scholarly journals FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE USING VECTOR INTEGRATED MOVING AVERAGE

2020 ◽  
Vol 13 (2) ◽  
pp. 182-193
Author(s):  
Trimono Trimono ◽  
Abdulah Sonhaji ◽  
Utriweni Mukhaiyar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Empirical Study ◽  
Moving Average ◽  
Agriculture Sector ◽  
Central Java ◽  
Several Variables ◽  
The Future ◽  
Vector Time Series ◽  
Central Java Province

Farmer Exchange Rate (FER) is an indicator that can be used to measure the level of farmers welfare. For every agriculture sector, FER is affected by the historical price of harvest from the corresponding sector and historical prices of other agriculture sectors. In Central Java Province, rice & palawija, horticulture, and fisheries are the largest agriculture sectors which is the main livelihood for most of the population. FER forecasting is a crucial thing to determine the level of farmers welfare in the future. One method that can be used to predict the value of a variable that is influenced by the historical value of several variables is Vector Time Series. An empirical study was conducted using FER data from the rice & palawija, horticulture and fisheries sectors for January 2011-June 2017 in Central Java Province. The results obtained show that by using the VIMA(2.1) model, the FER prediction was very accurate, with MAPE values were 1.91% (rice & palawija sector), 2.44% (horticulture sector), and 2.18% (fisheries sector).

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

scholarly journals Justification of Wold’s Theorem and the Unbiasedness of a Stable Vector Autoregressive Time Series Model Forecasts

2017 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Iberedem A. Iwok
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Process ◽  
Time Series Model ◽  
Average Process ◽  
Moving Average Process ◽  
Autoregressive Time Series ◽  
Vector Autoregressive ◽  
Vector Autoregressive Process ◽  
Vector Time Series

In this work, the multivariate analogue to the univariate Wold’s theorem for a purely non-deterministic stable vector time series process was presented and justified using the method of undetermined coefficients. By this method, a finite vector autoregressive process of order  [] was represented as an infinite vector moving average () process which was found to be the same as the Wold’s representation. Thus, obtaining the properties of a  process is equivalent to obtaining the properties of an infinite  process. The proof of the unbiasedness of forecasts followed immediately based on the fact that a stable VAR process can be represented as an infinite VEMA process.

Vector linear time series models

1976 ◽  
Vol 8 (2) ◽  
pp. 339-364 ◽  
Author(s):  
W. Dunsmuir ◽  
E. J. Hannan
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Time ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Multiple Time ◽  
Strong Law ◽  
Large Numbers ◽  
Spectral Approximations ◽  
Vector Time Series

This paper presents proofs of the strong law of large numbers and the central limit theorem for estimators of the parameters in quite general finite-parameter linear models for vector time series. The estimators are derived from a Gaussian likelihood (although Gaussianity is not assumed) and certain spectral approximations to this. An important example of finite-parameter models for multiple time series is the class of autoregressive moving-average (ARMA) models and a general treatment is given for this case. This includes a discussion of the problems associated with identification in such models.

scholarly journals The Effect of Human Capital, Labors, and Capital on Economic Growth in Barlingmascakeb

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Rizqia Mutiara Sani ◽  
Herman Sambodo ◽  
Bambang Bambang
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Human Capital ◽  
Linear Regression ◽  
Life Expectancy ◽  
Secondary Data ◽  
Positive Influence ◽  
Level Of Education ◽  
Central Java ◽  
Central Java Province

The economic growth of Banjarnegara, Purbalingga, Banyumas, Cilacap and Kebumen regencies or known as Barlingmascakeb region is on average lower than the economic growth of Central Java Province. This study aims to analyze the influence of human capital that proxy from level of education and life expectacy, labor, and capital on economic growth in the Barlingmascakeb region. The data used is secondary data, time series starting from 2008-2015. This study uses multiple linear regression. Based on the results of the study it is known that the variable human capital, which is seen from the level of education and life expectancy, labor, capital has a positive influence on economic growth in the Barlingmascakeb region.Keywords: Level of Education, Life Expectancy, Labor, Capital, Economic Growth.

scholarly journals PENGARUH PERTUMBUHAN EKONOMI DAN RATA-RATA LAMA SEKOLAH TERHADAP KEMISKINAN DI PROVINSI JAWA TENGAH

2020 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
Miftaqh Nur Faritz ◽  
Ady Soejoto
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Cross Section ◽  
Average Length ◽  
Random Effect ◽  
Random Effect Model ◽  
Significant Negative Effect ◽  
Central Java ◽  
Negative Effect ◽  
Central Java Province

Latar belakang yang mendasari penelitian ini karena Provinsi Jawa Tengah merupakan provinsi dengan presentase penduduk miskin sebesar 11,19% Tahun 2018 dan menempati posisi dua terbawah dari berberapa provinsi yang ada di Pulau Jawa, Kemiskinan di Jawa Tengah disebabkan oleh rendahnya pertumbuhan ekonomi serta rendahnya pendidikan masyarakat. Tujuan penelitian ini untuk mengetahui pengaruh pertumbuhan ekonomi dan rata-rata lama sekolah terhadap kemiskinan di Provinsi Jawa Tengah. Penelitian ini menggunakan teknik analisis data panel dengan data yang diperoleh dari Badan Pusat Statistik, mengunakan Cross Section 35 kabupaten/kota di Provinsi Jawa Tengah dan Time Series tahun 2009-2018, menggunakan model random effect. Hasil dari penelitian ini menunjukkan bahwa secara parsial pertumbuhan ekonomi berpengaruh signifikan negatif terhadap kemiskinan di provinsi jawa tengah, rata-rata lama sekolah berpengaruh signifikan negatif terhadap kemiskinan di provinsi jawa tengah. Sedangkan secara simultan pertumbuhan ekonomi dan rata-rata lama sekolah berpengaruh signifikan negatif terhadap kemiskinan di provinsi jawa tengah Kata Kunci : Pertumbuhan Ekonomi. Rata-Rata Lama Sekolah dan Kemiskinan AbstractThe background which is the basis of this research is that Central Java Province is a province with a poor population percentage of 11.19% in 2018 and occupies the second lowest position of several provinces in Java Island, Poverty in Central Java is caused by low economic growth and low public education . The purpose of this study was to determine the effect of economic growth and average length of school on poverty in Central Java Province. This research uses panel data analysis techniques with data obtained from the Central Statistics Agency, using Cross Section 35 districts / cities in Central Java Province and Time Series in 2009-2018, using a random effect model. The results of this research show that partially economic growth has a significant negative effect on poverty in Central Java Province, the average length of school has a significant negative effect on poverty in the province of Central Java. While simultaneous economic growth and average length of school have a significant negative effect on poverty in Central Java Province.Keywords: Economic Growth, Mean Years School, Poverty.

scholarly journals WHY VILLAGE FUND NOT YET PROMOTING ENVIRONMENTAL SUSTAINABILITY? AN EMPIRICAL STUDY OF VILLAGE FUND INCENTIVES IN CENTRAL JAVA PROVINCE, INDONESIA

2018 ◽  
Vol 7 (1) ◽  
pp. 171
Author(s):  
Djuwityastuti . ◽  
Wida Astuti
Keyword(s):  
Empirical Study ◽  
Environmental Sustainability ◽  
Empirical Data ◽  
Statistical Data ◽  
Sustainable Use ◽  
Physical Development ◽  
Central Java ◽  
Ministry Of Finance ◽  
Environmental Development ◽  
Central Java Province

<p>One of the purposes of granting village funds under Village Law Number 6 year 2014 is to support the rural development through the sustainable use of natural and environmental resources. Based on the statistical data from The Ministry of Finance Republic of Indonesia, the grant of village funding since 2015 has increased for each year. However, based on empirical data in The Central Java Province is still in the allocation for physical development (infrastructure sector) and far from environmental sustainability programs. Through research funded by Sebelas Maret University Surakarta, this article will describe (1) factors that hinder the sustainable environmental development and (2) how the way out that can be applied to support sustainable environmental development for subsequent years.</p>

scholarly journals Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

2021 ◽  
Vol 21 (1) ◽  
pp. 55-68
Author(s):  
Choiroel Woestho ◽  
Milda Handayani ◽  
Adi Wibowo Noor Fikri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Food Plants ◽  
Series Data ◽  
Food Crops ◽  
Food Crop ◽  
Land Area ◽  
Location Quotient ◽  
Central Java ◽  
Central Java Province

The food crop sector has an important role for regions in Indonesia. Food plants can be a determinant for an area in meeting the needs of the people in that area. In addition, the food crop sector, if developed, can become revenue for the region. This study aims to analyze the leading food plants in 35 districts / cities in Central Java Province. By using the location quotient (LQ) method and the Regional Specialization Index. The data used is time series data from 2014 to 2019 in 35 districts / cities in Central Java Province for food crops based on land area and production. The results obtained for the average LQ value of food crops based on land area, there are only 12 districts / cities which are the basis for superior food crops with Wonogiri Regency at the top. Meanwhile, based on the average LQ value based on production, only 11 districts / cities are the basis for superior food crops with Semarang Regency being the top. For the specialization index based on both land area and production, there is no Regency / City that specializes in Central Java Province.   Keywords: Foodcrop Sector, Location Quotient, Specialization Index, Central Java   Abstrak   Sektor tanaman pangan mempunyai peranan penting bagi daerah di Indonesia. Tanaman pangan dapat menjadi penentu bagi suatu daerah dalam memenuhi kebutuhan masyarakat yang ada di daerah tersebut. Selain itu, sektor tanaman pangan jika dikembangkan dapat menjadi pendapatan bagi daerah. Penelitian ini bertujuan untuk menganalisis tanaman pangan unggulan yang ada di 35 Kabupaten/Kota pada Provinsi Jawa Tengah. Dengan menggunakan metode location quotient (LQ) dan Indeks Spesialisasi Regional. Data yang digunakan adalah data time series selama tahun 2014 hingga tahun 2019 pada 35 Kabupaten/Kota di Provinsi Jawa Tengah untuk tanaman pangan berdasarkan luas lahan dan produksi. Hasil yang diperoleh untuk nilai rata – rata LQ tanaman pangan berdasarkan luas lahan, hanya terdapat 12 Kabupaten/Kota yang menjadi basis bagi tanaman pangan unggulan dengan Kabupaten Wonogiri berada di urutan teratas. Sementara berdasarkan nilai rata – rata LQ berdasarkan produksi, hanya 11 Kabupaten/Kota yang menjadi basis tanaman pangan unggulan dengan Kabupaten Semarang menjadi urutan teratas. Untuk indeks spesialisasi baik berdasarkan luas lahan dan produksi, tidak ada Kabupaten/Kota yang mempunyai spesialisasi terhadap Provinsi Jawa Tengah.   Kata kunci: Tanaman Pangan, Indeks Lokalisasi, Indeks Spesialisasi, Jawa Tengah

scholarly journals Forecasting RMB Exchange Rate Based on a Nonlinear Combination Model of ARFIMA, SVM, and BPNN

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chi Xie ◽  
Zhou Mao ◽  
Gang-Jin Wang
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Financial Time Series ◽  
Moving Average ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Rmb Exchange Rate ◽  
Combination Model ◽  
Arfima Model

There are various models to predict financial time series like the RMB exchange rate. In this paper, considering the complex characteristics of RMB exchange rate, we build a nonlinear combination model of the autoregressive fractionally integrated moving average (ARFIMA) model, the support vector machine (SVM) model, and the back-propagation neural network (BPNN) model to forecast the RMB exchange rate. The basic idea of the nonlinear combination model (NCM) is to make the prediction more effective by combining different models’ advantages, and the weight of the combination model is determined by a nonlinear weighted mechanism. The RMB exchange rate against US dollar (RMB/USD) and the RMB exchange rate against Euro (RMB/EUR) are used as the empirical examples to evaluate the performance of NCM. The results show that the prediction performance of the nonlinear combination model is better than the single models and the linear combination models, and the nonlinear combination model is suitable for the prediction of the special time series, such as the RMB exchange rate.

scholarly journals Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting

2021 ◽  
Vol 11 (12) ◽  
pp. 5658
Author(s):  
Pedro Escudero ◽  
Willian Alcocer ◽  
Jenny Paredes
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Short Term Memory ◽  
Moving Average ◽  
Validation Dataset ◽  
Short Term ◽  
Random Behavior ◽  
Forecasting Performance ◽  
Using Data

Analyzing the future behaviors of currency pairs represents a priority for governments, financial institutions, and investors, who use this type of analysis to understand the economic situation of a country and determine when to sell and buy goods or services from a particular location. Several models are used to forecast this type of time series with reasonable accuracy. However, due to the random behavior of these time series, achieving good forecasting performance represents a significant challenge. In this paper, we compare forecasting models to evaluate their accuracy in the short term using data on the EUR/USD exchange rate. For this purpose, we used three methods: Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) of the Elman type, and Long Short-Term Memory (LSTM). The analyzed period spanned from 2 January 1998, to 31 December 2019, and was divided into training and validation datasets. We performed forecasting calculations to predict windows with six different forecasting horizons. We found that the window of one month with 22 observations better matched the validation dataset in the short term compared to the other windows. Theil’s U coefficients calculated for this window were 0.04743, 0.002625, and 0.001808 for the ARIMA, Elman, and LSTM networks, respectively. LSTM provided the best forecast in the short term, while Elman provided the best forecast in the long term.

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