scholarly journals ARIMA Intervention Model for Measuring the Impact of the Lobster Seeds Fishing and Export Ban Policy on the Indonesian Lobster Export

2021 ◽  
Vol 2123 (1) ◽  
pp. 012011
Author(s):  
Ria Dhotul Ilmiah ◽  
Siskarossa Ika Oktora
Keyword(s):  
Time Series ◽  
Natural Disaster ◽  
Analytical Method ◽  
Step Function ◽  
Intervention Model ◽  
Policy Changes ◽  
Sales Promotions ◽  
External Event ◽  
The Impact

Abstract An intervention model is an analytical method for evaluating or measuring the impact of an external event called intervention, such as a natural disaster, holidays, sales promotions, and other policy changes. Two types of intervention variables will be used to represent the presence or absence of the event, i.e., a pulse or step. The pulse function is used to represent a temporary intervention, whereas the step function shows a long-term intervention. This study aims to develop a time series model with an intervention of step function for measuring the impact of two policies related to the prohibition of fishing and the export of lobster seeds on the export value of Indonesian lobster. These policies are the Ministerial Regulation No.1 of 2015 since January 2015 related banning of lobster seeds fishing (called first intervention) and the Ministerial Regulation No. 56 of 2016 since January 2017 related lobster seeds fishing and export ban policy (called second intervention). These regulations are designed to ensure lobster sustainability and add value to lobsters that are currently overfished. The results show that both policies significantly affect the export value of lobster in Indonesia, and the interventions have a permanent impact.

scholarly journals Analisis Intervensi Kebijakan Kenaikan Tarif Dasar Listrik (TDL) Tahun 2017 Terhadap Pemakaian Listrik Wilayah SULSELRABAR

2020 ◽  
Vol 2 (1) ◽  
pp. 31
Author(s):  
Ahmad Zaki ◽  
Rahmat Syam ◽  
Ahmad Firjatullah Hakim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Step Function ◽  
Government Policies ◽  
Series Data ◽  
Intervention Analysis ◽  
Intervention Model ◽  
Diagnosis Model ◽  
The Impact ◽  
Time Of Intervention

Penelitian ini merupakan penelitian terapan mengenai analisis intervensi yang memodelkan data time series yang dipengaruhi oleh adanya suatu kejadian atau intervensi Penelitian ini bertujuan untuk menentukan model intervensi fungsi step dengan waktu intervensi T (mei 2017) yang didapatkan dari proses pemodelan ARIMA preintervensi, identifikasi responintervensi, estimasi parameter intervensi dan pemeriksaan diagnosis model intervensi. Adapun data yang digunakan adalah data pemakaian listrik (dalamKWh), kategori rumah tangga dengan daya 900 VA, wilayah Sulawesi Selatan Tenggara Barat (SULSELRABAR) periode Januari 2016 sampai dengan Desember 2017 yang diperoleh dari PT. PLN Persero Wilayah SULSELRABAR Makassar. Berdasarkan hasil analisis didapatkan bahwa terjadi penurunan terhadap pemakaian listrik pada bulan setelah terjadinya intervensi sebagai dampak dari kebijakan pemerintah yang menaikkan tarif dasar listrik (didefinisikan sebagai intervensi).Kata kunci: Analisis intervensi, fungsi step, ARIMA, time series This research is an implementation research about intervention analysis that modelling time series data effected by the existence of an event or intervention. This research aimed to determine the model of intervention of  the step function with time of intervention (T) derived from process of ARIMA preintervensi modelling, identification of response of intervention, intervention parameter estimation and examination diagnosis of intervention model. As for the data that was used in the form of data of the using of electricity (in KWh), the category of households with power of  900 VA, South Southeast West Sulawesi Region  (SULSELRABAR) from January, 2016 to December, 2017 were obtained from PT PLN Persero SULSELRABAR Area Of Makassar. Based on the analysis result obtained that there is derivation towards the using of electricity in the month after the intervention, it shows the impact of government policies that raising the electricity base tarif rate (defined as the intervention).Keywords: Intervention Analysis, Step Function, ARIMA, Time Series.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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.

scholarly journals Do Long-Term Natural Disasters Influence Social Trust? Empirical Evidence from China

2021 ◽  
Vol 18 (14) ◽  
pp. 7280
Author(s):  
Yao Li ◽  
Haoyang Li ◽  
Jianqing Ruan
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Social Trust ◽  
Geographical Location ◽  
Household Survey ◽  
Flood Frequency ◽  
Psychological Characteristics ◽  
The North ◽  
The Impact

The natural environment is one of the most critical factors that profoundly influences human races. Natural disasters may have enormous effects on individual psychological characteristics. Using China’s long-term historical natural disaster dataset from 1470 to 2000 and data from a household survey in 2012, we explore whether long-term natural disasters affect social trust. We find that there is a statistically significant positive relationship between long-term natural disaster frequency and social trust. We further examine the impact of long-term natural disaster frequency on social trust in specific groups of people. Social trust in neighbors and doctors is stronger where long-term natural disasters are more frequent. Our results are robust after we considering the geographical difference. The effect of long-term natural disasters remains positively significant after we divide the samples based on geographical location. Interestingly, the impact of long-term flood frequency is only significant in the South and the impact of long-term drought frequency is only significant in the North.

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.

Advancing SME Sustainability

2021 ◽  
pp. 159-178
Author(s):  
Meng Kui Hu ◽  
Daisy Mui Hung Kee
Keyword(s):  
Financial Crisis ◽  
Global Financial Crisis ◽  
Policy Changes ◽  
Business Environments ◽  
Business Operations ◽  
New Business ◽  
Spanish Flu ◽  
The Global Financial Crisis ◽  
The Impact

The world has been struck by multiple crises that crippled the socio-economy of nations in the past. The impact of these crises was so significant that they initiated numerous policy changes worldwide. The radical crises in this context refer to the Spanish flu, the Asian financial crisis, the global financial crisis, and the current COVID-19 pandemic. Due to their small capital structure with limited resources and fragile nature, SMEs were severely impacted by these crises. Many SMEs were forced to close down their business operations. Somehow, the remaining SMEs managed to persist and survive through the crises. Moving forward, SMEs can better prepare for future crises by understanding and learning from the predicaments of these past crises. Consequently, SMEs must also be adaptive to new business environments and responding promptly to crises by realigning their strategies to achieve business sustainability in the long term.

scholarly journals Expansions in Maternity Leave Coverage and Children's Long-Term Outcomes

2012 ◽  
Vol 4 (3) ◽  
pp. 190-224 ◽  
Author(s):  
Christian Dustmann ◽  
Uta Schönberg
Keyword(s):  
Return To Work ◽  
Maternity Leave ◽  
Work Behavior ◽  
Strong Impact ◽  
Policy Changes ◽  
Long Term Outcomes ◽  
Long Run ◽  
Causal Impact ◽  
The Impact

This paper evaluates the impact of three major expansions in maternity leave coverage in Germany on children's long-run outcomes. To identify the causal impact of the reforms, we use a difference-indifference design that compares outcomes of children born shortly before and shortly after a change in maternity leave legislation in years of policy changes, and in years when no changes have taken place. We find no support for the hypothesis that the expansions in leave coverage improved children's outcomes, despite a strong impact on mothers' return to work behavior after childbirth. (JEL J13, J16, J22, J32)

scholarly journals Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe

2019 ◽  
Vol 11 (4) ◽  
pp. 467 ◽  
Author(s):  
Helga Weber ◽  
Stefan Wunderle
Keyword(s):  
Time Series ◽  
Explicit Knowledge ◽  
Regional Scale ◽  
Error Sources ◽  
Fire Cycle ◽  
Active Fire ◽  
Maximum Delay ◽  
Orbital Drift ◽  
The Impact

Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg − 1 for ▵ B T 34 (p ≤ 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods.

Habitat mapping and change assessment of coastal wetlands by using Sentinel-2 time series and ecological expert knowledge

2020 ◽  
Author(s):  
Maria Adamo ◽  
Valeria Tomaselli ◽  
Francesca Mantino ◽  
Cristina Tarantino ◽  
Palma Blonda
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Coastal Wetlands ◽  
Expert Knowledge ◽  
Land Reclamation ◽  
Habitat Mapping ◽  
The Mediterranean ◽  
The Impact ◽  
Sentinel 2

<p>Coastal wetlands are one of the most threatened ecosystems worldwide. In the Mediterranean Region, wetlands are undergoing rapid changes due to the increasing of human pressures (e.g. land reclamation, water resources exploitation) and climate changes (e.g. coastal erosion), with a resulting habitat degradation, fragmentation, and biodiversity loss.</p><p>Long-term habitat mapping and change detection are essential for the management of coastal wetlands as well as for evaluating the impact of conservation policies.</p><p>Earth observation (EO) data and techniques are a valuable resource for long-term habitat mapping, thanks to the large amount of available data and their high spatial and temporal resolution. In this study, we propose an approach based on the integration of time series of Sentinel-2 images and ecological expert knowledge for land cover (LC) mapping and automatic translation to habitats in coastal wetlands. In particular, the research relies on the exploitation of ecological rules based on combined information related to plant phenology, water seasonality of aquatic species, pattern zonation, and habitat geometric properties.</p><p>The methodology is applied to two Natura2000 sites, “Zone umide della Capitanata” and “Paludi presso il Golfo di Manfredonia”, located in the northeastern part of the Puglia region. These two areas are the most extensive wetlands of the Italian peninsula and the largest components of the Mediterranean wetland system.</p><p>Land Cover classes are labelled according to the FAO-LCCS taxonomy, which offers a framework to integrate EO data with in situ and ancillary data. Output habitat classes are labelled according to EUNIS habitat classification.</p>

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