scholarly journals ARFIMA Modelling for Tectonic Earthquakes in The Maluku Region

2021 ◽  
Vol 5 (1) ◽  
pp. 39-49
Author(s):  
Ferry Kondo Lembang ◽  
Lexy Janzen Sinay ◽  
Asrul Irfanullah
Keyword(s):  
Long Memory ◽  
Time Series Data ◽  
Series Data ◽  
Earthquake Intensity ◽  
The Third ◽  
Tectonic Earthquakes ◽  
History Of ◽  
Arfima Model ◽  
Second Period

Maluku Province is one of the regions in Indonesia with a very active and very prone earthquake intensity because it is a meeting place for 3 (three) plates, namely the Eurasian, Pacific and Australian plates. In the last 100 years, the history of tectonic earthquakes with tsunamis that occurred in Indonesia was 25-30% occurring in the Maluku Sea and Banda Sea. Based on this fact, this study aims to analyze the incidence of tectonic earthquakes that occurred in the Maluku region and its surroundings using the Autoregressive Fractionally Integrated Moving Averages (ARFIMA) model which has the ability to explain long-term time series data (long memory). The results of the research data analysis show that the best model for predicting the number of tectonic earthquakes that occur in Maluku and its surroundings is ARFIMA (0; 0.712; 1) with an MSE value of 0.1156. Meanwhile, the best model for predicting the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings is ARFIMA (0; -3,224 x 10-9; 1) with an MSE value of 0.01237. Based on the two best models, the prediction results obtained from the number of tectonic earthquakes and the average magnitude of the number of tectonic earthquakes that occurred in Maluku and its surroundings for the next three periods, namely the first period there were 31 tectonic earthquakes with an average magnitude of 4.38481 SR. the second period there were 32 tectonic earthquakes with an average magnitude of 4.38407, and the third period there were 32 tectonic earthquakes with an average magnitude of 4.38333.

scholarly journals Causality Effects among Gross Capital Formation, Unemployment and Economic Growth in South Africa

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 26 ◽  
Author(s):  
Michael Takudzwa Pasara ◽  
Rufaro Garidzirai
Keyword(s):  
Economic Growth ◽  
South Africa ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Capital Formation ◽  
Series Data ◽  
Vector Autoregressive ◽  
The Third ◽  
Short Run

Stagnant economic growth, decreasing investment and high unemployment remain consistent macroeconomic challenges for South Africa. Gross Capital formation (GCF) is designed to improve employment and economic growth (GDP). This study investigates the causality effects of the three variables using time series data from 1980 to 2018 in a Vector Autoregressive (VAR) framework. Results of the first model reveal a positive long-term relationship between gross capital formation GCF and economic growth GDP. Contrariwise, the first model indicates that unemployment (UNEMP) does not influence economic growth (GDP) in the short run. The second model results reveal a significant and positive relationship between UNEMP and GCF, while the third model shows an inverse relationship between GDP and UNEMP. Based on these findings, the study therefore recommends that fiscal authorities introduce expansionary fiscal policy that stimulates economic growth, investment and employment.

scholarly journals PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)

2018 ◽  
Vol 7 (1) ◽  
pp. 96-109
Author(s):  
Helmi Panjaitan ◽  
Alan Prahutama ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Moving Average ◽  
Step Function ◽  
Local Economy ◽  
Stationary Time Series ◽  
Series Data ◽  
Short Term

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

Economic Turmoil in Islamic Banking Investment

ETIKONOMI ◽  
2020 ◽  
Vol 19 (2) ◽  
Author(s):  
Budiandru Budiandru ◽  
Sari Yuniarti
Keyword(s):  
Interest Rates ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Islamic Banking ◽  
Vector Error Correction ◽  
Investment Financing ◽  
Vector Error ◽  
Operational Activities

Investment financing is one of the operational activities of Islamic banking to encourage the real sector. This study aims to analyze the effect of economic turmoil on investment financing, analyze the response to investment financing, and analyze each variable's contribution in explaining the diversity of investment financing. This study uses monthly time series data from 2009 to 2020 using the Vector Error Correction Model (VECM) analysis. The results show that the exchange rate, inflation, and interest rates significantly affect Islamic banking investment financing in the long term. The response to investment financing is the fastest to achieve stability when it responds to shocks to the composite stock price index. Inflation is the most significant contribution in explaining diversity in investment financing. Islamic banking should increase the proportion of funding for investment. Customers can have a larger business scale to encourage economic growth, with investment financing increasing.JEL Classification: E22, G11, G24How to Cite:Budiandru., & Yuniarti, S. (2020). Economic Turmoil in Islamic Banking Investment. Etikonomi: Jurnal Ekonomi, 19(2), xx – xx. https://doi.org/10.15408/etk.v19i2.17206.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

Does Public Debt Service Expenditure Crowd-Out Economic Growth? Empirical Evidence from an African Developing Country

2019 ◽  
Vol 64 (3) ◽  
pp. 23-38
Author(s):  
Talknice Saungweme ◽  
Nicholas M. Odhiambo
Keyword(s):  
Economic Growth ◽  
Public Debt ◽  
Time Series Data ◽  
Policy Formulation ◽  
Debt Service ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
The Impact

Abstract This paper contributes to the ongoing debate on the impact of public debt service on economic growth; and it provides an evidence-based approach to public policy formulation in Zimbabwe. The empirical analysis was performed by applying the autoregressive distributed lag (ARDL) technique to annual time-series data from 1970 to 2017. The study findings reveal that the impact of public debt service on economic growth in Zimbabwe is negative in the short run but positive in the long run. The results are suggestive of the existence of a crowding-out effect of public debt service in Zimbabwe in the short run and a crowding-in effect in the long run. In view of these findings, the government should consider fiscal and financial policies that promote a constant supply of long-term finance, long-term fixed investments, and extension of a government securities maturity structure so as to ensure sustainable short- and long-term public debt service expenditures. The study further recommends the strengthening of non-distortionary revenue mobilisation reforms to reduce market distortions and boost domestic investment.

scholarly journals Highly comparative time-series analysis: the empirical structure of time series and their methods

2013 ◽  
Vol 10 (83) ◽  
pp. 20130048 ◽  
Author(s):  
Ben D. Fulcher ◽  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Scientific Data ◽  
Series Data ◽  
Series Analysis ◽  
Analysis Methods ◽  
Scientific Disciplines ◽  
History Of ◽  
Reduced Representations

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

scholarly journals Beyond the implications of Grand Ethiopian Renaissance Dam filling policies

2021 ◽  
Vol 7 (3) ◽  
pp. 313-330
Author(s):  
Abay Yimere ◽  
◽  
Engdawork Assefa ◽  
Keyword(s):  
Time Series Data ◽  
Water Levels ◽  
Series Data ◽  
Future Climate Change ◽  
Policy Debate ◽  
International Treaty ◽  
Term Operation ◽  
Operating Level ◽  
Minimum Operating

<abstract> <p>The Grand Ethiopian Renaissance Dam (GERD) in Ethiopia and High Aswan Dam (HAD) in Egypt both operate on the Nile River, independent of a governing international treaty or agreement. As a result, the construction of the GERD, the Earth's eighth largest dam, ignited a furious debate among Ethiopia, Sudan, and Egypt on its filling policies and long-term operation. Ethiopia and Egypt's stance on the Nile River's water resources, combined with a nationalistic policy debate on the GERD's filling policies and long-term operation, has severely affected progress toward reaching agreeable terms before the first round of GERD filling was completed. These three countries continue to debate on the terms of agreement for the second round of GERD filling, scheduled to start by July 2021. We examined the GERD filling strategy for five- and six-year terms using time series data for the periods 1979–1987 and 1987–1992 to combine analyses for dry and wet seasons and investigate the potential impacts of filling the GERD above the downstream HAD using four HAD starting water levels. A model calibrated using MIKE Hydro results shows that during both five- and six-year terms of future GERD filling, Egypt would not need to invoke the HAD's minimum operating level. We pursued a narrative approach that appeals to both a technical and non-technical readership, and our results show the urgent need for cooperation at both policy and technical levels to mitigate and adapt to future climate change through the development of climate-proof agreements. Moreover, the results call for the riparian countries to move away from the current nationalistic policy debate approach and pursue a more cooperative, economically beneficial, and climate adaptive approach.</p> </abstract>

scholarly journals THE TESTING OF MONEY NEUTRALITY IN ECONOMIC GROWTH OF INDONESIA

2017 ◽  
Vol 1 (1) ◽  
pp. 12
Author(s):  
Muammil Sun’an ◽  
Amran Husen
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Output Growth ◽  
Broad Sense ◽  
Series Data ◽  
Analysis Tool ◽  
Real Sector ◽  
Short Term ◽  
Correction Model

<p>This study aim is to test the money neutrality in a narrow sense (M1) and a broad sense (M2) to the growth of output (GDP) in Indonesia, both in short term and long term. This research uses quarterly time series data at 2010 - 2016 periods. The analysis tool used is Error Correction Model (ECM). The results show that short-term money supply (M1 and M2) affect on output growth. However, in the long term, only money circulation in a broad sense (M2) affects on output growth, which also means that money is not neutral because it affects the real sector (GDP).</p><p> <strong>Keywords:</strong> M1, M2, Population, Capital, and Economic Growth.</p>

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