Indonesian Export Analysis: Autoregressive Distributed Lag (ARDL) Model Approach

Labibah, Syarifah; Jamal, Abd.; Dawood, Taufiq C.

doi:10.14414/jebav.v23i3.1668

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…They found that the efficacy of the forecast results was being outperformed by the Vector Autoregressive Model (VAR) with only one lag. The result of this study also agrees with Labibah, Jamal, & Dawood (2021), who established ARDL with 1 lag model as the best model. Forecasting future value depends on the last value of the variable in question since it contains the most information.…”

Section: Resultssupporting

confidence: 92%

Inflation forecasting using autoregressive distributed lag (ARDL) models

Ponziani

2023

JESP

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This study attempts to evaluate and compare the inflation-predicting performance of several ARDL models. Since there was no cointegration, the ARDL model does not employ an error correction term. Subsequently, model development showed that ARDL(2,2) should be used. Besides the formally developed model, some other more arbitrarily chosen ARDL models were also included, i.e., ARDL(1,1), ARDL(2,0), ARDL(1,0), ARDL(0,1), and ARDL(0,2). This research measures forecasting performance with inflation as the forecasting object. The duration of the monthly inflation statistics ranged from January 2011 to July 2022. The data were separated into two categories. The training data ranged between January 2011 and December 2021. After getting the appropriate parameters from the training data, the models generated projections from January 2022 to July 2022. The research determined that ARDL (1,0) was the most accurate inflation forecasting model, followed by ARDL (0,2) and formally constructed ARDL(2,2) finished in fourth place. This study suggests that the formal development of ARDL for forecasting purposes is unnecessary. Formal ARDL development is more appropriate for root cause analysis. In addition, the single autoregressive component indicates that most of the inflation value's information originated from the prior period. This suggests that the previous period's value is Indonesia's most significant predictor of inflation. The impact of greater period lags on inflation forecasting diminishes immediately.

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Section: Resultssupporting

confidence: 92%

Inflation forecasting using autoregressive distributed lag (ARDL) models

Ponziani

2023

JESP

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“…Table 1 shows the results of the unit root test using Augmented Dickey-Fuller, which demonstrated that the variables under examination are stationary at the level and first difference. The presence of unit roots in the data suggests that shocks have a long or short term impact (Labibah et al, 2021). Figure 1 shows the trend in Nigerian coffee production from 1981 to 2019, as well as a 10-year prediction.…”

Section: Resultsmentioning

confidence: 99%

“…The ARDL is used on time series data with integration orders of I (0) and I (1) (i.e. mixed order of integration) to generate an unbiased long-run estimate where a long-run connection exists (Bawa et al, 2016;Udoh et al, 2015;Labibah et al, 2021). The fundamental justification for using an ARDL model must be provided by an underlying economic theory or model, such as the neoclassical growth model.…”

Section: The Auto-regressive Distributed Lag Modelmentioning

confidence: 99%

Dynamics of coffee output in Nigeria

2022

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Coffee is a strategic cash crop for poverty reduction and economic growth in Nigeria, and it is consumed worldwide, making it a significant source of income at both the micro and macro levels. This study analysed the trends in ' 'Nigeria's coffee output and the short and long-run determinants of coffee output in Nigeria. A period of 38 years was considered and the data were sourced from the Food and Agriculture Organization, the World Bank and the International Coffee Organization. The linear trend and the Autoregressive Distributed Lag Model were instrumental in the data analysis. The trend analysis reveals that coffee output is decreasing, necessitating immediate action. Fertiliser use and land availability for farmers require extra attention in the short run because they are significant and had a positive impact on coffee output. In the long-run climate change, producer price and fertiliser use negatively impact the coffee farmers' output. The need to make land easily accessible to coffee farmers by amending land use regulations to ensure the conservation and expansion of farmlands is one of the most notable recommendations of this study.

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“…Nilai ECT pada Tabel 6, baik model hybrid maupun model makro, memiliki koefisien negatif dan signifikan. Hal tersebut mengartikan bahwa model tersebut dapat terkoreksi dan mencapai keseimbangan jangka panjangnya (Labibah et al, 2021). Dalam jangka pendek, variabel mikrostruktur memiliki pengaruh yang Kammoun, 2021).…”

Section: Pengaruh Model Hybrid Terhadap Nilai Tukar Menggunakan Ardlunclassified

Untitled

2023

JEI

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One barrier to a faster economic recovery is exchange rate volatility, which raises the need for appropriate exchange rate movement model. The fluctuation of the rupiah exchange rate versus the US dollar is examined in this study using a hybrid model, which combines microstructural and macroeconomic methodologies using ARDL approach. The results show that the hybrid model and exchange rate changes have a long-term and short-term relationship. Microstructural variables, specifically spreads and high-low spreads, are important in explaining exchange rates movements. Long-term effects of the money supply, net foreign assets, and interest rates on the macroeconomic system are negligible, except for price levels that can explain significant exchange rate movements. A hybrid model can be used to close the gap in macroeconomic relations in explaining exchange rates by incorporating microstructural elements.

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Indonesian Export Analysis: Autoregressive Distributed Lag (ARDL) Model Approach

Cited by 6 publications

References 11 publications

Inflation forecasting using autoregressive distributed lag (ARDL) models

Inflation forecasting using autoregressive distributed lag (ARDL) models

Dynamics of coffee output in Nigeria

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