Relevant bibliographies by topics / Vector prices

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Academic literature on the topic 'Vector prices'

Author: Grafiati
Published: 1 June 2024

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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 (November 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 used is monthly data from January 2003 until December 2020. The results indicate that in the short-run, only the lag of each vegetable oil prices affects their own price. Meanwhile, in the long-run the three prices have long-run relationship or in other words the prices are cointegrated. Using variance decomposition and impulse response shows that soybean oil price has more effect on rapeseed and palm oil prices. Therefore, it can be concluded, the fluctuation of rapeseed and palm oil prices will be affected by the price fluctuation of soybean oil price
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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 (July 25, 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; 0,151 while for the copra price, the estimated value of the parameter is 0,060; 0,004; 0,002; and for nutmeg prices the parameter value of 0,215 is obtained; 0,105; 0,625. Prediction results for April, May and June 2019, namely in April 2019 the price of cloves was Rp90.882, the price of copra was Rp4.461, and the price of nutmeg was Rp70.316. The prediction results in May 2019 of clove prices amounted to Rp90.231, copra prices amounted to Rp4.411, and nutmeg prices were Rp70.021. Predicted results in June 2019 of clove prices amounted to Rp89.392, copra prices of Rp4.356, and nutmeg prices of Rp69.532. Keywords: Vector Autoregressive (VAR) model, clove, copra, nutmeg.
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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 (July 11, 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 first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.
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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 (May 27, 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, real per capita income, agricultural land area, real sugar prices, real beef prices, and real rice prices. The data were analyzed using the VECM (Vector Error Correction Model) model. The results showed that in the short-term the factors that influence food security are income per capita real lag 1, real sugar prices lag 1, and real beef prices lag 1, while the factors that influence food security in the long-term are per capita income 1, agricultural area lag 1, real sugar 1 lag price, real beef price lag 1, and real rice price lag 1.
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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 (November 10, 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 Criterion (AIC). Based on the best VAR(p) model, granger-causality analysis is discussed, and for forecasting gasoline prices, a state space model will be developed based on the best VAR(p). State vectors are built based on canonical correlation analysis. Based on the results of granger causality analysis, gasoline prices in Indonesia are affected by past gasoline prices in Vietnam; gasoline prices in Malaysia are affected by past gasoline prices in Indonesia and Vietnam. The results of forecasting analysis for the next 12 months using the state space model show that gasoline prices in Indonesia for the next 12 months tend to have a downward trend; gasoline prices in Malaysia for the next 12 months tend to have an upward trend; and the price of gasoline in Vietnam for the next 12 months tends to have an upward trend for the first 6 months and then has a downward trend for the next 6 months.
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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 (January 26, 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, and RT but a negative relationship between stock price and M2 and interest rate (both TB & GB). Empirical findings of this study reveal that no macroeconomic variables except TB Granger cause stock price in short run. Variance Decomposition analysis shows that most of the stock price variance can be explained by its own shocks in the shorter horizon but its magnitude diminishes over the long horizon which is about 26.77% after 24 months. Therefore, empirical results suggest that stock prices are weakly exogenous relative to the macroeconomic variables. Findings of the study have important implications to market participants and financial analysts when they have chosen to invest in Bangladesh stock market.
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Roman, Monika, Aleksandra Górecka, and Joanna Domagała. "The Linkages between Crude Oil and Food Prices." Energies 13, no. 24 (December 11, 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 data series covers the period between January 1990 and September 2020. The empirical results from the paper indicate that there are long-term relationships between crude oil and meat prices. The linkage of crude oil prices occurred with food, cereal, and oil prices in the short term. Furthermore, the linkages between the analyzed variables increased in 2006–2020.
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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 (August 23, 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 tree (CART), general regression neural networks (GRNN), and backpropagation neural networks (BPNN), to forecast real estate prices. In addition, genetic algorithms were used to select parameters of machine learning models. Numerical results indicated that the least squares support vector regression outperforms the other three machine learning models in terms of forecasting accuracy. Furthermore, forecasting results generated by the least squares support vector regression are superior to previous related studies of real estate price prediction in terms of the average absolute percentage error. Thus, the machine learning-based model is a substantial and feasible way to forecast real estate prices, and the least squares support vector regression can provide relatively competitive and satisfactory results.
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Baranowski, Paweł, and Aleksandra Hałka. "Inflacja importowana w Polsce." Wiadomości Statystyczne. The Polish Statistician 2012, no. 8 (August 28, 2012): 44–54. http://dx.doi.org/10.59139/ws.2012.08.3.

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This article attempts to assess the impact of import price index on prices of goods and services (total CPI) in Poland and the producer price in the domestic market (domestic PPI). Based on monthly data, dependences were examined according to both long-term (price analysis, using the method co-integrative) and short-term (monthly analysis of price dynamics, using a vector error correction model). The study shows that both producer prices and the consumer react with a considerable delay to changes in import prices. Long-term effect of elasticity of import prices on producer prices is more than double than on consumer price and amounted to approximately 0,5. Moreover, for both measures, the long-term impact of domestic prices was stronger than in the short term.
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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 (July 20, 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 shock in the price on electricity will predict an increase in the return of the OMX 30 in the short run. This effect may come from the existence of a countercyclical risk premium. Although further research needs to be performed to conclude that this is the true reason for the observed result.
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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 methods to capture regularities in time lags among the series. Yearly time series data on two electricity prices and six regional economic variables for Montgomery County (Texas) are studied using time series methods. Directed Acyclic Graphs (DAGs) are used to impose restrictions on the Vector Auto Regression model (VAR). Using Innovation Accounting Analysis of the estimated Vector Auto Regression (VAR) model we unravel the dynamic relationships between the eight variables. We conclude that rising electricity prices have a negative impact on allregional economic variables. The commercial average electricity prices lead residential average electricity prices in the time frame we studied (1969-2000). Rising residential electricity prices also have a positive impact on income derived from transfer payments.
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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 understand the dynamic relationships over time among the series and improve the accuracy of forecasts for individuals series by utilizing the additional information available from the related series in the forecasts for each series.
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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 framework. This approach allows a separation between the long run equilibrium price and short run dynamics. Decreases in the real mortgage rate and increased real financial wealth seem to be most important in explaining rising house prices. Increased real construction costs and increased real disposable income also seem to have an effect. The estimated models suggest that around 40-50 percent, on average, of a short-term deviation from the long-run equilibrium price is closed within a year. As of the last quarter 2015, real house prices are significantly higher compared to the long run equilibrium price modeled. The deviation is found to be around 6-7 percent.
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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 hypotheses are tested in the thesis. The first and second hypotheses are if housing starts and housing price are affected by the given exogenous variables. The third hypothesis is if the past movement of economic variables reliable in predicting future values of that variable. The last hypothesis is to test if the “high-land-price” policy really pushes up the housing price. The empirical results found in this thesis are a little bit different to previous studies in Hong Kong and overseas. Factors which are frequently proved to be statistically significant are not significant in this study (e.g. interest rate and tender price index). Developers in Hong Kong are found to care more about the future market rather than the current market conditions. Many factors do not exert an influence directly on housing starts but indirectly through their impact to the change of the change of the housing price. It is interesting to know that housing starts react negatively to a change in housing price. An increase in the change of housing price is a bullish signal for the developers. They will hold the land for a while until they expect the peak is coming upon the completion of a project. Therefore, the empirical results suggest the government has to introduce some policies which will lead to a fall in housing price in case that she wants to increase the supply of new private residential housing. Developers will accelerate the applications to commence construction when they expect there will be a downward trend in the housing price (which is shown by a negative change of the housing price..
published_or_final_version
Real Estate and Construction
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Master of Philosophy
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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 valuations.
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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 policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.
Series: Working Papers in Regional Science
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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 what impact the repo rate exerts on various macroeconomic measures. In this paper, a statistical time series analysis is conducted using a Vector Autoregression model and the impulse responses are studied. A model of 7 economic variables is constructed to specially study the effect of the repo rate on employment and housing prices. Results demonstrate that rational expectations exist in the economy. Furthermore, results show that the repo rate influences factors affected by inflation rapidly, exerting maximum influence during the first year after the shock. On the other hand, real variables based on quantitative measures that are adjusted for inflation experience the greatest influence of the repo rate after a delay of 6 to 7 quarters. Employment experiences the greatest negative response to a repo rate shock after 7 quarters, with a magnitude of 0.317 standard deviations per standard deviation in the repo rate shock. Housing prices experience the greatest negative response to a repo rate shock after 4 quarters, with a magnitude of 0.209 standard deviations per standard deviation in the repo rate shock.
Det finns ingen allmänt vedertagen modell som beskriver olika penningpolitiska instruments påverkan på ekonomin. Under 2011-2017 har Sveriges inflationstakt legat under 2-procentsmålet vilket har fått Riksbanken att vidta åtgärder i syfte att stimulera inflationen. Fram till maj 2018 har upprepade sänkningar av reporäntan genomförts och den ligger i dagsläget på 0:50% vilket är den lägsta nivån någonsin. Då inflationstakten inte nått målet samtidigt som bostadsmarknaden har upplevt kraftig tillväxt och nylig nedgång uppstår frågan gällande vilken effekt som reporäntan utlovar på diverse makroekonomiska mått. I denna rapport genomförs en statistisk tidsserieanalys med en vektorautoregression och impuls-responserna studeras. En modell med 7 ekonomiska variabler skapas för att specifikt studera effekten av reporäntan på sysselsättning och bostadspriser. Resultaten visar att rationella förväntningar finns i ekonomin. Vidare visar resultaten att reporäntan influerar inflationspåverkade variabler omgående, med maximal påverkan inom det första året efter chocken. Å andra sidan påverkas volymbaserade variabler som justeras för inflation maximalt först efter en fördröjning på 6 till 7 kvartal. Sysselsättningen upplever störst negativ påverkan från en reporäntechock efter 7 kvartal motsvarande 0.317 standardavvikelser per standardavvikelse i chocken. Bostadspriser upplever störst negativ påverkan från en reporäntechock efter 4 kvartal motsvarande 0.209 standardavvikelser per standardavvikelse i chocken.
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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 better performing machine learning method for a twelve-month forecast,but that it still does not forecast as well as the ARIMA model for the same forecast period.
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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. Overall, both the basic VECM and the DCC-TGARCH analysis suggest that there are deviations from the cost of carry relationship in the first sub-sample when transactions costs in both markets are relatively high but that the cost of carry relationship tends to be valid in the second sub-sample when transactions costs are lower. This is further confirmed by the evidence of higher conditional correlations between the two markets in the second sub-sample as compared with the first, using the DCC-TGARCH analysis. This implies that the no-arbitrage cost of carry relationship between spot and futures markets is more effectively maintained by index arbitrageurs in the second period when market conditions are closer to perfect market assumptions, and hence the cost of carry model could be more reasonably used as a benchmark for pricing stock index futures. The threshold VECM analysis depicts regime-dependent price dynamics between FTSE100 spot and futures markets and leads to some interesting and important findings: arbitrage may not be practicable under some market conditions, either because it is difficult to find counterparties for the arbitrage transactions, or because there is significant risk associated with arbitrage; as a result, the cost of carry model may not always be suitable for pricing stock index futures. Furthermore, the threshold values yielded from estimating the threshold VECM reflect the average transaction costs for most arbitrageurs that are more reliable and fair than subjective estimations.
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Books on the topic "Vector prices"

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Howlett, Derval. Money, credit and prices: A VAR analysis. Dublin: 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. [Washington, DC]: 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. [Tel Aviv]: 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. Cambridge, MA: 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. London: LSE Financial Markets Group, 1991.

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

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

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

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

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Campbell, John Y. A variance decomposition for stock returns. Cambridge, MA: 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, 174–93. Wallingford: 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 the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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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, 174–93. Wallingford: 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 the effects on regional lumber export prices in Canada of: (1) a change in U.S. housing starts; (2) a reduction in U.S. GDP by one standard deviation; (3) a COVID-19 induced decline in U.S. GDP (of three standard deviations); (4) an increase in global oil prices; and, in the Appendix, (5) an increase in the long-term interest rate. Price impacts vary a great deal by Canadian region depending on the type of shock, with the propagation mechanism in Alberta significantly different from that in other regions. For example, with an oil price shock and because Alberta is a major exporter of oil, the lumber export price remains high even as the shock dissipates over time.
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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, 193–201. Singapore: 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, 97–107. Dordrecht: 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, 645–50. Berlin, Heidelberg: 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, 141–79. New York, NY: 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, 485–90. Boca Raton: 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, 71–86. Boca Raton, FL : CRC Press, Taylor & Francis Group, [2018] |: 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, 409–29. Berlin, Heidelberg: 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, 588–97. Berlin, Heidelberg: 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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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 of Central Bank of the Republic of Turkey. Overall, this study provides the evidence of significant time-varying spillovers on consumption and interest rate coming from the stock market during financial crises and implications of monetary policy in Turkey. In addition, a time-varying vector autoregressive model with stochastic volatility offers remarkable results about the impact of price shock on consumption levels in Turkey.
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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 contribution of the DRS, it was compared with the competence of the single-target regression and two state-of-the-art multi-target regressions (Stacked Single Target and Ensemble of Regressor Chains). All four approaches were performed based on Random Forest and Support Vector Machine algorithms over two real-life airfares datasets. After results, it was concluded DRS outperformed the other three methods, being the most indicated (most predictive) to assist air passengers in the prediction of flight ticket price.
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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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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 security in the aforementioned countries. The analysis uses the "producer price index" of agricultural products, such as corn, beans, sugar beets, sunflower seeds, and wheat, published by the United Nations Food and Agriculture Organization (FAO) to measure inflation, and "per capita food supply variability" to assess food security. The study examines the complex effects of agricultural product inflation on food security with the help of panel vector error correction model.
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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 contains stock financial information such as high, low, open, close, adjacent close, volume and several well-known technical indicators. The research showed that both the Random Forest and the Support Vector Machine forecast prices with less bias than the classic ARIMA model. The Random Forest algorithm forecasted prices with a constant level of bias over the forecasting period, while the error of the forecasts calculated by the Support Vector Machine algorithm for the first three periods was the lowest compared to the other of the analyzed models. studies have proved that the Random Forest algorithm and the Support Vector Machine can be used to make correct predictions for the uranium sector.
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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 to explore whether there is an exchange rate pass-through effect in 19 high-income OECD countries over the period 1990-2015. To this end, using a panel data set of consumer price index, producer price index proxied by wholesale price index, the nominal effective exchange rates, and industrial production presented by the value-added share of industry sectors in gross domestic product, structural vector autoregressive (VAR) and autoregressive distributed lag (ARDL) models are estimated in an unbalanced panel data analysis procedure. Results reveal that exchange rate pass-through effects on the domestic prices are significant but not that strong in both the short-run and the long-run. Expectedly, the pass-through effects tend to diminish over time. The study concludes that policy-makers need to consider policy actions accompanying the exchange rate changes to ensure domestic price stability which consequently interacts with many macroeconomic indicators.
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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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Hu, T., C. Chen, and H. Wei. "A Novel Methodology for Forecasting Petrochemical Product Prices in East China Market by Applying ARIMAX Time Series and Machine Learning Models." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23114-ms.

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Abstract Forecasting petrochemical product prices is essential for economic decision making in the petrochemical industry. However, it is a challenging task to achieve accurate forecasts, given the price volatility in East China market, and the fact that the petrochemical product prices can be affected by various factors relevant in the industry. Therefore, we proposed a novel methodology which applied ARIMAX time series and machine learning models, combined with feature selection, for the price forecasting. This paper proposes a novel approach, which involves four steps of data gathering, factor identification, feature selection and model construction, to forecasting the weekly and monthly prices of 24 petrochemical products, ranging from the upstream to the downstream of the petrochemical industrial chain. Among the various relevant factors which might affect the product prices, the most significant ones were identified by applying feature selection. The product prices were modelled and predicted using ARIMAX time series model and various machine learning models, including random forest (RF), support vector machine (SVM), gradient boosted decision tree (GBDT), etc. The data were classified into training set and test set. The results were assessed by mean absolute percentage error (MAPE) - a measure of forecasting accuracy, and direction statistics (Dstat), which evaluates the forecasting performance in terms of a downward/an upward trend in prices. Taking the price forecast of LLDPE in East China market as an example, it was shown by applying feature selection that, among the various relevant factors considered in this paper, the ones affecting LLDPE price the most were brent price, PE futures price and Purchasing Managers’ Index (PMI); additionally, the historical values of LLDPE price were also found to contribute to accurate forecasts. For LLDPE weekly price forecasting, the minimum MAPE of 0.7% was obtained using RF method, with Dstat being 64.1%; and the highest Dstat of 84.2% was achieved by applying GBDT and Multi-Layer Perceptron (MLP) methods, with MAPE being 1.3% and 1.4%, respectively. For LLDPE monthly price forecasting, a MAPE value of 1.3% and a Dstat value of 90.0% were achieved with ARIMAX algorithm. In general, considering all 24 petrochemical products studied in this work, good weekly and monthly forecasts were obtained regarding accuracy and tendency, by applying ARIMAX and machine learning models. The contents in this paper provide the following benefits: first, a wide range of petrochemical products were studied, filling the gaps in the literature and enriching the database; second, the applications of feature selection with a number of machine learning models, as well as ARIMAX model, to price forecasts, were evaluated and the methodology is applicable to other related industries; last but not least, the price forecasts provide guidance for petrochemical production, achieving economical and sustainable industrial development.
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Tören, Evrim, and Mehmet Balcılar. "Fiscal Policy Shocks and the Dynamics of Asset Prices in Turkey." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01285.

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Asset markets and the asset prices affect financial institutions, consumers, producers and policy makers while they are making decisions. There is an important relationship not only between the financial market and banking system but also between the housing market and the credit market. Therefore, the study analyzes the impact of fiscal policy on asset prices by using beyasian vector autoregressive models. The sample data has been gathered from the Central Bank of the Republic of Turkey. The aim is to demonstrate the effects of fiscal policy shocks on stock prices and housing prices. The data covers the period between 1988:Q1 and 2014:Q2. Overall, the results confirm that the spending shocks coming from fiscal policy have a greater influence on the stock prices. In addition, the government revenue shocks are more influential on the house prices compared to the stock prices in Turkey.
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Nainggolan, Nelson, Hanny A. H. Komalig, and Tohap Manurung. "Vector autoregressive time series model in predicting food prices in Manado city." In THE 2ND INTERNATIONAL CONFERENCE ON NATURAL SCIENCES, MATHEMATICS, APPLICATIONS, RESEARCH, AND TECHNOLOGY (ICON-SMART 2021): Materials Science and Bioinformatics for Medical, Food, and Marine Industries. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0119696.

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Reports on the topic "Vector prices"

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Galindo, Arturo, and Victoria Nuguer. Fuel-Price Shocks and Inflation in Latin America and the Caribbean. Inter-American Development Bank, March 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 experienced in 2021 and 2022 can be attributed to the shock in global fuel prices. These results are robust to changes in the specification that include additional controls, different commodity price measures, different lag structures, and alternative ordering.
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Moran, Kevin, Dalibor Stevanovic, and Stéphane Surprenant. Risk Scenarios and Macroeconomic Forecasts. CIRANO, May 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 US recession induces a significant slowdown. The very tight labor market scenario leads to additional price increases relative to benchmark and the restrictive monetary policy scenario increases the unemployment rate while lowering the inflation rate slightly.
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 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 previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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Dassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 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 previous research, we find that price discovery takes place mostly on the home market for the Australian firms and for all but one of the New Zealand firms. However, not seen in existing studies, we show that the NZX has grown in importance for both the Australian and New Zealand firms. This suggests that the NZX is deviating from being a pure satellite market.
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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, January 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 choice of prior that arises in this setting, which turns out to be important. Some sets of identifying restrictions are not particularly informative about the effects of monetary policy. However, combining the restrictions allows us to draw some useful inferences. There is robust evidence that an increase in the cash rate lowers output and consumer prices at horizons beyond a year or so. The results are consistent with the macroeconomic effects of a 100 basis point increase in the cash rate lying towards the upper end of the range of existing estimates.
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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, December 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 this challenge by focusing on Trinidad and Tobago: (1) what is the asymmetric effect of energy revenue shocks on macroeconomic performance, (2) what is the asymmetric effect of energy revenue shocks on government expenditure (disaggregated by categories), and (3) what is the effect of government expenditure shocks (disaggregated by categories) on economic growth. The results suggest that although positive energy revenue shock increases growth almost immediately, it is not sustained. A negative energy revenue shock is found to have a greater adverse effect on primary expenditure than a positive shock and this largely occurs through a reduction in capital expenditure. Transfers and subsidies, and goods and services are the most sensitive components of current expenditure to positive energy shocks. With respect to the effect of expenditure on growth, transfers and subsidies significantly reduce growth in the short run, whereas other categories of expenditure are found to have a largely positive effect on growth. These findings suggest three important implications for policymakers: the first is to reduce and or reorient public expenditure away from transfers and subsidies and towards more growth-enhancing areas; the second is the need for clear fiscal rules, and to more effectively balance the role of fiscal policy as a growth stimulus while also performing other social functions; and thirdly, these results bring into sharp focus the effectiveness of the rules of the country's stabilization fund to manage windfall energy revenues.
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Agudelo, Johana, Yolima Reyes, Leslie Bruzón, Viviana Flórez, Zulibeth Flórez, José Bonivento, José Luis Daza, et al. Primer caso identificado de leishmaniasis visceral en el municipio de Hatonuevo, La Guajira, 2018. Instituto Nacional de Salud, April 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 involucrados en la transmisión y describir las intervenciones realizadas por la entidad territorial del nivel municipal y departamental. Materiales y métodos: se realizó estudio de brote con investigación epidemiológica de campo (IEC) en el municipio de Hatonuevo-Guajira, barrio Los Mayalitos II, comunidad Guaimarito, y Guamachito. Se aplicaron herramientas de vigilancia activa, encuestas de conocimientos, actitudes y prácticas, estudio de foco, intervenciones, muestreo canino y de menores sintomáticos. Los datos fueron registrados y procesados en Microsoft Excel 2016®. Se realizó análisis descriptivo con las características del caso, abordaje e intervenciones. Los resultados se presentaron en tablas de frecuencias. Resultados: se establece como un brote de leishmaniasis visceral, caso confirmado por laboratorio, autóctono por las condiciones para la presencia del vector y reservorio positivo: niño de 14 meses, indígena, cuadro clínico de fiebre, trombocitopenia y anemia, confirmado por inmunofluorescencia indirecta (IFI) para leishmaniasis visceral, en el estudio de foco se identificó el vector y reservorio doméstico (canino) positivo en casco urbano. En la búsqueda activa comunitaria no se identificaron niños menores de cinco años con sintomatología compatible con leishmaniasis visceral. Conclusión: se establece un brote de leishmaniasis visceral con un caso confirmado por laboratorio, autóctono por las condiciones para la presencia del vector y reservorio positivo en el municipio de Hatonuevo, La Guajira en el 2018.
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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Ambaw, Dessie, Madhavi Pundit, Arief Ramayandi, and Nicholas Sim. Real Exchange Rate Misalignment and Business Cycle Fluctuations in Asia and the Pacific. Asian Development Bank, March 2022. http://dx.doi.org/10.22617/wps220066-2.

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This paper investigates the impact of real exchange rate (RER) misalignment on business cycles among 22 economies in Asia and the Pacific from 1990 to 2018. It employs a panel vector autoregression involving consumer price index (CPI) inflation, output gap, short-term interest rate, and RER misalignment. The authors find that RER overvaluation may lead to a reduction in CPI inflation and short-term interest rate. The study also illustrates Asia and the Pacific’s heterogeneity as evidenced by the output gaps of some economies, particularly in Southeast Asia, which are shown to be more susceptible to RER misalignment shocks.
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Mawassi, Munir, Baozhong Meng, and Lorne Stobbs. Development of Virus Induced Gene Silencing Tools for Functional Genomics in Grapevine. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7613887.bard.

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Grapevine is perhaps the most widely grown fruit crop. To understand the genetic make-up so as to improve the yield and quality of grapes and grape products, researchers in Europe have recently sequenced the genomes of Pinot noir and its inbred. As expected, function of many grape genes is unknown. Functional genomics studies have become the major focus of grape researchers and breeders. Current genetic approaches for gene function studies include mutagenesis, crossing and genetic transformation. However, these approaches are difficult to apply to grapes and takes long periods of time to accomplish. It is thus imperative to seek new ways for grape functional genomics studies. Virus-induced gene silencing (VIGS) offers an attractive alternative for this purpose and has proven highly effective in several herbaceous plant species including tomato, tobacco and barley. VIGS offers several advantages over existing functional genomics approaches. First, it does not require transformation to silence a plant gene target. Instead, it induces silencing of a plant gene through infection with a virus that contains the target gene sequence, which can be accomplished within a few weeks. Second, different plant genes can be readily inserted into the viral genome via molecular cloning and functions of a large number of genes can be identified within a short period of time. Our long-term goal of this research is to develop VIGS-based tools for grapevine functional genomics, made of the genomes of Grapevine virus A (GVA) from Israel and Grapevine rupestris stem pitting-associated virus (GRSPaV) from Canada. GVA and GRSPaV are members of the Flexiviridae. Both viruses have single-stranded, positive sense RNA genomes, which makes them easy to manipulate genetically and excellent candidates as VIGS vectors. In our three years research, several major breakthroughs have been made by the research groups involved in this project. We have engineered a cDNA clone of GVA into a binary vector that is infectious upon delivery into plantlets of micropropagated Vitis viniferacv. Prime. We further developed the GVA into an expression vector that successfully capable to silence endogenous genes. We also were able to assemble an infectious full-length cDNA clones of GRSPaV. In the following sections Achievements and Detailed description of the research activities, we are presenting the outcome and results of this research in details.
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