Islamic banks in Indonesia exist side by side with their conventional counterparts within a dual banking system. The central bank aims to achieve price stability in the economy using both conventional and Islamic monetary instruments within this dual monetary system. This creates a unique environment for Islamic banks. This research aims to examine the role of Islamic banks in the monetary policy transmission mechanism using Granger Causality and Autoregressive Distributed Lag (ARDL). The balance sheet components of deposit and financing are hypothesized to function in the monetary transmission process within the bank financing channel. Granger causality reveals that the Islamic interbank overnight rate Granger causes Islamic deposits and financing, and that these in turn Granger cause the industrial production index. This index Granger causes inflation, Islamic deposits, and the Islamic interbank overnight rate. Islamic deposits and inflation then Granger cause the Islamic interbank overnight rate. The ARDL results show cointegrating relationships in the output and inflation model. Long-term convergence could be achieved to correct deviations in output and inflation by way of Islamic banks' deposits and financing. However, there is only a short-term influence of Islamic bank deposits on output. In the short-run, these deposits do not contribute to inflation. Islamic bank financing does not have a short-term relationship with output and inflation; therefore, there is declining effectiveness of Islamic banks' financing contribution to the economy.

Purpose: This research purports to forecast the number of foreign tourists arriving at major airport in Indonesia. The airports chosen are Soekarno Hatta, Juanda, I Gusti Ngurah Rai, and Kualanamu international airports. Design/methodology/approach: The data used were foreign tourists arrival at major airports located in Jakarta, Surabaya, Medan, and Denpasar. The data extended from January 2014 until December 2018. Two time-series methods were employed, namely Holt-Winter Seasonality and Exponential Smoothing with maximum likelihood. The forecasts would reveal the fitted numbers of foreign tourists arriving from January 2019 until December 2019. The fitted numbers would then be compared to the actual numbers of January 2019 to December 2019. Findings: The results showed that, overall, Holt-Winters seasonality excel at forecasting foreign tourists arrival at Soekarno Hatta and Juanda international airports. While Exponential Smoothing perform better for prediction at I Gusti Ngurah Rai and Kualanamu international airports. The MAPE for Holt-Winters at Soekarno Hatta and Juanda international airports were 26.1585% and 14.538%. The MAPE for Exponential Smoothing at at I Gusti Ngurah Rai and Kualanamu international airports were 7.76% and 15.6791%. Research limitations/implications: Forecasting for foreign tourist arrival at Soekarno Hatta and Juanda international airports should employ Holt-Winters approach. Forecasting for foreign tourists arrival at I Gusti Ngurah Rai and Kualanamu international airports should employ Exponential Smoothing with maximum likelihood. Practical implications: Certain forecasting methods work better than the others at certain international airports. Many forercasting methods are available. Two methods are specifically prominent for detecting seasonality and trend, i.e Holt-Winters and Exponential Smoothing with maximum likelihood. Originality/value: Most research focus on one method at a time. This research compares two methods so that we can know better which method is suitable for certain airports. Four international airports are sampled in this study. Not many research focus on several places at a time. Paper type: Research paper

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.

The research aims to investigate the dynamics among rural banks’ capital, macroeconomic variables and microconomic variables. Macroeoconomic variable consists of infllation and interest. Microeconomic variables consist of loan to deposi ratio, nonperforming loans, and return on assets. The data are excerpted from OJK and BI’s website. The data are monthly data extending from January 2010 until May 2021. The testing method used is vector error correction model (VECM). The results show that rural banks’ capital is significantly affected the previous state of capital and profitability. This indicates the importance of sustainability of capital in rural banks and how it is very much dependent upon the profitability of the rural banks. Further, the research results show that there ar two cointegrating functions in the model. Both cointegration functions are influential to inflation. The speed adjustment derived from the residuals of capital function is 0.6754% and 13.5669% for residual from inflation function itself. The slow adjustment process is due to the small market share and assets of rural banking sector. In addition, capital, nonperforming loans, and return on assets are pivotal for central bank monetary policy to control inflation.