Relevant bibliographies by topics / Vector prices

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Vector prices'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Vector prices"

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Rifin, A., and D. Nauly. "Vector error correction model relationship between three vegetable oil products." IOP Conference Series: Earth and Environmental Science 892, no. 1 (2021): 012062. http://dx.doi.org/10.1088/1755-1315/892/1/012062.

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Abstract International price of palm oil fluctuated frequently. It is predicted that the international price of palm oil is affected by the other vegetable oil prices. Soybean oil, rapeseed oil and palm oil are the three most important vegetable oil in the word. These commodities compete but on the other hand the world prices are moving in the same direction. This paper analyzes the relationship of these three prices in the short-run and long-run. The method utilizes in the analysis is the vector error correction model (VECM) followed by Impulse Response and Variance Decomposition. The data us
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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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CHEN, Jieh-Haur, Chuan Fan ONG, Linzi ZHENG, and Shu-Chien HSU. "FORECASTING SPATIAL DYNAMICS OF THE HOUSING MARKET USING SUPPORT VECTOR MACHINE." International Journal of Strategic Property Management 21, no. 3 (2017): 273–83. http://dx.doi.org/10.3846/1648715x.2016.1259190.

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This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the fi
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Prasada, I. made Yoga, Moh Wahyudi Priyanto, and Yahya Shafiyuddin Hilmi. "KETAHANAN PANGAN PENDUDUK DI PULAU JAWA: PENDEKATAN VECTOR ERROR CORRECTION MODEL." Agrisocionomics: Jurnal Sosial Ekonomi Pertanian 4, no. 1 (2020): 85–95. http://dx.doi.org/10.14710/agrisocionomics.v4i1.5560.

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Food security over the past few decades has been a hot topic discussed in Indonesia. Food security can indirectly reflect the level of welfare of a household in a region. Various factors can influence the level of food security, both in the short term and in the long term. Therefore, this research was conducted with the aim to find out the factors that influence the food security of the population in the short term and in the long term. The data used in this study are secondary data sourced from the Central Bureau of Statistics (BPS) in 2008-2017, namely data on food and non-food expenditure,
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Azebre, Abu Ibrahim. "A Vector Error Correction Analysis of International Price Dynamics in Ghana’s Cocoa, Gold, and Crude Oil Markets." International Journal of Research and Scientific Innovation XII, no. V (2025): 834–50. https://doi.org/10.51244/ijrsi.2025.12050081.

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The Vector Error Correction Model (VECM) on the dynamic interrelationships between the international prices of cocoa, gold, and crude oil in the context of Ghana were conducted. Summary statistics indicate that gold is observed to have the greatest stability in prices whereas crude oil has been most volatile. The unit root tests confirmed that in their original form all three commodities are non-stationary but become stationary after first differencing hence implying similar integration properties. The Johansen co-integration test identified two significant long-run relationships whereby crude
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Usman, Mustofa, M. Komarudin, Nurhanurawati Nurhanurawati, Edwin Russel, Wamiliana Wamiliana, and Faiz A. M. Elfaki. "Analysis Forecasting of Gasoline Prices in Some ASEAN Countries by Using State Space Representation on Vector Autoregressive Model." International Journal of Energy Economics and Policy 13, no. 6 (2023): 194–202. http://dx.doi.org/10.32479/ijeep.14893.

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Research on the price of gasoline has become a topic of research that has been carried out by many researchers. The topic is interesting because the price of gasoline has a relationship with many aspects of people's lives. This study aims to examine the relationship pattern of gasoline prices in several ASEAN countries: Indonesia, Malaysia, and Vietnam, and to make gasoline price forecasting in these three countries for the next 12 months. This study uses a multivariate time series approach; first, the best vector autoregressive (VAR(p)) model will be built based on Akaike's Information Criter
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Ali, Mostafa, Gang Sun, and Mohammed Ali Arshad Chowdhury. "Dynamic Interaction Between Macroeconomic Fundamentals and Stock Prices in Bangladesh." Indonesian Journal of Management and Business Economics 1, no. 1 (2018): 66. http://dx.doi.org/10.32455/ijmbe.v1i1.53.

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This study attempts to investigate whether dynamics in fundamental macroeconomic factors significantly influence the stock prices of Bangladesh by applying cointegration test, Granger causality test based on the Vector Error Correction Model (VECM), Variance Decomposition and Impulse Response Analysis. Johansen and Juselius cointegration test detect six cointegrating vectors and a short-run and long-run relationship is investigated by normalizing the first cointegrating vector corresponding to the largest Eigen-value. We find a long-run positive relationship between stock price and IP, CPI, EX
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Roman, Monika, Aleksandra Górecka, and Joanna Domagała. "The Linkages between Crude Oil and Food Prices." Energies 13, no. 24 (2020): 6545. http://dx.doi.org/10.3390/en13246545.

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This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel–food price linkage models with consideration to the time series literature. This study adopts several methods, namely the Augmented Dickey–Fuller test, Granger causality test, the cointegration test, the vector autoregression model, and the vector error correction model, for studying the price transmission among the crude oil and five selected food groups. The
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Pai, Ping-Feng, and Wen-Chang Wang. "Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices." Applied Sciences 10, no. 17 (2020): 5832. http://dx.doi.org/10.3390/app10175832.

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Real estate price prediction is crucial for the establishment of real estate policies and can help real estate owners and agents make informative decisions. The aim of this study is to employ actual transaction data and machine learning models to predict prices of real estate. The actual transaction data contain attributes and transaction prices of real estate that respectively serve as independent variables and dependent variables for machine learning models. The study employed four machine learning models-namely, least squares support vector regression (LSSVR), classification and regression
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Algahtani, Goblan J. "The Effect of Oil Price Shocks on Economic Activity in Saudi Arabia: Econometric Approach." International Journal of Business and Management 11, no. 8 (2016): 124. http://dx.doi.org/10.5539/ijbm.v11n8p124.

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<p>This paper is attempt to investigate the effect of oil price shocks on the Saudi's economic activity using annual data (1970-2015) to cover all of oil price shocks; particularly the recent decline in oil prices amid 2014. The vector autoregressive (VAR) and vector error correction model (VECM) were utilized to investigate the long-run and the short-run relationships between variables. The findings suggest a positive and significant relationship between oil prices and the Saudi's GDP in the long run. </p><p> </p>
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Dissertations / Theses on the topic "Vector prices"

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Sjödin, Wågberg Anton. "Prices on electricity and the prices on stocks : -A Vector autoregressive approach." Thesis, Umeå universitet, Nationalekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-153448.

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This study will investigate if a relationship exists between the price of electricity and the Swedish stock market. This study will also try to investigate what consequences an increase in the price of electricity will have on the return of the Swedish stock market. Economic theory and earlier literature will then be used to try to explain the results obtained in this study. The results from the tests performed in this study imply that a one-way Granger-causality exists between the prices on electricity and the price on the OMX 30. The impulse response functions performed shows that a positive
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Bethapudi, Daniel Naveen. "Dynamic interactions between electricity prices and the regional economy." Texas A&M University, 2003. http://hdl.handle.net/1969.1/2275.

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In this thesis we study characterize the dynamic relationships among two electricity price variables (residential and commercial) and six regional economic variables in order to examine each individual variable??s role in regional economic activity. We also answer the question ??Do electricity prices have impact on regional economic variables??? We use two statistical techniques as engines of analysis. First, we use directed acyclic graphs to discover how surprises (innovations) in prices from each variable are communicated to other variables in contemporaneous time. Second, we use time series
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Dongo, Kouadio Kouman. "Forecasting the Chinese Futures Markets Prices of Soy Bean and Green Bean Commodities." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/23.

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Using both single and vector processes, we fitted the Box-Jenkin’s ARIMA model and the Vector Autoregressive model following the Johansen approach, to forecast soy bean and green bean prices on the Chinese futures markets. The results are encouraging and provide empirical evidence that the vector processes perform better than the single series. The co-integration test indicated that the null hypothesis of no co-integration among the relevant variables could be rejected. This is one of the most important findings in this paper. The purposes for analyzing and modeling the series jointly are to u
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Ångman, Josefin. "What is driving house prices in Stockholm?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-130692.

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An increased mortgage cap was introduced in 2010, and as of May 1st 2016 an amortization requirement was introduced in an attempt to slow down house price development in Sweden. Fluctuations in the house prices can significantly influence macroeconomic stability, and with house prices in Stockholm rising even more rapidly than Sweden as a whole makes the understanding of Stockholm’s dynamics very important, especially for policy implications. Stockholm house prices between the first quarter of 1996 and the fourth quarter of 2015 is therefore investigated using a Vector Error Correction framewo
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Wong, Kin-man, and 黃健文. "A vector autoregression (VAR) model of housing starts and housing price in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/194603.

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It is observed that there are many different models about housing price. Yet, this is relatively smaller number of studies about housing starts. This thesis is an empirical study to work out the relationship between housing starts, housing price and other economic and policy instrumental factors. To achieve this objective, a Vector Autoregression (VAR) model is built since there is inter-relationship between housing starts and housing price. By applying previous models filled with the research gaps, a new VAR model about the housing starts and housing price in Hong Kong is built. Four hypot
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Persson, Rickard. "The short and long-term interdependencies between stock prices and dividends: A panel vector error correction approach." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-255666.

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This paper examines the short and long-term interdependencies between stock prices and dividends. I utilize firm level data from FTSE ALL SHARE from 1990-2014 and apply panel vector error correction model estimated with Engle & Grangers (1987) two-step procedure. The results show that there is a bi-directional long-term relationship between stock prices and dividends, i.e. an adjustment process is at work when a disequilibrium occurs. I also find a bi-directional short-term relationship. This paper also shows that Lintners model and the present value model are relevant frameworks in stock
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Fischer, Manfred M., Florian Huber, Michael Pfarrhofer, and Petra Staufer-Steinnocher. "The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions." WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6065/1/2018%2D02%2D16_housing_favar_final.pdf.

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In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary poli
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Borén, Christofer, and Felix Ewert. "Assessing the Effect of the Riksbank Repo Rate on National Output and Price Level in Sweden : Focusing on Employment and Housing Prices." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228969.

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There is no single commonly adapted model that explains the influence that various monetary policy instruments carry for the economy. During 2011-2017, the Swedish inflation rate has remained below the 2 percent target which has led the Riksbank to take measures aimed at stimulating the inflation. As of May 2018, the repo rate has experienced a number of decreases and is now at 􀀀0:50% which represents an unprecedentedly low level. With the inflation rate remaining below the target whilst the housing market has experienced substantial growth and recent decline, the question arises regarding wha
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Rostami, Jako, and Fredrik Hansson. "Time Series Forecasting of House Prices: An evaluation of a Support Vector Machine and a Recurrent Neural Network with LSTM cells." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385823.

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In this thesis, we examine the performance of different forecasting methods. We use dataof monthly house prices from the larger Stockholm area and the municipality of Uppsalabetween 2005 and early 2019 as the time series to be forecast. Firstly, we compare theperformance of two machine learning methods, the Long Short-Term Memory, and theSupport Vector Machine methods. The two methods forecasts are compared, and themodel with the lowest forecasting error measured by three metrics is chosen to be comparedwith a classic seasonal ARIMA model. We find that the Long Short-Term Memorymethod is the b
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Tao, Juan. "A re-examination of the relationship between FTSE100 index and futures prices." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/8071.

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This thesis examines the validity of the cost of carry model for pricing FTSE100 futures contracts and the relationship between FTSE100 spot and futures markets during two sub-periods characterised by different market trading systems employed by the LSE and LIFFE. The empirical work is carried out using three approaches to econometric modeling: a basic VECM for spot and futures prices, a VECM extended with a DCCTGARCH framework to account for the conditional variance-covariance structure for spot and futures prices and a threshold VECM to capture regime-dependent spot-futures price dynamics. O
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Books on the topic "Vector prices"

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Howlett, Derval. Money, credit and prices: A VAR analysis. Research and Publications Department, Central Bank of Ireland, 1994.

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Elitzak, Howard. Quarterly forecasting of meat retail prices: A vector autoregression approach. U.S. Dept of Agriculture, Economic Research Service, Commodity Economics Division, 1989.

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Eckstein, Zvi. Agricultural supply response using vector autoregressions (VAR) with panel data: Some evidence from India. David Horowitz Institute for the Research of Developing Countries, Tel-Aviv University, 1985.

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Ang, Andrew. A no-arbitrage vector autoregression of term structure dynamics with macroeconomic and latent variables. National Bureau of Economic Research, 2001.

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Beltratti, Andrea. Asset prices and persistence in fundamentals: A vector arma estimation of expectations theories for stocks and bonds. LSE Financial Markets Group, 1991.

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Bidard, Christian. Monotonic movement of price vectors. Department of Economics and Economic History, Manchester Metropolitan University, 1994.

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Salvatore, R. A. Vector Prime. Random House Publishing Group, 2003.

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Salvatore, R. A. Star Wars: Vector Prime: The New Jedi Order #1. Ballantine Pub. Group, 1999.

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Campbell, John Y. A variance decomposition for stock returns. LSE Financial Markets Group, 1990.

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Campbell, John Y. A variance decomposition for stock returns. National Bureau of Economic Research, 1990.

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Book chapters on the topic "Vector prices"

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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples. CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0174.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine th
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Mokhtarzadeh, Fatemeh. "A global vector autoregression model for softwood lumber trade." In International trade in forest products: lumber trade disputes, models and examples. CABI, 2021. http://dx.doi.org/10.1079/9781789248234.0008.

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Abstract A novel econometric approach is developed in this chapter, namely, the Global Vector Autoregressive (GVAR) model. It provides a comprehensive framework for analyzing the country-level impacts of various domestic, foreign, and/or global shocks on softwood lumber trade. The GVAR approach is applied to Canada-U.S. trade in softwood lumber and used to analyze the effect of external shocks on Canadian lumber prices. Findings indicate that Canada's export prices are positively correlated to U.S. housing starts and real GDP. Further, using impulse response functions, it is used to examine th
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Chiroma, Haruna, Sameem Abdul-Kareem, Adamau Abubakar, Akram M. Zeki, and Mohammed Joda Usman. "Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices." In Lecture Notes in Electrical Engineering. Springer Singapore, 2013. http://dx.doi.org/10.1007/978-981-4585-18-7_23.

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Annas, Suwardi, Zulkifli Rais, Aswi Aswi, Indrayasaro, and Nurfajriani. "Implementation of Support Vector Regression (SVR) Analysis in Predicting Gold Prices in Indonesia." In Advances in Computer Science Research. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-332-0_12.

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Xiao-lin, Zhou, and Wu Hai-wei. "Crude Oil Prices Predictive Model Based on Support Vector Machine and Particle Swarm Optimization." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29455-6_89.

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Östermark, Ralf. "Modeling Cointegrated Processes by a Vector-Valued State Space Algorithm — Evidence on The Impact of Japanese Stock Prices on The Finnish Derivatives Market." In Applications of Computer Aided Time Series Modeling. Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-2252-1_7.

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Campbell, Geoffrey B. "Euler Products Over Primes and New VPV Formulas." In Vector Partitions, Visible Points and Ramanujan Functions. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003174158-30.

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Napolitano, Jim. "Vectors and Matrices." In A Mathematica Primer for Physicists. CRC Press, 2018. http://dx.doi.org/10.1201/b21981-6.

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Shiller, Robert J. "Price-Conditional Vector Autoregressions and Theories of Stock Price Determination." In A Reappraisal of the Efficiency of Financial Markets. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74741-0_24.

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R., Abirami, and Vijaya M.S. "Stock Price Prediction Using Support Vector Regression." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29219-4_67.

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Conference papers on the topic "Vector prices"

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Handhayani, Teny, Irvan Lewenusa, and Marchel Yusuf Rumlawang Arpipi. "Forecasting Volatile Fresh Chili Prices In Indonesia Using Support Vector Regression." In 2024 International Conference on Information Technology Research and Innovation (ICITRI). IEEE, 2024. http://dx.doi.org/10.1109/icitri62858.2024.10699118.

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Chen, Mingyuan, Jifeng Song, Yini He, et al. "Support Vector Electromechanical Price Prediction Method Based on Synchronous Squeezing Wavelet Transform." In 2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET). IEEE, 2024. http://dx.doi.org/10.1109/icepet61938.2024.10626280.

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Tören, Evrim. "The Impact of Stock Prices on Consumption and Interest Rate in Turkey: Evidence from a Time Varying Vector Autoregressive Model." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01142.

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This paper aims to examine the spillovers from stock prices onto consumption and interest rate for Turkey by using a time-varying vector autoregressive model with stochastic volatility. A three-variable time-varying vector autoregressive model is estimated to capture the time-varying nature of the macroeconomic dynamics in the Turkish economy between real consumption, nominal interest rate and real stock prices. In order to obtain the macroeconomic dynamics in a small open economy, the data covers the period 1987:Q1 until 2013:Q3 in Turkey. The sample data is gathered from the official website
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Santana, Everton, Saulo Mastelini, and Sylvio Jr. "Deep Regressor Stacking for Air Ticket Prices Prediction." In XIII Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2017. http://dx.doi.org/10.5753/sbsi.2017.6022.

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Purchasing air tickets by the lowest price is a challenging task for consumers since the prices might fluctuate over time influenced by several factors. In order to support users’ decision, some price prediction techniques have been developed. Considering that this problem could be solved by multi-target approaches from Machine Learning, this work proposes a novel method looking forward to obtaining an improvement in air ticket prices prediction. The method, called Deep Regressor Stacking (DRS), applies a naive deep learning methodology to reach more accurate predictions. To evaluate the contr
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Baral, Gaurab, and Junxiu Zhou. "A hybrid Regression method for Predicting Housing Prices." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005725.

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Accurate house price prediction is crucial for accommodating the diverse needs of stakeholders in the home-buying process. House prices can be affected by various factors, such as location, construction date, exterior, etc. This work proposes a hybrid regression method that leverages the strengths of different regression techniques to improve prediction accuracy. Specifically, this work looks at conventional linear regression and other machine learning techniques such as support vector regression (SVR), and XGBoost regression. Then we compare these models with our proposed hybrid regression mo
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İzgi, Mehmet Tevfik, Faig Mammadov, and Oğuzhan Özçelebi. "The Impact of Agricultural Price Inflation on Food Security: An Analysis of Countries Surrounding the Black Sea." In International Conference on Eurasian Economies. Eurasian Economists Association, 2023. http://dx.doi.org/10.36880/c15.02806.

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This article examines the impact of inflation in agricultural prices on food security in the countries surrounding the Black Sea, including Bulgaria, Georgia, Romania, the Russian Federation, Turkey, and Ukraine. Concerns about inflation in agricultural prices and food security have increased globally in recent years, especially due to the COVID-19 pandemic and the Russia-Ukraine conflict, which has resulted in problems with agricultural production and logistical constraints, leading to increased food prices worldwide. This study analyzes the impact of agricultural price inflation on food secu
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Stepovaya, A. Y., and N. A. Babkina. "ANALISIS OF PRICES OF GOODS OF THE COMPANY "PROCTER&GAMBLE" ON THE INTERNET PLATFORMS OF RUSSIA AND CHINA." In RUSSIA AND CHINA: A VECTOR OF DEVELOPMENT. Amur State University, 2019. http://dx.doi.org/10.22250/rc.2019.1.46.

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Sroka, Lukasz. "APPLYING OF RANDOM FOREST AND SUPPORT VECTOR MACHINE IN PREDICTING PRICES OF URANIUM COMPANIES." In 10th SWS International Scientific Conferences on SOCIAL SCIENCES - ISCSS 2023. SGEM WORLD SCIENCE, 2023. http://dx.doi.org/10.35603/sws.iscss.2023/s03.14.

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Due to the war in Ukraine and restrictions on the hydrocarbons export from Russia by the European countries, uranium companies are again becoming an interesting sector in terms of investment. Consequently, it is important for investors to have accurate forecasts of uranium sector. This article applies machine learning algorithms such as the Random Forests and the Support Vector Machine to predict future URA ETF prices for the next five periods. The study was conducted using data on the ETF Global X Uranium for the period from 08/11/2010 to 31/05/2023 was obtained from investing.com. The data c
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Bal, Harun, Mehmet Demiral, and Filiz Yetiz. "Exchange Rate Pass-Through to Domestic Prices: Evidence from OECD Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01951.

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There is an immense literature on the effects of exchange rate changes on macroeconomic indicators, specifically on the trade balance, growth, inflation, and overall productivity in open economies. One of the main attempts in the related literature is about ascertaining whether the exchange rate fluctuations alter domestic prices. This possible mechanism is called as the pass-through effect which is getting more important since the argument that exchange rate adjustment is a part of the solution for global rebalancing is empirically well-supported. Starting from this claim, this study purposes
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Pongiannan, R. K., Swetanshu Agrawal, Samudra Banerjee, R. Brindha, Richard Pravin A, and Franklin J. "Predicting Average Tomato Prices Using Support Vector Machine with Polynomial Features." In 2023 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2023. http://dx.doi.org/10.1109/icscan58655.2023.10394972.

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Reports on the topic "Vector prices"

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Alviarez, Vanessa, Peter Pedroni, Andrew Powell, and Ingri Katherine Quevedo Rocha. International versus Domestic Shocks and Pass-Through to Country Prices: A Heterogeneous VAR Approach. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013406.

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This study investigates the disconnect between falling agricultural commodity prices and persistent food inflation by applying a Heterogeneous Vector Autoregression (VAR) model to a panel of 203 countries using data from 1961 to 2022. It analyzes the impact of global crops, fertilizer, and oil prices on domestic inflation and explores the asymmetries in the pass-through of global shocks. Results show that fertilizer price shocks significantly influence crop prices, especially maize and soybeans, while production shocks have a weaker effect. Demand-driven price changes exhibit higher pass-throu
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Galindo, Arturo, and Victoria Nuguer. Fuel-Price Shocks and Inflation in Latin America and the Caribbean. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004724.

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We estimate the impact of fuel-commodity price shocks on inflation and inflation expectations for eight Latin American countries in which monetary policy follows inflation-targeting frameworks. We use Bayesian Vector Autoregressive models (BVARs) and data from 2005 and up to 2022 to quantify these impacts. We find that the fuel-price shocks are significant in all cases and the response ranges between 0.01 and 0.04 percentage points of inflation, following a 1 p.p. shock to fuel prices. A variance decomposition exercise shows that more than 50% of the outburst in inflation that these countries
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Moran, Kevin, Dalibor Stevanovic, and Stéphane Surprenant. Risk Scenarios and Macroeconomic Forecasts. CIRANO, 2024. http://dx.doi.org/10.54932/dcxi8467.

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This paper discusses the usefulness of risk scenarios – forecasts conditional on specific future paths for economic variables and shocks – for monitoring the Canadian economy. To do so, we use a Vector Autoregressive (VAR) approach to produce macroeconomic forecasts conditional on four risk scenarios: high oil prices, a US recession, a tight labor market, and a restrictive monetary policy. The results show that these scenarios represent significant risk factors for the evolution of the Canadian economy. In particular, the high-oil-price scenario is beneficial for the Canadian economy, while a
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, 2015. http://dx.doi.org/10.34074/rsrp.039.

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This study re-investigates the price discovery dynamics of selected stocks cross-listed on the Australian Stock Exchange (ASX) and the New Zealand Stock Exchange (NZX) during a bear trading phase from January 2008 to December 2011. A differing price discovery dynamic in a bear market versus a bull market may occur because of variations in investor sentiments and disparities in the role of the stock prices. Using intraday data, we employ the vector error correction mechanism, Hasbrouck’s (1995) information share and Grammig et al.’s (2005) conditional information share methods. Consistent with
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Larrahondo, Cristhian, Augusto Chávez, Laura Giles Álvarez, and Leandro Gaston Andrian. The exchange rate passthrough to domestic prices, new evidence from Colombia. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013378.

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This paper calculates the exchange rate pass through (ERPT) with time constant and time varying coefficients for Colombia between 2006 and 2023. It then estimates the ERPT during four specific depreciation events during the period of analysis: the 2008 financial crisis, the 2014-2016 fall in international fuel prices, the COVID-19 pandemic and the post-COVID recovery. A Bayesian Vector Autoregressive model with exogenous variables (BVARX) model with time constant and time varying coefficients is used for the exercise. The results for time constant coefficients show that a 1 percentage point (p
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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the
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Read, Matthew. Sign Restrictions and Supply-demand Decompositions of Inflation. Reserve Bank of Australia, 2024. http://dx.doi.org/10.47688/rdp2024-05.

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Policymakers are often interested in the degree to which changes in prices are driven by shocks to supply or demand. One way to estimate the contributions of these shocks is with a structural vector autoregression identified using sign restrictions on the slopes of demand and supply curves. The appeal of this approach is that it relies on uncontroversial assumptions. However, sign restrictions only identify decompositions up to a set. I characterise the conditions under which these sets are informative, examining both historical decompositions (contributions to outcomes) and forecast error var
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Khadan, Jeetendra. An Econometric Analysis of Energy Revenue and Government Expenditure Shocks on Economic Growth in Trinidad and Tobago. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0011776.

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Energy revenues represent roughly 45 percent of Trinidad and Tobago's GDP and are highly volatile since they are correlated with the price of oil and gas. Hence, sharp changes in energy prices, whether temporary or sustained, can have important consequences for economic growth and overall macroeconomic performance. After the 2014 crash in oil prices, a key challenge that emerged for policymakers in hydrocarbon-exporting countries is how to manage fiscal retrenchment in an environment of subdued growth. Using structural vector autoregression, this article examines three questions related to thi
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Agudelo, Johana, Yolima Reyes, Leslie Bruzón, et al. Primer caso identificado de leishmaniasis visceral en el municipio de Hatonuevo, La Guajira, 2018. Instituto Nacional de Salud, 2020. http://dx.doi.org/10.33610/01229907.2020v2n1a4.

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Introducción: las leishmaniasis son zoonosis que afectan la piel, las mucosas y las vísceras, causadas por un protozoario flagelado del género Leishmania, introducido al cuerpo por la picadura de un insecto flebotomíneo del género Lutzomyia. El 96 % de los casos en esta región, se encuentran en Brasil, Argentina y Colombia (valle del Magdalena y en la zona caribe) (1). Las especies incriminadas como vectores de leishmaniasis visceral son: L. longipalpis, y L. Evansi, y el principal reservorio domestico es el perro. Los objetivos fueron caracterizar el caso e identificar los factores de riesgo
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