of VAR model, it divides the time series into different regimes that are separated by a different threshold. The purpose of this research are is to see the effects between inflation, exchange rate depreciation and money supply in Indonesia and to know the performance of forecasting with VAR and TVAR model. Inflation, exchange rate depreciation and money supply growth produce TVAR on lag 1 with one threshold and two regimes. Each regimes shows different effects. TVAR model has a smaller AIC than the VAR model so forecasting performance of TVAR model is better used in this modeling.

The TSpVARX model can be used in inflation and money outflow forecasting by accommodating the reciprocal relationship among endogenous variables, the influence of exogenous variables, inter-regional linkages, and the nonlinearity of the relationship between endogenous and predetermined variables. However, the impact of some events, such as Eid al-Fitr and fuel price adjustments, still cannot be accommodated in the TSpVARX model. This condition causes inflation and money outflow forecasting using TSpVARX to be unsatisfactory. Our study is to improve the forecasting performance of the TSpVARX model by adding subset and dummy variables. We use a 12th lag subset variable to capture seasonal effects and a dummy variable to represent fuel price changes. These additions enhance the model’s accuracy in forecasting inflation and money outflow by accounting for recurring patterns and specific events, like fuel price changes. Based on the RMSE values of the training and testing data, we can conclude that forecasting inflation and money outflow using TSpVARX with the addition of subset and dummy variables is better than the regular TSpVARX. The inflation and money outflow forecasting generated after the addition of subset and dummy variables are also more fluctuating as in the movement of the actual data.

Food security is the condition of meeting food needs for the country down to the individual level, as measured by the availability, affordability, utilization, and stability of food. Despite being a basic human need, food security in Indonesia is not evenly distributed, especially in Eastern Indonesia. Based on these findings, this study aims to determine the general picture of food security and the factors influencing it in districts/cities in Eastern Indonesia in 2024. The method used is the Spatial Durbin Model (SDM) with an inverse distance weighting matrix. The results show that the variables Distribution of GRDP of Sector Agriculture, Forestry and Fishing, Poverty Rate, Average Years of Schooling, Lag of Food Security Index, Lag of Open Unemployment Rate, and Lag of Poverty Rate have a significant influence on the Food Security Index variable in districts/cities in Eastern Indonesia in 2024.