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Journal articles on the topic 'Vector autoregressive VAR model'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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1

Chapman, David, Mark A. Cane, Naomi Henderson, Dong Eun Lee, and Chen Chen. "A Vector Autoregressive ENSO Prediction Model." Journal of Climate 28, no. 21 (2015): 8511–20. http://dx.doi.org/10.1175/jcli-d-15-0306.1.

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Abstract The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR

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2

Gankhuu, Battulga. "Gordon Growth Model with Vector Autoregressive Process." Mongolian Mathematical Journal, no. 25 (December 27, 2024): 1–9. https://doi.org/10.5564/mmj.v27i25.3505.

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In this study, we introduce a Gordon’s dividend discount model, based on Vector Auto Regressive Process (VAR). We provide two Propositions, which are related to the generic Gordon growth model and the Gordon growth model, which is based onthe VAR process.

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Tita, Rosita, Zaekhan, and Dwi Estuningsih Rachmawati. "Vector Autoregressive (VAR) for Rainfall Prediction." International Journal of Engineering and Management Research 8, no. 2 (2018): 96–102. https://doi.org/10.5281/zenodo.3361980.

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Weather and climate information is useful in a variety of areas including agriculture, tourism, transportation both land, sea and air. For that, up to date weather and climate data and its forecasting are essential. This study aims to create rainfall modeling with Vector Auto Regressive (VAR) using circular data and linear data. The data used comes from the station climatology Darmaga Bogor period 2006-2017. The VAR model (2) of the rainfall variables in the t-month is affected by the t-1 moisture air moisture, the t-2 moisture air and the air temperature at t2. This VAR model (2) is used to f

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Sihombing, Pardomuan, and Bekti Endar Susilowati. "Aplikasi Model Vector Autoregressive (VAR) pada Data Tamu Mancanegara di Hotel Bintang dan Non Bintang di Daerah Istimewa Yogyakarta." Jurnal Statistika dan Aplikasinya 3, no. 2 (2019): 6–15. http://dx.doi.org/10.21009/jsa.03202.

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Model Vector Autoregressive (VAR) merupakan gabungan dari beberapa model Autoregressive (AR), dimana model membentuk sebuah vektor yang antara variabel-variabelnya saling memengaruhi. Model AR(1) menyatakan bahwa pengamatan waktu sekarang dipengaruhi pengamatan satu waktu sebelumnya dan unsur error. Pada analisis ini, model Vector Autoregressive (VAR) digunakan pada data tamu mancanegara per bulan yang menginap di Hotel Bintang dan Non bintang di Daerah Istimewa Yogyakarta per bulan periode Januari 2008 sampai dengan Desember 2015. Pembentukan model VAR melalui beberapa tahap yaitu: uji stasio

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Ningrum, Dewi Kusuma, and Sugiyarto Surono. "Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)." JURNAL EKSAKTA 18, no. 2 (2018): 167–77. http://dx.doi.org/10.20885/eksakta.vol18.iss2.art8.

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Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of

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Lanne, Markku, and Pentti Saikkonen. "NONCAUSAL VECTOR AUTOREGRESSION." Econometric Theory 29, no. 3 (2012): 447–81. http://dx.doi.org/10.1017/s0266466612000448.

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In this paper, we propose a new noncausal vector autoregressive (VAR) model for non-Gaussian time series. The assumption of non-Gaussianity is needed for reasons of identifiability. Assuming that the error distribution belongs to a fairly general class of elliptical distributions, we develop an asymptotic theory of maximum likelihood estimation and statistical inference. We argue that allowing for noncausality is of particular importance in economic applications that currently use only conventional causal VAR models. Indeed, if noncausality is incorrectly ignored, the use of a causal VAR model

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Iskandar, Iskandar. "ANALISIS VECTOR AUTOREGRESSION (VAR) TERHADAP INTERRELATIONSHIP ANTARA FINANCING DEPOSIT RATIO (FDR) DAN RETURN ON ASSET (ROA) PADA BANK SYARIAH DI INDONESIA." Jurnal Ekonomi Syariah, Akuntansi dan Perbankan (JESKaPe) 3, no. 2 (2019): 19–39. http://dx.doi.org/10.52490/jeskape.v3i2.430.

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The Vector Autoregressive Model (VAR) is a very useful analytical tool in understanding the existence of interrelationships between economic variables and in the formation of a structured economy. This study aims to explain the analysis of the Vector Autoregressive (VAR) model and explain the application of the Vector Autoregressive (VAR) model for influence analysis. The FDR ratio in the Sharia Commercial banks tends to be stable. This is illustrated from the coefficient of determination which is almost close to 100%, namely 91.55%. Cointegration test results show there is no long-term balanc

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Sathyanarayana, S., and Sudhindra Gargesa. "Modeling Cryptocurrency (Bitcoin) using Vector Autoregressive (Var) Model." SDMIMD Journal of Management 10, no. 2 (2019): 47–64. http://dx.doi.org/10.18311/sdmimd/2019/23181.

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Shapor, Maria Alexandrovna, and Rafael Rubenovich Gevogyan. "Features of the vector autoregression models application in macroeconomic research." Mezhdunarodnaja jekonomika (The World Economics), no. 8 (August 10, 2021): 634–49. http://dx.doi.org/10.33920/vne-04-2108-05.

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In this paper, we analyzed articles by foreign authors that use various vector autoregression models to calculate the impact of qualitative indicators on the economic processes of countries or a group of countries. In particular, the article analyzed the classical model of vector autoregression (VAR), panel model of autoregressive (PVAR), Bayesian model of autoregressive (BVAR), structural model of autoregressive (SVAR), and the global model of autoregressive (GVAR). Among the works using vector autoregressive models, the main emphasis is on financial indicators. Moreover, articles with non-tr

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Musyoki, Michael, David Alilah, and David Angwenyi. "Updated Vector Autoregressive Model Incorporating new Information Using the Bayesian Approach." SCIENCE MUNDI 4, no. 2 (2024): 178–97. http://dx.doi.org/10.51867/scimundi.mathematics.4.2.17.

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Vector Autoregressive (VAR) models have been applied extensively in modeling time series due to their high precision when used to forecast. In the VAR development, if we have information up to time t, then a VAR(p) model is fitted. However, if new information at time t + 1, is obtained, then a new VAR(p) model has to be fitted which makes one to go through the process again. Therefore, despite their good performance, a need would arise to incorporate new information that could be obtained after the model has been fitted to update the model instead of fitting a new model each and every time a n

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Jung, A.-Hyun, Dong-Hyun Lee, Jin-Young Kim, Chang Ki Kim, Hyun-Goo Kim, and Yung-Seop Lee. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea." Energies 15, no. 21 (2022): 7853. http://dx.doi.org/10.3390/en15217853.

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Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly

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Saikkonen, Pentti, and HELMUT Lütkepohl. "Infinite-Order Cointegrated Vector Autoregressive Processes." Econometric Theory 12, no. 5 (1996): 814–44. http://dx.doi.org/10.1017/s0266466600007179.

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Estimation of cointegrated systems via autoregressive approximation is considered in the framework developed by Saikkonen (1992, Econometric Theory 8, 1-27). The asymptotic properties of the estimated coefficients of the autoregressive error correction model (ECM) and the pure vector autoregressive (VAR) representations are derived under the assumption that the autoregressive order goes to infinity with the sample size. These coefficients are often used for analyzing the relationships between the variables; therefore, they are important for applied work. Tests for linear restrictions on the co

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Lestari, Lisa, Evy Sulistianingsih, and Hendra Perdana. "VECTOR AUTOREGRESSIVE WITH OUTLIER DETECTION ON RAINFALL AND WIND SPEED DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0117–28. http://dx.doi.org/10.30598/barekengvol18iss1pp0117-0128.

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Vector Autoregressive (VAR) is a multivariate time series model that analyzes more than one variable where each variable in the model is endogenous. VAR is one of the models used in forecasting rainfall and wind speed. In observations of rainfall and wind speed, there are usually a series of events whose values are far from other observations or can be said to be outliers. The purpose of this study is to compare the VAR model on rainfall and wind speed data before and after outlier detection. This study uses secondary data, namely monthly data on rainfall and wind speed from 2019 to 2021. From

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PÂRȚACHI, Ion, and Simion MIJA. "MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS." Revista Economica 76, no. 1 (2025): 85–93. https://doi.org/10.56043/reveco-2024-0008.

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Building a multivariate GDP forecasting model based on relevant macroeconomic indicators selected through a proper selection process. This paper assesses whether alternative specifications of the Bayesian model can provide higher forecast accuracy compared to a standard VECM (Vector Error Correction Model). To achieve this, a Bayesian VAR (Vector Autoregressive) model is estimated using the Litterman precedent (1979). Compare the result based on the Bayesian VAR (Vector Autoregressive) model with the DFM (Dynamic Factor Model). The out-of-sample forecast performance of the models is then evalu

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Partachi, Ion, and Simion Mija. "MOLDOVA GDP FORECASTING USING BAYESIAN MULTIVARIATE MODELS." Revista Economica 76, no. 1 (2024): 85–93. https://doi.org/10.56043/reveco-2024-0008.

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Building a multivariate GDP forecasting model based on relevant macroeconomic indicators selected through a proper selection process. This paper assesses whether alternative specifications of the Bayesian model can provide higher forecast accuracy compared to a standard VECM (Vector Error Correction Model). To achieve this, a Bayesian VAR (Vector Autoregressive) model is estimated using the Litterman precedent (1979). Compare the result based on the Bayesian VAR (Vector Autoregressive) model with the DFM (Dynamic Factor Model). The out-of-sample forecast performance of the models is then evalu

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Nabila, Siti Ulfa, Novian Riskiana Dewi, Ana Risqa JL, and Wahyu Hidayat Tullah. "Pemodelan dan Peramalan Data Ekspor Sektor Pertanian Menggunakan Model Vector Autoregressive (VAR)." Journal of Mathematics Education and Science 6, no. 1 (2022): 19–28. http://dx.doi.org/10.32665/james.v6i1.1030.

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Model Vector Autoregressive (VAR) merupakan salah satu pemodelan dalam statistika yang dapat digunakan untuk pemodelan data multivariat time series yang biasa ditemukan dalam bidang keuangan, manajemen, bisnis dan ekonomi. Model VAR menganalisis data time series secara simultan untuk mendapatkan kesimpulan yang tepat dan dapat menjelaskan perilaku hubungan antar variabel endogeneous maupun antar variabel endegeneous dan eksogeneous secara dinamis. Selain itu model VAR juga dapat menjelaskan mengenai hubungan antar variabel selain hanya dipengaruhi oleh faktor dirinya sendiri dari waktu ke wakt

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Miasary, Seftina Diyah. "PENERAPAN VECTOR AUTOREGRESSIVE (VAR) DALAM MEMPREDIKSI RETURN SAHAM DI INDONESIA." Jurnal Edukasi dan Sains Matematika (JES-MAT) 8, no. 2 (2022): 171–80. http://dx.doi.org/10.25134/jes-mat.v8i2.6225.

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The rate of return (return) and risk are inseparable in investing activities. One equilibrium model that describes the relationship between return and risk assumes that the expected return is influenced by more than one macroeconomic factor. Furthermore, the causal relationship between stock returns and macroeconomic factor returns was analyzed using VAR. The application of VAR in this study is to predict stock returns through the stages of checking data stationarity, determining the optimal lag length, testing Granger causality between variables, estimating VAR model parameters and Portmantea

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Ding, Yishan, Dongwei He, Jun Wu, and Xiang Xu. "Crude Oil Spot Price Forecasting Using Ivanov-Based LASSO Vector Autoregression." Complexity 2022 (November 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/5011174.

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This paper proposes a forecasting methodology that investigates a set of different sparse structures for the vector autoregression (VAR) model using the Ivanov-based least absolute shrinkage and selection operator (LASSO) framework. The variant auxiliary problem principle method is used to solve the various Ivanov-based LASSO-VAR variants, which is supported by parallel computing with simple closed-form iteration and linear convergence rate. A test case with ten crude oil spot prices is used to demonstrate the improvement in forecasting skills gained from exploring sparse structures. The propo

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Jiang, Han, Yajie Zou, Shen Zhang, Jinjun Tang, and Yinhai Wang. "Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model." Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/9236156.

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Recently, a number of short-term speed prediction approaches have been developed, in which most algorithms are based on machine learning and statistical theory. This paper examined the multistep ahead prediction performance of eight different models using the 2-minute travel speed data collected from three Remote Traffic Microwave Sensors located on a southbound segment of 4th ring road in Beijing City. Specifically, we consider five machine learning methods: Back Propagation Neural Network (BPNN), nonlinear autoregressive model with exogenous inputs neural network (NARXNN), support vector mac

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Metsileng, Lebotsa Daniel, Ntebogang Dinah Moroke, and Johannes Tshepiso Tsoku. "Modelling the BRICS Exchange Rates Using the Vector Autoregressive (VAR) Model." Journal of Economics and Behavioral Studies 10, no. 5(J) (2018): 220–29. http://dx.doi.org/10.22610/jebs.v10i5(j).2511.

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The paper modelled the BRICS exchange rates using the Vector Autoregressive (VAR) model. Monthly time series data ranging from January 2008 to January 2018 was used. All the analysis was computed using the R programming software. The study aimed to determine a suitable VAR model in modelling the BRICS exchange rates and determine the linear dependency between the financial markets (in particular BRICS exchange rates). Optimal lag length of one (1) was selected using the SIC. The VAR model with lag length one was fitted and the parameters were estimated. The results revealed that there is a uni

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Yasin, Hasbi, Budi Warsito, Rukun Santoso, and Suparti. "Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based." E3S Web of Conferences 73 (2018): 13008. http://dx.doi.org/10.1051/e3sconf/20187313008.

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Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each nu

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Prahutama, Alan, S. Suparti, Dwi Ispriyanti, and Tiani Wahyu Utami. "Modelling Inflation Sectors in Indonesia Using Vector Autoregressive (VAR)." Jurnal ILMU DASAR 20, no. 1 (2019): 47. http://dx.doi.org/10.19184/jid.v20i1.7259.

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Analisis time series dapat dibagi menjadi dua yaitu analisis time series univariat dan analisis time series multivariat. Analisis time series univariat salah satunya menggunakan ARIMA, sedangkan analisis time series multivariat dapat menggunakan VAR. VAR merupakan pemodelan persamaan simultan yang memiliki beberapa variabel endogen secara bersamaan. Asumsi dalam model VAR antara lain terjadi kausalitas antar variabel (kausalitas Granger), residual white noise dan berdistribusi normal multivariat. Pada paper ini, metode VAR diimplementasikan dalam memodelkan sektor-sektor Inflasi di Indonesia.

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Aisyah, Aminy, and Suwanda. "Penerapan Model Vector Autoregressive (VAR) untuk Peramalan Jumlah Kedatangan dan Keberangkatan Penerbangan Domestik di Kota Batam." Bandung Conference Series: Statistics 2, no. 2 (2022): 365–72. http://dx.doi.org/10.29313/bcss.v2i2.4456.

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Abstract. Batam is one of the cities in Indonesia which is located very close to Singapore and Malaysia. The number of tourists or local residents for vacation or just visiting Batam City is quite high. Especially via domestic flights. From the number of departures and arrivals of domestic flights recorded by airport officials in several periods, it can be analyzed simultaneously using times series vector analysis. From the model obtained, it can be used to estimate the value of the data in the future for later use in planning an institution where the data is obtained. The model used in this s

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RANANGGA, TJOK GDE SAHITYAHUTTI, I. WAYAN SUMARJAYA, and I. GUSTI AYU MADE SRINADI. "METODE VECTOR AUTOREGRESSIVE (VAR) DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI." E-Jurnal Matematika 7, no. 2 (2018): 157. http://dx.doi.org/10.24843/mtk.2018.v07.i02.p198.

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The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan) arrival to Bali using vector autoregressive (VAR) method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6)).We used multivariate least square method to estimate the VAR(6)’s parameters.The mean absolute percentage error (MAPE) in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tou

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Savada, A. Gilang Aleyusta, Gigih Forda Nama, Titin Yulianti, and Mardiana Mardiana. "Peramalan Data Ekonomi Menggunakan Model Hybrid Vector Autoregressive-Long Short Term Memory." Jurnal Teknik Informatika dan Sistem Informasi 11, no. 1 (2025): 91–104. https://doi.org/10.28932/jutisi.v11i1.10066.

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Fluktuasi pada harga saham serta nilai tukar Rupiah menimbulkan ketidakpastian bagi investor dalam pengambilan keputusan investasi. Salah satu upaya meminimalisasi risiko investasi adalah dengan melalui peramalan menggunakan metode yang handal. Model peramalan tradisional seperti Vector Autoregressive (VAR) efektif menangkap pola linier, namun kurang mampu mengakomodasi pola yang lebih kompleks. Di sisi lain, model deep learning modern seperti Long Short Term Memory (LSTM) mampu menangani pola yang dinamis (linear dan nonlinear), tetapi memiliki keterbatasan dalam memproses hubungan simultan a

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Danuletiu, Adina Elena, Iulia Cristina Iuga, and Adela Socol. ""Investigating Banking Households' Deposits Using Vector Autoregressive Model Var "." Annales Universitatis Apulensis Series Oeconomica 1, no. 16 (2014): 85–103. http://dx.doi.org/10.29302/oeconomica.2014.16.1.8.

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Li, Yuanyuan, and Dietmar Bauer. "Modeling I(2) Processes Using Vector Autoregressions Where the Lag Length Increases with the Sample Size." Econometrics 8, no. 3 (2020): 38. http://dx.doi.org/10.3390/econometrics8030038.

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In this paper the theory on the estimation of vector autoregressive (VAR) models for I(2) processes is extended to the case of long VAR approximation of more general processes. Hereby the order of the autoregression is allowed to tend to infinity at a certain rate depending on the sample size. We deal with unrestricted OLS estimators (in the model formulated in levels as well as in vector error correction form) as well as with two stage estimation (2SI2) in the vector error correction model (VECM) formulation. Our main results are analogous to the I(1) case: We show that the long VAR approxima

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Saib, Nairobi, Ambya Ambya, Edwin Russel, et al. "Analysis of Data Inflation Energy and Gasoline Price by Vector Autoregressive Model." International Journal of Energy Economics and Policy 12, no. 2 (2022): 120–26. http://dx.doi.org/10.32479/ijeep.12497.

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The study of multivariate time series data analysis has become many topics of research in the fields of economics and business. In the present study, we will analyze data energy inflation and gasoline prices of Indonesia over the years from 2014 to 2020. The purpose of this study is to obtain the best model of the dynamic relationship between inflation and gasoline prices. The dynamic modeling that will be used in this research is modeling using the Vector Autoregressive (VAR) model. From the analysis results, the best model is the VAR model with order 3 (p=3), VAR(3). Based on the best model,

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Margaretha Ratih Dyah Novitasari, I Wayan Sumarjaya, and I Gusti Ayu Made Srinadi. "IMPLEMENTASI METODE VECTOR AUTOREGRESSIVE DALAM PERAMALAN JUMLAH PRODUKSI PADI DI KABUPATEN BADUNG." Jurnal Cahaya Mandalika ISSN 2721-4796 (online) 5, no. 1 (2024): 451–60. http://dx.doi.org/10.36312/jcm.v5i1.2122.

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Forecast is a process to predict something in the future using past data. One of common model used in forecast is time series data that is vector autoregressive (VAR) model. The research purpose is to know the model and amount of rice production in Badung regency. It is used seconder data get from the BPS office Bali and BMKG Denpasar, that are rice production data, harvest area, and rainfall from Januari 2018 till December 2022. Base on lag optimum model VAR, the research result show that the VAR(1) model is suitable being used. Therefore, base on MAPE forecast criteria the VAR model in this

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Morana, Claudio. "PC-VAR Estimation of Vector Autoregressive Models." Open Journal of Statistics 02, no. 03 (2012): 251–59. http://dx.doi.org/10.4236/ojs.2012.23030.

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Naghizadeh, Mostafa, and Mauricio Sacchi. "Multicomponent f-x seismic random noise attenuation via vector autoregressive operators." GEOPHYSICS 77, no. 2 (2012): V91—V99. http://dx.doi.org/10.1190/geo2011-0198.1.

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We propose an extension of the traditional frequency-space ([Formula: see text]) random noise attenuation method to three-component seismic records. For this purpose, we develop a three-component vector autoregressive (VAR) model in the [Formula: see text] domain that is applied to the multicomponent spatial samples of each individual temporal frequency. VAR model parameters are estimated using the least-squares minimization of forward and backward prediction errors. VAR modeling effectively identifies the potential coherencies between various components of a multicomponent signal. We use the

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Lütkepohl, Helmut, and D. S. POSKITT. "Testing for Causation Using Infinite Order Vector Autoregressive Processes." Econometric Theory 12, no. 1 (1996): 61–87. http://dx.doi.org/10.1017/s0266466600006447.

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Tests for Granger-causality have been performed in numerous empirical studies. These tests are usually based on finite order vector autoregressive (VAR) processes, and the assumption is made that the model fitted to the available data corresponds to the true data generating mechanism. In the present study, the more general assumption is made that a finite order VAR model is fitted to a potentially infinite order process. The order is assumed to increase with the sample size. Asymptotic properties of tests for Granger-causality as well as other types of causality concepts are derived. Some limi

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Gupta, Shashi, Himanshu Choudhary, and D. R. Agarwal. "Hedging Efficiency of Indian Commodity Futures." Paradigm 21, no. 1 (2017): 1–20. http://dx.doi.org/10.1177/0971890717700529.

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This article examines the hedge ratio and hedging effectiveness in agricultural (castor seed, guar seed) and non-agricultural (copper, nickel, gold, silver, natural gas and crude oil) commodities traded in National Commodity and Derivative Exchange (NCDEX) and Multi Commodity Exchange (MCX), respectively. Constant and dynamic hedge ratios are estimated by using ordinary least square (OLS), vector autoregression (VAR), vector error correction model (VECM) and vector autoregressive-multivariate generalized autoregressive conditional heteroskedasticity model (VAR-MGARCH). The results of constant

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Widya, Bernadus Divanda, Agus Rusgiyono, and Iut Tri Utami. "PERAMALAN HARGA PASAR TELUR AYAM RAS MENGGUNAKAN VECTOR AUTOREGRESSIVE (VAR) (Studi Kasus: Harga Pasar Telur Ayam Ras di eks Karesidenan Surakarta Tahun 2020-2022)." Jurnal Gaussian 14, no. 1 (2025): 97–106. https://doi.org/10.14710/j.gauss.14.1.97-106.

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The stabilization of the market price of broiler eggs is one of the points that the government should pay attention to. This is to prevent significant price increases or decreases. Government precautions to keep broiler egg prices stable can be done through forecasting. Vector autoregression (VAR) is a time series model that can be used to model and forecast data containing multiple variables at once. VAR models were constructed to estimate relationships between economic variables without paying attention to exogenous issues. The level of predictive accuracy of vector autoregressive (VAR) mode

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Yudistira, Ira, Kuzair, and Faisol. "Vector Autoregressive (VAR) modeling for weather forecasting in Madura." Journal Focus Action of Research Mathematic (Factor M) 7, no. 2 (2024): 134–48. https://doi.org/10.30762/f_m.v7i2.3486.

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Air temperature, air humidity and sunlight are interconnected weather elements. The high and low intensity of solar radiation affects air temperature, while air temperature affects air humidity. These three elements have an important role in global climate change and human activities on earth. In the agricultural sector, air temperature, air humidity and sunlight influence plant growth and development. These three elements also influence the water supply on earth. Information about these three weather elements is very important for Madura, where the majority of the population are farmers and p

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Ting, Chee-Ming, Abd-Krim Seghouane, Muhammad Usman Khalid, and Sh-Hussain Salleh. "Is First-Order Vector Autoregressive Model Optimal for fMRI Data?" Neural Computation 27, no. 9 (2015): 1857–71. http://dx.doi.org/10.1162/neco_a_00765.

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We consider the problem of selecting the optimal orders of vector autoregressive (VAR) models for fMRI data. Many previous studies used model order of one and ignored that it may vary considerably across data sets depending on different data dimensions, subjects, tasks, and experimental designs. In addition, the classical information criteria (IC) used (e.g., the Akaike IC (AIC)) are biased and inappropriate for the high-dimensional fMRI data typically with a small sample size. We examine the mixed results on the optimal VAR orders for fMRI, especially the validity of the order-one hypothesis,

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Kim, Yunsun, and Sahm Kim. "Electricity Load and Internet Traffic Forecasting Using Vector Autoregressive Models." Mathematics 9, no. 18 (2021): 2347. http://dx.doi.org/10.3390/math9182347.

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This study was conducted to investigate the applicability of measuring internet traffic as an input of short-term electricity demand forecasts. We believe our study makes a significant contribution to the literature, especially in short-term load prediction techniques, as we found that Internet traffic can be a useful variable in certain models and can increase prediction accuracy when compared to models in which it is not a variable. In addition, we found that the prediction error could be further reduced by applying a new multivariate model called VARX, which added exogenous variables to the

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Rasheed, Dawuda, and S. K. Appiah. "Vector Autoregressive Model of Maize Production in Northern Region of Ghana." Asian Journal of Agricultural Extension, Economics & Sociology 41, no. 11 (2023): 299–311. http://dx.doi.org/10.9734/ajaees/2023/v41i112287.

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Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized

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Tunang, Yulin, Tohap Manurung, and Nelson Nainggolan. "Penerapan Model Vector Autoregressive (VAR) untuk Memprediksi Harga Cengkeh, Kopra dan Pala di Sulawesi Utara." d'CARTESIAN 8, no. 2 (2019): 100. http://dx.doi.org/10.35799/dc.8.2.2019.23967.

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YULIN TUNANG. Application of Vector Autoregressive (VAR) Model to Predict Prices of Clove, Copra and Nutmeg Commodities in North Sulawesi. Under the guidance of NELSON NAINGGOLAN as main supervisor and TOHAP MANURUNG as a co-supervisor.The purpose of this study is to determine the vector autoregressive (VAR) model of the prices of clove, copra and nutmeg commodities in North Sulawesi. The data used are data on monthly prices of cloves, copra and nutmeg for the period of January 2015 to March 2019. Parameter estimation results for clove prices are estimated parameter values of 0,174; 0,260; 0,1

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Pradhan, Kailash. "The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in India." South East European Journal of Economics and Business 6, no. 1 (2011): 111–23. http://dx.doi.org/10.2478/v10033-011-0010-2.

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The Hedging Effectiveness of Stock Index Futures: Evidence for the S&P CNX Nifty Index Traded in IndiaThis study evaluates optimal hedge ratios and the hedging effectiveness of stock index futures. The optimal hedge ratios are estimated from the ordinary least square (OLS) regression model, the vector autoregression model (VAR), the vector error correction model (VECM) and multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) models such as VAR-GARCH and VEC-GARCH using the S&P CNX Nifty index and its futures index. Hedging effectiveness is measured in terms

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ISMAIL, MOHD TAHIR, and ZAIDI BIN ISA. "MODELING THE INTERACTIONS OF STOCK PRICE AND EXCHANGE RATE IN MALAYSIA." Singapore Economic Review 54, no. 04 (2009): 605–19. http://dx.doi.org/10.1142/s0217590809003471.

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After the East Asian crisis in 1997, the issue of whether stock prices and exchange rates are related or not have received much attention. This is due to realization that during the crisis the countries affected saw turmoil in both their currencies and stock markets. This paper studies the non-linear interactions between stock price and exchange rate in Malaysia using a two regimes multivariate Markov switching vector autoregression (MS-VAR) model with regime shifts in both the mean and the variance. In the study, the Kuala Lumpur Composite Index (KLCI) and the exchange rates of Malaysia ringg

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Zomchak, Larysa, Mariana Komar, and Iryna Karpa. "VECTOR AUTOREGRESSIVE MODEL OF LVIV REGION SUSTAINABLE DEVELOPMENT." Market economy: modern management theory and practice 21, no. 3(52) (2023): 101–13. http://dx.doi.org/10.18524/2413-9998.2022.3(52).275787.

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The concept of sustainable development combines economic, ecological and social approaches to decision-making, taking into account the need to minimize damage to the environment and simultaneously ensure the socio-economic development of the system at the appropriate level. It is obvious that regional sustainable development will always be compatible with global sustainable development, because the sustainability of the system is ensured by the sustainability of its components. The article implements a vector autoregression model of sustainable development of the Lviv region. The model shows t

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Rohaeti, Embay, I. Made Sumertajaya, Aji Hamim Wigena, and Kusman Sadik. "THE PROMINENCE OF VECTOR AUTOREGRESSIVE MODEL IN MULTIVARIATE TIME SERIES FORECASTING MODELS WITH STATIONARY PROBLEMS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 4 (2022): 1313–24. http://dx.doi.org/10.30598/barekengvol16iss4pp1313-1324.

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One of the problems in modelling multivariate time series is stationary. Stationary test results do not always produce all stationary variables; mixed stationary and non-stationary variables are possible. When stationary problems are found in multivariate time series modelling, it is necessary to evaluate the model's performance in various stationary conditions to obtain the best forecasting model. This study aims to get a superior multivariate time series forecasting model based on the goodness of the model in various stationary conditions. In this study, the evaluation of the model's perform

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Sumihi, Deastic, John Kekenusa, and Nelson Nainggolan. "Prediksi Tinggi Gelombang Laut di Perairan Laut Sulawesi Utara dengan Menggunakan Model Vector Autoregressive (VAR)." d'CARTESIAN 6, no. 2 (2017): 73. http://dx.doi.org/10.35799/dc.6.2.2017.17837.

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Telah dilakukan penelitian tentang tinggi gelombang laut di perairan laut Sulawesi Utara yang bertujuan untuk menerapkan model Vector Autoregressive (VAR) dalam memprediksi tinggi gelombang laut di wilayah perairan Bitung, perairan Manado, dan perairan Tahuna. Model VAR merupakan salah satu model time series yang menghendaki pemodelan secara simultan dengan beberapa peubah. Data yang digunakan dalam penelitian ini adalah data rata-rata harian tinggi gelombang laut di wilayah perairan Bitung, wilayah perairan Manado, dan wilayah perairan Tahuna yang diperoleh dari Badan Meteorologi, Klimatologi

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Sukarna, S., Wahidah Sanusi, and Serly Diliyanti Restu Ningsih. "Model Vector Autoregressive Exogenous dan Aplikasinya pada Curah Hujan Kota Makassar." Journal of Mathematics, Computations, and Statistics 2, no. 2 (2020): 108. http://dx.doi.org/10.35580/jmathcos.v2i2.13745.

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Jenis penelitian ini adalah penelitian terapan yang bertujuan untuk meramalkan curah hujan di Kota Makassar dengan menggunakan model VARX. Model VARX dikembangkan dari model VAR dengan menambahkan faktor eksogen yang mempengaruhi curah hujan seperti Sea Surface Temperature (SST) Nino 3.4, Southern Oscillation Index (SOI), dan Dipole Mode Index (DMI). Data curah hujan yang digunakan pada penelitian ini adalah data curah hujan bulanan di Kota Makassar dari tahun 1987-2016 di tiga stasiun yaitu Panaikang, Paotere, dan Biring Romang sebagai faktor endogen. Data ini diperoleh dari Balai Besar Meteo

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Handoyo, Samingun, Ying-Ping Chen, Tiara Mawidha Shelvi, and Heni Kusdarwati. "Modeling Vector Autoregressive and Autoregressive Distributed Lag of the Beef and Chicken Meat Prices during the Covid-19 Pandemic in Indonesia." Journal of Hunan University Natural Sciences 49, no. 3 (2022): 220–31. http://dx.doi.org/10.55463/issn.1674-2974.49.3.25.

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The impact of the COVID-19 pandemic has spread to all aspects of life. Modeling the price of beef and chicken meat is very important for the government to avoid extreme fluctuations of both commodities in the prices so that society's purchasing power can be maintained. This study has several objectives, namely building VAR and ARDL models from multiple time series data (beef and chicken meat prices), conducting variable selection with forwarding subset selection on input lag in the ARDL model, and measuring the performance of the VAR and ARDL models on the both of beef and chicken meat prices

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Israel, Timothy Olaniyi Morounfolu, Nwuju, Kingdom, Da-wariboko Asikiye Yvonne, and Wegbom Anthony Ike. "Application of Bayesian Vector Autoregressive Models in the Analysis of Quasi Money and Money Supply: A Case Study of Nigeria." Asian Journal of Probability and Statistics 25, no. 3 (2023): 108–17. http://dx.doi.org/10.9734/ajpas/2023/v25i3567.

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Aims: The aim of this study is to model the relationship between Nigerian quasi money and money supply using the Bayesian Vector Autoregressive (BVAR) model. Study design: The study collected and analyzed monthly data from the Central Bank of Nigeria (CBN) money and credit statistics over an 8-year period (November 2015 to December 2022). The analysis utilized both Vector Autoregressive (VAR) Model and BVAR model to examine the dynamics between these variables and their implications for monetary policy. Methodology: The study employed the Bayesian Vector Autoregressive (BVAR) model

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Jia, Zhixuan, Wang Li, Yunlong Jiang, and Xingshen Liu. "The Use of Minimization Solvers for Optimizing Time-Varying Autoregressive Models and Their Applications in Finance." Mathematics 13, no. 14 (2025): 2230. https://doi.org/10.3390/math13142230.

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Time series data are fundamental for analyzing temporal dynamics and patterns, enabling researchers and practitioners to model, forecast, and support decision-making across a wide range of domains, such as finance, climate science, environmental studies, and signal processing. In the context of high-dimensional time series, the Vector Autoregressive model (VAR) is widely used, wherein each variable is modeled as a linear combination of lagged values of all variables in the system. However, the traditional VAR framework relies on the assumption of stationarity, which states that the autoregress

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Widiarti, Widiarti, Mustofa Usman, Almira Rizka Putri, and Edwin Russel. "Modeling and Analysis Data Production of Oil, and Oil and Gas in Indonesia by Using Threshold Vector Error Correction Model." Science and Technology Indonesia 9, no. 1 (2024): 189–97. http://dx.doi.org/10.26554/sti.2024.9.1.189-197.

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Data in the fields of finance, business, economics, agriculture, the environment and weather are commonly in the form of time series data. To analyze time series data that involves more than one variable (multivariate), vector autoregressive (VAR) models, vector autoregressive moving average (VARMA) models are generally used. If the variables discussed have cointegration, then the VAR model is modified into a vector error correction model (VECM). The relationship between short-term dynamics and deviation in the VECM model is assumed to be linear. If there is a nonlinear relationship between sh

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Ahelegbey, Daniel Felix. "Inference of Impulse Responses via Bayesian Graphical Structural VAR Models." Econometrics 13, no. 2 (2025): 15. https://doi.org/10.3390/econometrics13020015.

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Impulse response functions (IRFs) are crucial for analyzing the dynamic interactions of macroeconomic variables in vector autoregressive (VAR) models. However, traditional IRF estimation methods often have limitations with assumptions on variable ordering and restrictive identification constraints. This paper applies the Bayesian graphical structural vector autoregressive (BGSVAR) model, which integrates structural learning to capture both temporal and contemporaneous dependencies for more accurate impulse response estimation. The BGSVAR framework provides a more efficient and interpretable me

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