Abstract
Bali is known as one of the most popular tourism destination in the world. The number of tourist visit to Bali increases every year. In 2010, there roughly 7 millions tourist visits to Bali and reach up to 14 million people by the end of 2017. This increased in number may affect the growth of tourism industries and economic growth in Bali Province. This study aims to analyze the patterns of causal relationship between tourism industry receipts, tourist visits, and economic growth in Bali based on time series data using vector autoregressive (VAR) model. The results conclude the following: (i) foreign tourist visits is significantly affect economic growth. In addition, economic growth, domestic tourist visits, and foreign tourist visits are significantly impact to tourism industry receipts, (ii) economic growth would affect the tourism industry receipts in the next four consecutive months, (iii) the forecasting result of economic growth with VAR model is highly accurated with MAPE 2%.
Highlights
Bali is known as one of the most popular tourism destination in the world
In 2010, there roughly 7 millions tourist visits to Bali and reach up to 14 million people by the end of 2017. This increased in number may affect the growth of tourism industries and economic growth in Bali Province
This study aims to analyze the patterns of causal relationship between tourism industry receipts, tourist visits, and economic growth in Bali based on time series data using vector autoregressive (VAR) model
Summary
Data deret waktu (time series) adalah sekumpulan pengamatan yang disusun dalam urutan waktu seperti data tahunan, triwulanan, dan bulanan. Salah satu model deret waktu multivariate yang sering digunakan adalah vector autoregressive (VAR), model ini digunakan untuk menganalisis hubungan dinamis dari sejumlah variabel secara simultan dari peubah-peubah deret waktu yang stasioner. Model vector autoregressive (VAR) umum digunakan dalam sistem peramalan dari data deret waktu (time series) yang saling berhubungan dan untuk menganalisis dampak dinamis dari sistem dengan sejumlah variabel. (tidak terdapat akar unit atau data stasioner) dengan statistik uji untuk adalah :. . Dalam uji ini dapat digunakan statistik uji trace dengan adalah nilai eigen ke–i dari matriks. H0 : Tidak terdapat autokorelasi pada residual model dari lag 1 sampai , H1 : Terdapat paling sedikit satu autokorelasi pada residual model dari lag 1 sampai , Adapun statistik uji yang digunakan adalah statistik sebagai berikut:. Uji Kointegrasi Johansen (jika data tidak stasioner pada level dan terintegrasi pada orde yang sama)
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