scholarly journals Pemodelan Dan Peramalan Indeks Harga Konsumen (IHK) Kota Sampit Dengan Seasonal Arima (Sarima)

2020 ◽  
Vol 6 (2) ◽  
pp. 61-72
Author(s):  
Agustina Elisa Dyah Purwandari
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Consumer Price Index ◽  
Real Income ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Goods And Services ◽  
Seasonal Factors ◽  
The Government ◽  
The Right

AbstractSampit is one of 82 cities in Indonesia which calculate inflation. Inflation is an increase of prices on goods and services in a region. Government’s control is very important because inflation relates to the real income, the exchange rate, import exports, and so on. Inflation is based on the Consumer Price Index (CPI). Because of CPI is a monthly data prices, it is highly influenced by seasonal factors. Therefore, CPI data modelling is needed because it helps the government to make appropriate policies. Method that can be used for time series data with seasonal influences is Seasonal Autoregressive Integrated Moving Average (SARIMA). The results of the study show that the right model for Sampit’s CPI is SARIMA with the order p = 1, d = 1, P = 1, D = 1, Q = 1, s = 12. It is the best model that can built and be used for forecasting because with 95 percent of confidence, the model explains 87.23 percent of data. Forecasting in this research use interval analysis and found that January 2020 may be the highest increase of CPI (inflation) in 2020. Keywords: CPI, Inflation, SARIMA

scholarly journals Effectiveness and Forecasting of Interest Rate Reversal BI 7-Day Repo Rate in Indonesia: Lower Bound on Monetary Policy?

2018 ◽  
Vol 9 (1) ◽  
pp. 171-180
Author(s):  
I Gede Sanica ◽  
I Ketut Nurcita ◽  
I Made Mastra ◽  
Desak Made Sukarnasih
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Implementation Of Monetary Policy ◽  
Repo Rate ◽  
The Right ◽  
The Impact

AbstractThis study aims to analyze effectivity and forecast of interest rate BI 7-Day Repo Rate as policy reference in the implementation of monetary policy. The method was used in this study contains Vector Autoregression (VAR) to estimate effectivity of BI 7-Day Repo Rate and Autoregressive Integrated Moving Average (ARIMA) to forecast of BI 7-Day Repo Rate. Period of observation in this study used time series data during 2016.4 until 2017.6. The result of this research shows that the transformation of the BI Rate to BI 7-Day Repo Rate is the right step in the monetary policy operation in the effort to reach deepening of the financial market and strengthen the interbank money market structure so that it will decrease loan interest rate and encourage credit growth. The effectiveness of the use of BI 7 Day-Repo Rate on price stability is indicated by the positive relationship between the benchmark interest rate and inflation compared to the BI Rate. The impact of BI 7-Day Repo Rate on economic growth that tends to be positive. Forecasting the use of BI 7-Day Repo Rate shows good results with declining value levels, so this will encourage deepening the financial markets.

scholarly journals Comparison of Indonesian Imports Forecasting by Limited Period Using SARIMA Method

2019 ◽  
Vol 2 (2) ◽  
pp. 90
Author(s):  
Harits Ar Rosyid ◽  
Mutyara Whening Aniendya ◽  
Heru Wahyu Herwanto ◽  
Peizhi Shi
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Market Price ◽  
Forecast Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Strategic Plans ◽  
The Government ◽  
Forecast Time ◽  
High Degree

The development of Indonesia's imports fluctuate over years. Inability to anticipate such rapid changes can cause economic slump due to inappropriate policy. For instance, recent years imports in rice led to the extermination of rice reserves. The reason is to maintain the market price of rice in Indonesia. To overcome these changes, forecasting the amount of imports should assist the Government in determining the optimum policy. This can be done by utilizing an algorithm to forecast time series data, in this case the amount of imports in the next few months with a high degree of accuracy. This study uses data obtained from the official website of the Indonesian Ministry of Trade. Then, Seasonal Autoregressive Integrated Moving Average (SARIMA) method is applied to forecast the imports. This method is suitable for the interconnected dependent variables, as well as in forecasting seasonal data patterns. The results of the experiment showed that 6-period forecast is the most accurate results compared to forecasting by 16 and 24 periods. The research resulted in the best model, that is ARIMA (0, 1, 3)(0, 1, 1)12 produces forecasting with a MAPE value of 7.210 % or an accuracy rate of 92.790 %. By applying this imports forecast model, the government can have a forward strategic plans such as selectively imports products and carefully decide the amount of the incoming products to Indonesia. Hence, it could maintain or improve the economic condition where local businesses can grow confidently. 

scholarly journals The Nexus between Export and Economic Growth in Tanzania

2019 ◽  
Vol 1 (2) ◽  
pp. p95
Author(s):  
Romanus L. Dimoso (PhD, Economics) ◽  
UTONGA, Dickson (MSc. Economics)
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
Causality Test ◽  
Goods And Services ◽  
Long Run ◽  
Causality Tests ◽  
The Government ◽  
Econometrics Analysis

This study explored the causal relationship between exports and economic growth in Tanzania. It analyzed time series data for the period of 1980 to 2015. Economic growth is measured in terms of growth per cent while exports are measured in percentage change of goods and services sold abroad. Econometrics analysis was employed in the due course. Such procedures as testing for the presence of unit root, co-integration and causality were done. Furthermore, the Johansen co-integration and Granger causality tests were employed to examine the long-run relationship among variables. The results of co-integration indicate the existence of one co-integrating equation. The causality test results exhibited causality which runs from economic growth to exports. The results conclude that, in the long run, there is a relationship between exports and economic growth in Tanzania. This study recommends the Government to make efforts to improve exports and eventually, in the long-run, rejuvenating the economy.

scholarly journals Accounting Implications of Micro-Fiscal Measures and Quality of Real Gross National Goods and Services: Empirical Evidence From Nigeria

2020 ◽  
Vol 11 (6) ◽  
pp. 155
Author(s):  
Akabom I. Asuquo ◽  
Arzizeh Tiesieh Tapang ◽  
Uwem E. Uwah ◽  
Nicholas O. Dan ◽  
Ashishie Peter Uklala
Keyword(s):  
Empirical Evidence ◽  
Inverse Relationship ◽  
Time Series Data ◽  
Series Data ◽  
Government Agencies ◽  
Exploratory Research ◽  
Direct Effects ◽  
Goods And Services ◽  
The Government

The study explored into accounting implications of micro-fiscal measures and quality of real gross national goods and services: empirical evidence from Nigeria for a period of thirty years. The objective was to examine how micro-fiscal measures affect real gross national goods and services using thirty years’ time-series data. The exploratory research methodology was applied and data collected were analysed using multiple regression and other statistical techniques. Findings of the study revealed that significant and direct effects were exerted on gross national goods and services by all the known and identified micro-fiscal measures in the review, except swap and levy ratios which had inverse relationship as revealed by their coefficients obtained from the analysis. Therefore, the government and government agencies have a duty to control macro-fiscal activities in terms of creation of national goods, wealth and services using the identified micro-fiscal mechanisms as the basis for decisions and policies making besides implementation.

scholarly journals Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA

2018 ◽  
Vol 2 (2) ◽  
pp. 49-57
Author(s):  
Dwi Yulianti ◽  
I Made Sumertajaya ◽  
Itasia Dina Sulvianti
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Space Time ◽  
Series Data ◽  
Model Parameters ◽  
Autoregressive Integrated Moving Average ◽  
Sumatra Island ◽  
Rice Price ◽  
Multiple Variables

Generalized space time autoregressive integrated  moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

scholarly journals OPEN DEFECATION IN MALAWI: A BOX-JENKINS ARIMA APPROACH

2020 ◽  
Vol 5 ◽  
pp. 156-165
Author(s):  
Smartson. P. NYONI ◽  
Thabani NYONI
Keyword(s):  
Time Series Data ◽  
Series Data ◽  
Annual Number ◽  
Open Defecation ◽  
Sanitation And Hygiene ◽  
Vision 2030 ◽  
Annual Time Series ◽  
The Government ◽  
The Right ◽  
Annual Time

Using annual time series data on the number of people who practice open defecation in Malawi from 2000 – 2017, the study predicts the annual number of people who will still be practicing open defecation over the period 2018 – 2021. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that the M series under consideration is an I (1) variable. Based on the AIC, the study presents the ARIMA (3, 1, 0) model as the optimal model. The diagnostic tests further show that the presented model is stable and its residuals are stationary in levels. The results of the study indicate that the number of people practicing open defecation in Malawi is likely to decline, over the period 2018 – 2022, from approximately 5.1% to almost 2.8% of the total population. Indeed, by 2030, open defecation can be eliminated in Malawi: hence, the country is in the right track with regards to its vision 2030 (on water, sanitation and hygiene). The study suggested a 3-fold policy recommendation to be put into consideration, especially by the government of Malawi.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Trade Openness And Inflation: Empirical Explanation Of The Nexus In Nigeria

2019 ◽  
Vol 1 (2) ◽  
pp. 35-45
Author(s):  
Musa Abdullahi Sakanko ◽  
David Joseph
Keyword(s):  
Inflation Rate ◽  
Time Series Data ◽  
Trade Openness ◽  
Consumer Price Index ◽  
Negative Relationship ◽  
Series Data ◽  
Secondary Sources ◽  
Long Run ◽  
Short Run ◽  
The Government

Purpose of the study: The study aims is to examine the effect of trade openness on inflation rate in Nigeria. Methodology: Time series data were collected from secondary sources.  EViews10 (statistical software for data analysis) ware employed to analyze the data collected. Findings: The results revealed a cointegrating and one-way Granger causality between inflation rate, and trade openness. In addition, both the short-run and the long-run results demonstrate a significant and negative relationship between inflation rate and trade openness in Nigeria. Application: The study is paramount to the government and policymakers in dealing and taking a decision regarding consumer price index and trade openness in Nigeria. We conclude that the government should work towards full diversification and diversion of the economy from oil export, control, and management of the degree of trade liberalization and the extent to which goods enter the country, and the control of money supplied. Novelty/Originality: The study accorded to debate on the inflation rate, and trade openness in Nigeria looking, at both short-run and long-run effects, before few accessible studies focused on impact, and trade openness was not measured as the value of net export divided by gross domestic product. Finally, the paper contributed to the scanty of the literature.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

Sign in / Sign up

Export Citation Format

Share Document