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Rukini Rukini
Badan Pusat Statistik Provinsi Bali
Economics, Econometrics & Finance

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Peramalan Jumlah Kunjungan Wisatawan Mancanegara (Wisman) Ke Bali Tahun 2019: Metode ARIMA Rukini Rukini; Putu Simpen Arini; Esthisatari Nawangsih
Jurnal Ekonomi Kuantitatif Terapan 2015: Vol. 8, No. 2, Agustus 2015 (pp. 113-216)
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (721.502 KB) | DOI: 10.24843/JEKT.2015.v08.i02.p04

Abstract

IndonesiaTourism has been growing significantly over the past few years. In 2013, income from tourism reached 10,054 millions dollars, occupied the third place of indonesia export commodities. The ministry of tourism and creatice economy has targeted 20 millions arrivals and 240 trillion rupiahs from international tourist arrivals in 2019. As Bali has been contributing for more than 40 percent of international tourist arrivals in Indonenesia, it is expected to have around 8 millions international arrivals in 2019. Using ARIMA method, it is predicted that the number of international tourist arrivals in Bali will be 5,07 millions in 2019, far below the target. This result suggests that government should give more effort to develop tourism in the upcoming years to fulfil the target.
Model ARIMAX Dan Deteksi GARCH Untuk Peramalan Inflasi Kota Denpasar Tahun 2014 Rukini Rukini
Jurnal Ekonomi Kuantitatif Terapan 2014: Vol. 7, No. 2, Agustus 2014 (pp. 83-198)
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1282.475 KB) | DOI: 10.24843/JEKT.2014.v07.i02.p09

Abstract

Inflation is an important indicator that can provide information on the development of prices of goods and services consumed by public. Forecasting inflation is important in order to assist the government in taking monetary policy to maintain economic stability in the future. In general, forecasting inflation can be done with time series approach, causal approach, and a combination of time series and causal approaches. Models with a combined approach that is widely used for forecasting inflation is ARIMAX model that includes Transfer Function and Intervention Model or also known as dynamic regression models. In addition, Generalized Autoregresive Conditional Heteroscedasticity (GARCH) for variance has also been applied to models in forecasting inflation. This study explains the procedure of building the ARIMAX models and GARCH detection using a case study of inflation data in denpasar city. Predictor variables  consist of metric data variable (ie number of foreign tourists) and non-metric data variables (the increase of fuel oil (BBM) ), basic electricity tariff (TDL) and  Bali bombings). The best model for in-sample data is intervention model with the smallest value of AIC and SBC, whereas the best model for data out-sample is  transfer function model with the smallest RMSE value. GARCH detection results with Langrange Multiplier test shows no evidence of heteroscedasticity in  ARIMAX model.
Co-Authors Esthisatari Nawangsih Putu Simpen Arini