This study aims to compare forecast performance of Neural Network (NN) to ARIMA in the case of Indonesia’s inflation and to find if there is any interesting trend in Indonesia’s inflation. We use year-on-year monthly Indonesia’s inflation data from 2006:12 to 2018:12 released by Bank Indonesia (BI) and the Indonesian Central Bureau of Statistics (CBS). We divide the series into 3 data series to capture the trend in the inflation (i.e DS1, DS2 and DS3). The data set 1 (DS1) covers data from 2006:12 to 2014:08, DS2 from 2006:12 to 2018:12, dan DS3 from 2010:12 to 2018:12. The series is then processed using the standard ARIMA method and NN model. We found that the NN model outperforms the ARIMA model in forecasting inflation for each respective series by analysing its Root Mean Squared Error (RMSE). We also found that short term lagged-inflation (backward-looking) variable has lesser effect on inflation compared to the more recent series.
By measuring time-varying financial spillovers of five asset classes, we analyze the propagation of shocks originating in the United States and Japan into countries of Emerging Asia (EA). We compare the scale and nature of spillovers during the 2008-09 global financial crisis (GFC), the 2013 “taper tantrum” (TT), and the on-going COVID-19 pandemic (C-19). Based on the direct and indirect spillovers, the intensity of the spillover effect was largest during C-19 due to its global and multidimensional nature, and the United States was a net transmitter of spillovers particularly in bonds and equity markets. TT was an important episode for EA as it marked the beginning of the region's financial volatility and increased spillovers especially in bonds market. The impulse responses reveal that most spillovers were transmitted rapidly, in a matter of days. In times of recession whereby financial stability is in danger of being affected by spillovers, a concrete financial cooperation remains absent in EA although formal institutions designed to deal with the contagion have been put in place.
The purpose of this study is to deals with a data mining to extract knowledge regarding factors which affect tuition fee and predict future amount of the tuition fee. Specifically this paper aims to know what factors do affect tuition fee of a private university in eastern part of Indonesia. We used a vector autoregressive (VAR) model with variables including tuition fee, inflation rate, number of enrolled students and regional minimum wage. The data covers from January 2010 to December 2018 and were collected from the private university, the Bank Indonesia, the Central Bureau of Statistics (CBS), and the South Sulawesi office CBS. We carried out a step-by-step procedure that consists of stationarity test, optimal lag determination, and significance of parameters test as well as un-correlatedness of residuals and structure stability tests. We found that the tuition fee and the number of students are affected by inflation. This result gives impact on tuition fee prediction.
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