ARDL Method: Forecasting Data Kemiskinan di NTB

Rahmasari, Aulia; Sunani, Eka Hawari; Jannah, Miftahul; Fathulaili, Fathulaili; Kurnia, Linda; Satria, Ahmad

doi:10.31764/jtam.v3i1.767

Cited by 4 publications

(3 citation statements)

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“…Metode ARDL merupakan salah satu bentuk metode dalam ekonometrika. Metode ini dapat mengestimasi model regresi linear dalam menganalisis hubungan jangka panjang yang melibatkan adanya uji kointegrasi diantara variabel-variabel times series (Rahmasari et al, 2019).…”

Section: Metode Ardlunclassified

Pengaruh Ekspor, Investasi, Dan Indeks Pembangunan Manusia (Ipm) Terhadap Produk Domestik Bruto (Pdb) Di Indonesia

Naila,

Abbas,

Abubakar

2024

JERU

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This research aims to determine the influence of Exports, Investment and the Human Development Index (HDI) on Gross Domestic Product (GDP) in Indonesia in 1993 - 2022. This research uses secondary data in the form of a time series for 30 years. This research uses the Autoregressive Distributed Lag (ARDL) test analysis method. The results of this research show that in the short term the export variable has a positive and significant effect, and in the long term exports has a positive and insignificant effect on GDP in Indonesia. In the short term, investment variables have a positive and significant effect, as well as in the long term, investment has a positive and significant effect on GDP in Indonesia. In the short term HDI has a negative and significant effect, and in the long term HDI has a negative and insignificant effect on GDP in Indonesia. It is hoped that the Government can increase GDP by improving people's welfare, so that it can have an impact on people's ability to carry out production which can ultimately be exported to other countries.

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Section: Metode Ardlunclassified

Pengaruh Ekspor, Investasi, Dan Indeks Pembangunan Manusia (Ipm) Terhadap Produk Domestik Bruto (Pdb) Di Indonesia

Naila,

Abbas,

Abubakar

2024

JERU

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“…Analisis runtun waktu merupakan analisis sekumpulan data dalam suatu periode waktu yang lampau yang berguna untuk mengetahui atau meramalkan kondisi masa mendatang [17]. Analisis peramalan atau proyeksi ini merupakan langkah terakhir setelah dilakukan estimasi parameter model yang cocok yang dilakukan dalam penelitian ini, yakni model ARIMA (1,1,0) dengan plot data ditampilkan pada Gambar 3.…”

Section: Analisis Proyeksi Arima (110)unclassified

Implementasi Model Autoregressive Integrated Moving Average pada Proyeksi Komoditas Ekspor Timah

Dalimunthe,

Aldila

2023

Jambura J. Math

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The Bangka Belitung Archipelago is a potential area in the mining sector because many soils contain tin minerals and minerals that are spread evenly. Based on this phenomenon, this study uses the ARIMA model to analyze the prediction of the number of tin export commodities in the Bangka Belitung Islands Province. The time series data used in this study begins in January 2020 and ends in September 2022, with projected results ending in June 2023. Based on the analysis results, it is found that the ARIMA model (1,1,0) is the best model that can be used to project the value of tin export commodities in the Bangka Belitung Islands Province. This model was selected through the results of the correlogram test, which shows that the data is cut off at the second lag for the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. This projection analysis was carried out after passing the stationarity test first through the Augmented Dicky Fuller (ADF) test. Through this test, it is found that the data is stationary at the first difference, and the prob value is 0.0003 with the projected result that there will be an increase in the number of exports of tin commodities with a total increase of 0.03%. The results of this analysis can certainly be part of preventive actions for the government to be able to assist the country in increasing the country’s foreign exchange through increasing export commodities.

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“…The Naïve method is more accurate in forecasting poverty data than the Moving Average method (Kumila et al, 2019). In addition, the Autoregressive Distributed Lag method can be used to Forecasting accurately poverty data in West Nusa Tenggara Province (Rahmasari et al, 2019).…”

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confidence: 99%

Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

Silalahi

2020

JTAM

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The government continues to carry out poverty reduction strategies in Indonesia, especially in West Java Province. West Java Province is a province that has the most populous population in Indonesia. This will affect the level of welfare and the amount of poverty. The strategy undertaken is inseparable from accurate poverty data and is available from year to year. Even from the available data, the government can forecast the number of poor people in the coming years. Seasonal Autoregressive Integrated Moving Average (SARIMA) method is one of forecasting methods. SARIMA is the development of the ARIMA model which has a seasonal effect. Based on the results of the study, that poverty data forecasting in the province of West Java using the SARIMA method obtained SARIMA model (0,1,1) (1,1,1)4. This model is the best model for forecasting data with an R-Squared value of 98%, Mean Square Error is 7.705.5800.000 and Mean Absolute Percentage Error IS 2,81%. It’s means this SARIMA model is very good in predicting poverty data in West Java Province.

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ARDL Method: Forecasting Data Kemiskinan di NTB

Cited by 4 publications

References 0 publications

Pengaruh Ekspor, Investasi, Dan Indeks Pembangunan Manusia (Ipm) Terhadap Produk Domestik Bruto (Pdb) Di Indonesia

Pengaruh Ekspor, Investasi, Dan Indeks Pembangunan Manusia (Ipm) Terhadap Produk Domestik Bruto (Pdb) Di Indonesia

Implementasi Model Autoregressive Integrated Moving Average pada Proyeksi Komoditas Ekspor Timah

Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province

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