scholarly journals THE ROLE OF MASS COMMUNICATIONS TO THE MARKET INTERVENTIONS OF RICE COMMODITY IN INDONESIA (Peran Komunikasi Massa terhadap Intervensi Pasar Komoditas Beras di Indonesia)

2019 ◽  
Vol 9 (2) ◽  
pp. 173-186
Author(s):  
Kumara Jati

AbstractThe rice is a staple food for the people and significantly contributes to economic development in Indonesia. Occasionally a market intervention should be implemented by the Government of Indonesia during the low harvest season to control and to manage the price of rice and the inflation, so low-income society could meet their basic needs. This study examines how communication aspect is really important as a part of market intervention mechanism to control the price and the stock of rice in Indonesia. Autoregressive and Moving Average, Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity, and the Structural Time-Series Model are applied  with a dummy variable on daily and monthly data of the stock and the price of rice from January 1, 2015 until June 27, 2016. It can be inferred from the data that the form of mass communication by the government to relevant stakeholders (channel distribution and consumers) can run well, especially in order to maintain the supply and the price stabilization of rice. Nevertheless, the ARMA(1,1)-GARCH(1,1) model with dummy variables, inter alia mass communication, and also the number of market operations and rice policy, are not so influential on the price of rice, but more influence on the stock of rice. Then, the Structural Time-Series Model shows that the fluctuation of price and stock is affected by seasonal and cycle components especially more fluctuated in the month of January-March. Therefore, the relevant authorities are expected to maximize the rice policy in order to maintain the price stability in the short term, medium term and long term. AbstrakBeras merupakan makanan pokok bagi masyarakat dan secara signifikan berkontribusi terhadap pembangunan ekonomi di Indonesia. Terkadang intervensi pasar harus dilaksanakan oleh pemerintah diluar musim panen untuk mengendalikan dan mengelola harga beras dan inflasi, sehingga masyarakat berpenghasilan rendah dapat memenuhi kebutuhan mereka. Penelitian ini mengkaji bagaimana aspek komunikasi sangat penting sebagai mekanisme intervensi pasar untuk mengendalikan harga dan stok beras di Indonesia. Autoregressive and Moving Average and Autoregressive Conditional Heteroskedasticity / Generalized Autoregressive Conditional Heteroskedasticity serta the Structural Time-Series Model  digunakan dengan variabel dummy pada data stok dan harga beras, baik harian maupun bulanan, antara 1 Januari 2015 hingga 27 Juni 2016. Hasil analisis menyimpulkan bahwa komunikasi massa oleh pemerintah kepada pihak-pihak yang berkepentingan (pelaku usaha dan konsumen) dapat berjalan dengan baik terutama untuk menjaga pasokan dan stabilitas harga beras. Namun demikian, model ARMA(1,1)-GARCH(1,1) dengan variabel dummy yaitu komunikasi massa, serta jumlah operasi pasar dan kebijakan beras kurang berpengaruh terhadap harga beras namun lebih berpengaruh terhadap stok beras. Kemudian, the Structural Time-Series Model menunjukkan bahwa naik turunnya harga dan stok beras berasal dari komponen musiman dan siklus terutama lebih berfluktuasi pada bulan Januari-Maret. Oleh karena itu, otoritas terkait diharapkan dapat memaksimalkan kebijakan beras untuk menjaga stabilitas harga dan stok beras dalam jangka pendek, menengah, dan panjang. 

2019 ◽  
Vol 35 (1) ◽  
pp. 9-30
Author(s):  
Reinier Bikker ◽  
Jan van den Brakel ◽  
Sabine Krieg ◽  
Pim Ouwehand ◽  
Ronald van der Stegen

Abstract Seasonally adjusted series of Gross Domestic Product (GDP) and its breakdown in underlying categories or domains are generally not consistent with each other. Statistical differences between the total GDP and the sum of the underlying domains arise for two reasons. If series are expressed in constant prices, differences arise due to the process of chain linking. These differences increase if, in addition, a univariate seasonal adjustment, with for instance X-13ARIMA-SEATS, is applied to each series separately. In this article, we propose to model the series for total GDP and its breakdown in underlying domains in a multivariate structural time series model, with the restriction that the sum over the different time series components for the domains are equal to the corresponding values for the total GDP. In the proposed procedure, this approach is applied as a pretreatment to remove outliers, level shifts, seasonal breaks and calendar effects, while obeying the aforementioned consistency restrictions. Subsequently, X-13ARIMA-SEATS is used for seasonal adjustment. This reduces inconsistencies remarkably. Remaining inconsistencies due to seasonal adjustment are removed with a benchmarking procedure.


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