scholarly journals Pendekatan Univariate Time Series Modelling untuk Prediksi Kuartalan Pertumbuhan Ekonomi Indonesia Pasca Vaksinasi COVID-19

2022 ◽  
Vol 4 (1) ◽  
pp. 86-103
Author(s):  
Asrirawan Asrirawan ◽  
Sri Utami Permata ◽  
Muhammad Ilham Fauzan
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Models ◽  
Arima Model ◽  
Training Data ◽  
Series Data ◽  
Growth Data ◽  
Dynamic Linear Models ◽  
Univariate Time Series ◽  
Time Series Modelling

The development of COVID-19 has had a significant negative impact on Indonesia’s economic growth based on the indicator of the value of the quarterly year of year data in 2020 and 2021. Economic growth is still experiencing a recession per first quarter with a percentage of - 2.19 percent at the beginning of 2021. The government has to take vaccination measures for the community gradually with the aim of reducing the number of sufferers of these cases. The purpose of this study is to predict economic growth quarterly after vaccination using 3 (three) univariate time series models, namely ARIMA, Holt-Winters and Dynamic Linear models for policymaking. Holt-Winters and Dynamic Linear models make it possible to handle time-series data containing trends and seasonality. The data is divided into training data and test data obtained from the ministry of finance and the Indonesian Central Statistics Agency (BPS). The goodness of the model uses MSE, MAE and U-Theil criteria. Based on the results of the analysis using the R library, the results show that the best modelling for economic growth data is the ARIMA model with the lowest MSE, MAE and U-Theil values with the difference between the models being 0.000242. The ARIMA model looks better than other models because the economic growth data only contains trends and assumes a seasonal element in the data. In addition, the Holt-Winters and Dynamic Linear models produce a forecast for Indonesia’s economic growth to still experience a recession (negative growth) in the next four quarterly data, while the ARIMA model produces a positive growth forecast in the fourth quarter.

scholarly journals Market model analysis and forecasting behavior of watermelon production in Bangladesh

2014 ◽  
Vol 26 (1-2) ◽  
pp. 47-56
Author(s):  
Murshida Khanam ◽  
Umme Hafsa
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Linear Models ◽  
Time Series Data ◽  
Arima Model ◽  
Model Analysis ◽  
Market Model ◽  
Series Data ◽  
Log Linear

An attempt has been made to study various models regarding watermelon production in Bangladesh and to identify the best model that may be used for forecasting purposes. Here, supply, log linear, ARIMA, MARMA models have been used to do a statistical analysis and forecasting behavior of production of watermelon in Bangladesh by using time series data covering whole Bangladesh. It has been found that, between the supply and log linear models; log linear is the best model. Comparing ARIMA and MARMA models it has been concluded that ARIMA model is the best for forecasting purposes. DOI: http://dx.doi.org/10.3329/bjsr.v26i1-2.20230 Bangladesh J. Sci. Res. 26(1-2): 47-56, December-2013

scholarly journals The Influence Of Small Micro Industries On Economic Growth

JEJAK ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 318-326
Author(s):  
Rohadin Rohadin ◽  
Yanah Yanah
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Financial Institutions ◽  
Time Series Data ◽  
Series Data ◽  
Growth Data ◽  
Capital Access ◽  
The Government ◽  
Analyze Data

The purpose of this study to determine whether SMEs have a role to economic growth and how big the role of SMEs to economic growth in Indonesia. Types of data used are time series data i.e SMEs data and Economic growth data from year 2003 until 2018 in Indonesia.Tool of analyze data used in this research is multiple linear regression. The result of analysis shows that the influence between of SMEs on economic growth in Indonesia is only 12,5%, it means that Small Micro Entreprises do not have a significant influence on economic growth in Indonesia, government to accelerate the development of SMEs in Indonesia in order to contribute to economic growth as in the economic crisis that occurred in 1998 SMEs are able to survive when many large companies are bankrupt. This may be caused by SMEs owners and workers in SMEs do not pay taxes to the government so that not much contribute to the economic growth of the Indonesia. In order for SMEs to contribute to economic growth, must export their products to other countries and support from the government is needed to facilitate SMEs in obtaining capital access from financial institutions.

scholarly journals ARIMAX Model for Short-Term Electrical Load Forecasting

2019 ◽  
Vol 8 (4) ◽  
pp. 2786-2790
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Load Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Exogenous Variables ◽  
Time Series Modelling ◽  
Electrical Load Forecasting

The scope for ARIMAX approach to forecast short term load has gained a lot of significant importance.In this paper, ARIMAXmodel which is an extension of ARIMA model with exogenous variables is used for STLF on a time series data of Karnataka State Demand pattern. The forecasting accuracy of ARIMA model is enhanced by taking into consideration hour of the day and day of the week as exogenous variables for ARIMAX model. Forecasting performance is thus improved by considering these significant load dependent factors. The forecasted results indicate that the proposed model is more accurate according to mean absolute percentage error (MAPE) obtained during the testing period of the model. As the historical load data are available on the databases of the utility, researches in the areas of time series modelling are ongoing for electrical load forecasting. In the proposed paper real time demand data available on Karnataka Power Transmission Corporation Ltd. (KPTCL) website is taken to develop and test the proposedload forecasting model.The power utility system operational costs and its securitydepend on the load forecasting for next few hours. Regional load forecasting helps in the accurate management performance of generation of power plant. Today’s deregulated markets have great demand for prediction of electrical loads, required for generating companies. There has been tremendous growth in electric power demand and hence it is very much essentialfor the utility sectors to have theirdemand information in advance.

scholarly journals Bernoulli Time Series Modelling with Application to Accommodation Tourism Demand

2021 ◽  
Vol 5 (1) ◽  
pp. 17
Author(s):  
Miguel Ángel Ruiz Reina
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prognostic Models ◽  
Series Data ◽  
Economic Agents ◽  
Time Series Modelling ◽  
Simulation And Training ◽  
Uncertainty Method ◽  
And Training ◽  
Hotel Accommodation

In this research, a new uncertainty method has been developed and applied to forecasting the hotel accommodation market. The simulation and training of Time Series data are from January 2001 to December 2018 in the Spanish case. The Log-log BeTSUF method estimated by GMM-HAC-Newey-West is considered as a contribution for measuring uncertainty vs. other prognostic models in the literature. The results of our model present better indicators of the RMSE and Ratio Theil’s for the predictive evaluation period of twelve months. Furthermore, the straightforward interpretation of the model and the high descriptive capacity of the model allow economic agents to make efficient decisions.

scholarly journals A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3299
Author(s):  
Ashish Shrestha ◽  
Bishal Ghimire ◽  
Francisco Gonzalez-Longatt
Keyword(s):  
Time Series ◽  
Kinetic Energy ◽  
Power System ◽  
Bayesian Model ◽  
Arima Model ◽  
Power Converter ◽  
Series Data ◽  
Percentage Error ◽  
Optimal Size ◽  
Short Term

Withthe massive penetration of electronic power converter (EPC)-based technologies, numerous issues are being noticed in the modern power system that may directly affect system dynamics and operational security. The estimation of system performance parameters is especially important for transmission system operators (TSOs) in order to operate a power system securely. This paper presents a Bayesian model to forecast short-term kinetic energy time series data for a power system, which can thus help TSOs to operate a respective power system securely. A Markov chain Monte Carlo (MCMC) method used as a No-U-Turn sampler and Stan’s limited-memory Broyden–Fletcher–Goldfarb–Shanno (LM-BFGS) algorithm is used as the optimization method here. The concept of decomposable time series modeling is adopted to analyze the seasonal characteristics of datasets, and numerous performance measurement matrices are used for model validation. Besides, an autoregressive integrated moving average (ARIMA) model is used to compare the results of the presented model. At last, the optimal size of the training dataset is identified, which is required to forecast the 30-min values of the kinetic energy with a low error. In this study, one-year univariate data (1-min resolution) for the integrated Nordic power system (INPS) are used to forecast the kinetic energy for sequences of 30 min (i.e., short-term sequences). Performance evaluation metrics such as the root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE) of the proposed model are calculated here to be 4.67, 3.865, 0.048, and 8.15, respectively. In addition, the performance matrices can be improved by up to 3.28, 2.67, 0.034, and 5.62, respectively, by increasing MCMC sampling. Similarly, 180.5 h of historic data is sufficient to forecast short-term results for the case study here with an accuracy of 1.54504 for the RMSE.

CAUSAL RELATIONSHIP OF ENERGY CONSUMPTION AND ECONOMIC GROWTH – A CASE STUDY OF PAKISTAN

2021 ◽  
Vol 9 (1) ◽  
pp. 139-164
Author(s):  
Saddam Hussain ◽  
Chunjiao Yu
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Energy Consumption ◽  
Causal Relationship ◽  
Inflation Rate ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Series Data ◽  
Relationship Of

This paper explores the causal relationship between energy consumption and economic growth in Pakistan, applying techniques of co-integration and Hsiao’s version of Granger causality, using time series data over the period 1965-2019. Time series data of macroeconomic determi-nants – i.e. energy growth, Foreign Direct Investment (FDI) growth and population growth shows a positive correlation with economic growth while there is no correlation founded be-tween economic growth and inflation rate or Consumer Price Index (CPI). The general conclu-sion of empirical results is that economic growth causes energy consumption.

scholarly journals ANALISIS KAUSALITAS KEBIJAKAN FISKAL EKSPANSIF, KEBIJAKAN MONETER EKSPANSIF DAN PERTUMBUHAN EKONOMI DI INDONESIA

2020 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Fadhliah Yuniwinsah ◽  
Ali Anis
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Monetary Policy ◽  
Fiscal Policy ◽  
Time Series Data ◽  
Vector Autoregression ◽  
Series Data ◽  
Vector Autoregression Model ◽  
Autoregression Model

This study examined the causality between expansionary fiscal policy, expansionary monetary policy and economic growth in Indonesia’s using a time series data with vector autoregression model (VAR) in the period of 1969-2018. The results of this study showed that are there is no causality between expansionary fiscal policy and expansionary monetary policy but there one-way relationship between them, it is the expansionary monetary policy gives influence to expansionary fiscal policy. There is no causality between expansionary fiscal policy and economic growth but there one-way relationship between them, It is economic growth gives influence to expansionary fiscal policy. And there is no causality between expansionary monetary policy and economic growth but there one-way relationship between them, it is economic growth gives influence to expansionary monetary policy.

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