U.S. DIESEL FUEL PRICE RESPONSES TO THE GLOBAL CRUDE OIL SUPPLY AND DEMAND

2018 ◽  
Vol 13 (04) ◽  
pp. 1850018 ◽  
Author(s):  
BAHRAM ADRANGI ◽  
ARJUN CHATRATH ◽  
JOSEPH MACRI ◽  
KAMBIZ RAFFIEE
Keyword(s):  
Crude Oil ◽  
Supply And Demand ◽  
Fuel Price ◽  
Demand And Supply ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Vector Error ◽  
Global Demand ◽  
Market Fundamentals ◽  
The U.S

The objective of this study is to examine the monthly movements of U.S. diesel price for the period 1974–2017. We argue that the diesel price may be responsive to crude oil market fundamentals. The model employed includes the global demand and supply for crude oil, in addition to the inventory of crude oil and the level of industrial production for the U.S. The Structural Vector Autoregressive formulation and the Vector Error Correction model suggest that global demand shocks to crude oil, including the inventory of crude oil in the U.S. are primarily responsible for diesel price movements in the U.S., accounting for up to 30–70% of its variation.

Measuring Dynamic Sales Impacts of LBA Using Wireless Communication Technology

2013 ◽  
Vol 662 ◽  
pp. 896-901
Author(s):  
Zong Jin Liu ◽  
Yang Yang ◽  
Zheng Fang ◽  
Yan Yan Xu
Keyword(s):  
Wireless Communication ◽  
Error Correction ◽  
Communication Technology ◽  
Dynamic Error ◽  
Rapid Development ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Vector Error ◽  
Equilibrium Relationship

Because of rapid development of wireless communication technology, there is an increasing adoption of mobile advertising, such as location based advertising (LBA). To what extent can LBA improve advertising effectiveness is an important topic in the field of wireless communication technology research. Most researches quantify long term impacts of advertisings by VAR (Vector Autoregressive) model. However, compared to VAR model, VECM (Vector Error Correction Model) is a better method in that it allows one to estimate both a long-term equilibrium relationship and a short-term dynamic error correction process. In this study, we employ VECM to explore LBA’s (Location Based Advertising) and PUA’s (Pop-up Advertising) sales impact in both short and long terms. The developed VECM reveals that LBA’s sales impact is about more than2 times as big as PUA’s in short dynamic term and nearly 6 times bigger than PUA’s in long equilibrium term. These findings add to advertising and VECM literatures. These results can give managers more confident to apply wireless communication technology to advertising.

scholarly journals The spatial integration of potato wholesale markets of Uttarakhand in India

2017 ◽  
Vol 14 (2) ◽  
pp. 20-30 ◽  
Author(s):  
A Kumar ◽  
R Mishra
Keyword(s):  
Wholesale Price ◽  
Likelihood Method ◽  
Spatial Integration ◽  
Market Information ◽  
Demand And Supply ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
Short Run ◽  
Price Demand

This paper analyzes the spatial integration of potato markets in Uttarakhand using monthly wholesale price for ten years. The maximum likelihood method of cointegration developed by Johansen (1988) was used in the study. The dynamics of short-run price responses were examined using vector error correction model (VECM). The results indicated that five potato markets reacted on the long-run cointegrating equations while the speed of price adjustment in the short-run was almost absent. Moreover, it was found that the longer the distance between the markets, the weaker the integration was. To increase the efficiency of potato markets in Uttarakhand, there is need to focus on building an improved market information system. This system should be able to disseminate timely market information about price, demand and supply of produce to enable producers, traders and consumers to make proper production and marketing decisions.SAARC J. Agri., 14(2): 20-30 (2016)

scholarly journals Seasonal Soybean Price Transmission between the U.S. and Brazil Using the Seasonal Regime-Dependent Vector Error Correction Model

2019 ◽  
Vol 11 (19) ◽  
pp. 5315
Author(s):  
Byung Min Soon ◽  
Jarrett Whistance
Keyword(s):  
Price Transmission ◽  
Seasonal Effects ◽  
Vector Error Correction Model ◽  
Correction Model ◽  
Vector Error Correction ◽  
Vector Error ◽  
The World ◽  
Soybean Price ◽  
Dependent Vector ◽  
The U.S

Soybean production and trade in the U.S. and Brazil are seasonal. Our research question is whether the seasonal tendencies cause the price relationship between U.S. and Brazilian soybean prices. Therefore, the objective is to test for seasonality in the price transmission between the U.S. and Brazil soybean prices using the seasonal regime-dependent vector error correction model (VECM). Our results show that the speed of the adjustment for the U.S. soybean price in the first half of the year is greater than the speed of the adjustment for the Brazilian soybean price. However, the pattern of their responses becomes the reverse in the second half of the year. The component share calculated by the result of the VECM with seasonal effects indicates that the U.S. dominates the world soybean market during the second half of the year while Brazil is dominant in the soybean market in the first half of the year. These results give us an important finding that we could not find using the VECM without seasonal effects. Finally, our results imply that the seasonal pattern of production in the U.S. and Brazil could cause the sustainability of the supply chain in the world soybean market.

scholarly journals KOINTEGRASI BURSA EFEK INDONESIA DENGAN BURSA EFEK AMERIKA SERIKAT, JEPANG, HONGKONG, DAN MALAYSIA

2021 ◽  
Vol 5 (2) ◽  
pp. 335
Author(s):  
Ignatius Roni Setyawan ◽  
Rorlen Rorlen ◽  
Margarita Ekadjaja
Keyword(s):  
United States ◽  
Composite Index ◽  
Stock Exchanges ◽  
Vector Error Correction Model ◽  
Vector Autoregressive Model ◽  
Slow Response ◽  
Correction Model ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Vector Error

Penelitian ini bertujuan untuk menganalisis kointegrasi bursa efek di negara Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia dari tahun 2008-2020 dengan menggunakan model Vector Autoregressive Model. Penelitian ini dilakukan pada rentang waktu selama 156 bulan, di mana data yang diamati pasca krisis global di dunia (2008-2014) dan saat kemajuan ekonomi Cina yang berdampak pada perang dagang dengan USA (2014-2020).  Berdasarkan hasil olah data dengan menggunakan aplikasi eviews 9.0 ditemukan adanya kointegrasi antara bursa efek di negara Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia yang diproksikan indeks DJIA, Hang Seng, Nikkei, KLCI, dan IHSG pada tahun 2008-2020. Hasil uji Vector Error Correction Model menunjukkan tidak adanya kausalitas jangka pendek antara pergerakan indeks Dow Jones, Nikkei, Hang Seng, KLCI, dan IHSG. Hasil uji impulse response menggambarkan impact dari perubahan pada indeks Dow Jones, Hang Seng, Nikkei, dan KLCI terhadap IHSG bersifat jangka panjang untuk kembali ke posisi setara dengan perlahan (slow response).Indeks DJIA yang menguat dipercaya dapat memberikan pengaruh positif bagi saham di Indonesia. Sehingga naik atau turunnya indeks DJIA akan diikuti pula naik atau turunnya IHSG. Implikasi dari penelitian ini adalah terkointegrasinya indeks bursa efek negara di Amerika Serikat, Jepang, Hongkong, Malaysia, dan Indonesia memberikan prediksi bagi investor terhadap fluktuasi indeks saham yang akan terjadi. This research is an empirical study regarding the cointegration of stock exchanges in US, Japan, Hongkong, Malaysia, and Indonesiafrom 2008-2020 using the Vector Autoregressive Model.  This research was conducted over a period of 156 months, where data was observed after the global crisis in the world (2008-2014) and when Hongkong's economic progress had an impact on the trade war with the USA (2014-2020). Based on the results of data processing using the eviews 9.0 application, it was found that there was a cointegration between stock exchanges in the United States, Japan, Hongkong, Malaysia, and Indonesia, which were proxied by the DJIA, Hang Seng, Nikkei, KLCI, and IHSG indexes in 2008-2020. The results of the Vector Error Correction Model test show that there is no short-term causality between the movements of the Dow Jones, Nikkei, Hang Seng, KLCI, and Indonesia Composite index. The impulse response test results illustrate the long-term impact of changes in the Dow Jones, Hang Seng, Nikkei, and KLCI indices on the Indonesia Composite Index to return to an equivalent position slowly (slow response). The stronger DJIA index is believed to have a positive impact on stocks in Indonesia. So that the increase or decrease in the DJIA index will also be followed by an increase or decrease in the Indonesia Composite index. The implication of this research is the cointegration of stock exchange indexes in United States, Japan, Hongkong, Malaysia, and Indonesia can help investors to predict the fluctuation indexes.

scholarly journals An Analysis of Factors Influencing Rice Export in Egypt Based on Vector Autoregressive Model

2019 ◽  
pp. 876-887
Author(s):  
Hanan Mahmoud Sayed Agbo
Keyword(s):  
Autoregressive Model ◽  
Export Price ◽  
Vector Autoregressive Model ◽  
Correction Model ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Export Decisions ◽  
Vector Error ◽  
Factors Influencing ◽  
The Empirical Analysis

Vector Autoregressive Model (VAR) lead to the integration of production and export decisions of rice. The main objective of the study is to determine the main factors influencing Egypt’s rice exports. This model can also be used to study the prospects of Egyptian rice exports. The results of variance decomposition confirm that the most important variables influence the value of Egyptian rice exports is Egyptian export price, and the empirical analysis of Vector Error Correction Model relieves the possibility of improving the competitiveness of Egyptian exports of rice in global markets in the forecast period (2015:2025).

scholarly journals Contemporary Literature Review of the Russian Rouble Determinants

2021 ◽  
pp. 26-31
Author(s):  
Morad Bali ◽  
Keyword(s):  
Price Index ◽  
Contemporary Literature ◽  
Consumer Price Index ◽  
Ordinary Least Squares ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Vector Error ◽  
Vector Error Correction Models ◽  
Gas Price ◽  
Structural Vector

This short literature review’s goal is to examine available papers regarding the study of Russian Rouble determinants. For purpose of analysis, 35 articles were studied among which 22 were selected, for a total of 414 pages shelled. This work analyzes most recent empirical articles, in order to identify factors responsible for the Russian currency fluctuations. Different models will be compared to learn if some are more effective than others, from basic Linear regression to Structural vector autoregressive, through Ordinary least squares or Vector error correction models. Moreover, a very special and particular attention will be paid to variables used. Which combinations of variables are used to study factors influencing the Russian currency? While it seems vital to include oil prices, interest rate, and consumer price index, is it important to have them all together in the same model? Are results among papers similar? In addition, would it be necessary to add variables such as GDP, gold price, gas price, M2 aggregate or sanctions? However, this paper will compare data from each model and try to find out if there is one best way to study the Russian currency determinants.

scholarly journals Test FOR Dynamic Relationship between Financial Development and Economic Growth in Malaysia: A Vector Error Correction Modeling Approach

2007 ◽  
Vol 9 (1) ◽  
pp. 61 ◽  
Author(s):  
Rosilawati Amiruddin ◽  
Abu Hassan Shaari Mohd Nor ◽  
Ismadi Ismail
Keyword(s):  
Economic Growth ◽  
Error Correction ◽  
Financial Development ◽  
Impulse Response Function ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Dynamic Relationship ◽  
Vector Error ◽  
Error Correction Modeling

This paper purports to study the effectiveness of financial development to Malaysian economic growth utilizing quarterly data. In view of the priority given to dynamic relationship in conducting this study, Vector Autoregressive (VAR) method which encompasses Johansen-Juselius’ Multivariate cointegration, Vector Error Correction Model (VECM), Impulse Response Function (IRF), and Variance Decomposition (VDC) are used as empirical evidence. The result reveals a short-term and long-term dynamic relationship between financial development and economic growth. The importance of financial sector in influencing the economic activity is proven as a clear policy implication.

Optimal Hedge Ratio for Brent Oil Market; Baysian Approach

2014 ◽  
Vol 37 ◽  
pp. 82-87
Author(s):  
Mohsen Mehrara ◽  
Monire Hamldar
Keyword(s):  
Error Correction ◽  
Oil Futures ◽  
Vector Autoregressive ◽  
Vector Error Correction ◽  
Oil Market ◽  
Vector Error ◽  
Hedging Ratio ◽  
Daily Data ◽  
Crude Oil Futures ◽  
Optimal Hedge

This paper examines the optimal hedging ratio (OHR) for the Brent Crude Oil Futures using daily data over the period 1990/17/8-2014/11/3. To gain OHR, it is employed a Vector Autoregressive (VAR) and Vector Error Correction (VEC) and Baysian Vector Autoregressive (BVAR) models. At last, the efficiency of these calculated OHR are compared through Edrington's index.

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