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Academic literature on the topic 'Time price'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Time price"

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Ahmadi, Ahmadi, and R. Adisetiawan. "Multivariate Time Series in Macroeconomics." Eksis: Jurnal Ilmiah Ekonomi dan Bisnis 11, no. 2 (November 23, 2020): 151. http://dx.doi.org/10.33087/eksis.v11i2.209.

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Gold is one of the most popular commodities and investment alternatives. Gold prices are thought to be influenced by several other factors such as the US Dollar, oil price, inflation rate, and stock exchange so that gold price modeling is not only influenced by its own value. This research was conducted to determine the best forecasting model and to find out what factors influence the price of gold. This research modeled the price of gold in a multivariate and reviewed the univariate modeling that will be used as a comparison model of multivariate modeling. Univariate modeling is done using ARIMA model where the modeling results state that gold price fluctuations as white noise. Multivariate gold price modeling is done using Vector Error Correction Model with gold, oil, US Dollar and Dow Jones indices, and inflation rate as predictors. The results showed that the VECM model has been able to model the gold price well and all the factors studied influenced the gold price. The US dollar and oil prices are negatively correlated with gold prices, while the inflation rate is positively correlated with gold prices. The Dow Jones index was positively correlated with gold prices in just two periods.

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Yao, Jun, and Harmen Oppewal. "Unit pricing matters more when consumers are under time pressure." European Journal of Marketing 50, no. 5/6 (May 9, 2016): 1094–114. http://dx.doi.org/10.1108/ejm-03-2015-0122.

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Purpose This paper aims to first investigate how unit pricing affects consumers’ grocery purchase decisions and perceptions of the shopping task’s information load. The second goal is to test how time pressure enhances the behavioural and perceptual effects of displaying unit prices. Design/methodology/approach Two on-line experiments were conducted using national samples of shoppers. In Study 1, participants indicated their choices and perceptions in an inter-brand shopping scenario where prepackaged products have conflicting positions on retail price and unit price. In Study 2, participants conducted the same shopping task but now under a condition of time pressure. Findings Study 1 shows that unit pricing shifts consumer choices towards the lower unit priced options and improves their perceptions of task information load. Study 2 shows that when consumers are under time pressure, unit pricing shows stronger effects on choices but not on perceptions. Research limitations/implications The study comprised a fairly homogenous set of low involvement categories and relatively small assortments in a hypothetical purchase setting. Exploration of the role of unit pricing in more complex and more realistic purchase environments pose suitable avenues for future research. Practical implications This study shows that consumers benefit from unit pricing because it makes it easier for them to find the lower unit priced items and to more quickly complete their shopping task. Retailers will benefit from increased customer satisfaction and possibly an improved store image. Social implications The study shows that consumers generally benefit from the presence of unit pricing and that unit price information does not create harmful effects in terms of increasing their information load. Originality/value This study uses a specifically designed and controlled but nevertheless realistic grocery choice task to study the effects of unit pricing in an inter-brand context where there are only small differences in size and price. The study contributes to the literature by showing that in such conditions, unit prices help consumers compare the economic losses associated with product options. Their heuristic role is more pronounced when consumers are under time pressure. The study shows that consumers generally benefit from the presence of unit prices.

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Curry, David J., and Peter C. Riesz. "Prices and Price/Quality Relationships: A Longitudinal Analysis." Journal of Marketing 52, no. 1 (January 1988): 36–51. http://dx.doi.org/10.1177/002224298805200104.

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Though price and quality are recognized as important tactical and strategic variables for a marketing manager, few empirical data are available on the behavior of price or the correspondence between price and quality over time. The authors report results for three hypotheses derived from product life cycle theory, dynamic pricing policy, and economic information theory about price trends, price convergence, and the correspondence between price and quality among brands in 62 durable product forms. Results strongly confirm the hypotheses that prices converge as well as decrease in real terms. The decline in price variation apparently results from a narrowing of prices by all relevant competitors. Brands entering or exiting a category counterbalance one another and are nearly as likely to be priced below as above a category mean. Reduced correspondence between price and quality levels over time suggests that as pricing flexibility declines, competition may occur in the form of promotional expenditures rather than relative quality improvements. Implications of these findings for marketing strategy and consumer welfare are discussed.

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Doucouliagos, Chris. "Price exhaustion and number preference: time and price confluence in Australian stock prices." European Journal of Finance 11, no. 3 (June 2005): 207–21. http://dx.doi.org/10.1080/1351847042000254194.

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Chen, Xi, and Michael Funke. "Real-Time Warning Signs of Emerging and Collapsing Chinese House Price Bubbles." National Institute Economic Review 223 (February 2013): R39—R48. http://dx.doi.org/10.1177/002795011322300105.

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The recent increase in Chinese house prices has led to concerns that China is vulnerable to asset price shocks. In this paper, we apply recently developed recursive unit root tests to spot the beginning and the end of potential speculative bubbles in Chinese house price cycles. Overall, we find that except for 2009–10 actual house prices are not significantly disconnected from fundamentals. Thus, the evidence for speculative house price bubbles in China is in general weak.

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Michaillat, Pascal, and Emmanuel Saez. "Aggregate Demand, Idle Time, and Unemployment *." Quarterly Journal of Economics 130, no. 2 (February 8, 2015): 507–69. http://dx.doi.org/10.1093/qje/qjv006.

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Abstract This article develops a model of unemployment fluctuations. The model keeps the architecture of the general-disequilibrium model of Barro and Grossman (1971) but takes a matching approach to the labor and product markets instead of a disequilibrium approach. On the product and labor markets, both price and tightness adjust to equalize supply and demand. Since there are two equilibrium variables but only one equilibrium condition on each market, a price mechanism is needed to select an equilibrium. We focus on two polar mechanisms: fixed prices and competitive prices. When prices are fixed, aggregate demand affects unemployment as follows. An increase in aggregate demand leads firms to find more customers. This reduces the idle time of their employees and thus increases their labor demand. This in turn reduces unemployment. We combine the predictions of the model and empirical measures of product market tightness, labor market tightness, output, and employment to assess the sources of labor market fluctuations in the United States. First, we find that product market tightness and labor market tightness fluctuate a lot, which implies that the fixed-price equilibrium describes the data better than the competitive-price equilibrium. Next, we find that labor market tightness and employment are positively correlated, which suggests that the labor market fluctuations are mostly due to labor demand shocks and not to labor supply or mismatch shocks. Last, we find that product market tightness and output are positively correlated, which suggests that the labor demand shocks mostly reflect aggregate demand shocks and not technology shocks.

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Zhao, Lu-Tao, Shun-Gang Wang, and Zhi-Gang Zhang. "Oil Price Forecasting Using a Time-Varying Approach." Energies 13, no. 6 (March 17, 2020): 1403. http://dx.doi.org/10.3390/en13061403.

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The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

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Zhang, Jian Hua, Fan Tao Kong, Jian Zhai Wu, Meng Shuai Zhu, Ke Xu, and Jia Jia Liu. "Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network." Applied Mechanics and Materials 644-650 (September 2014): 2636–40. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2636.

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Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

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Bulfone, Liliana. "High prices for generics in Australia — more competition might help." Australian Health Review 33, no. 2 (2009): 200. http://dx.doi.org/10.1071/ah090200.

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It is commonly believed that dispensed prices of medicines in Australia are substantially lower than those in other developed countries, particularly the US. This article reports the results of an analysis comparing dispensed prices for the most commonly prescribed and the highest cost items in Australia with dispensed prices in the US. Although a large majority of items are less expensive in Australia than in the US, Australian prices are higher for a substantial number of products, particularly generic drugs. This article examines various policies affecting the pricing of generics in Australia. It is postulated that the main cause for higher prices for a substantial number of generic products is the lack of price competition. This results from government policy which ensures that a price reduction by one company is communicated immediately to all competitors in that market along with an invitation to match the reduced price. The dominant strategy for all suppliers is to only reduce their price in response to a reduction in price by a competitor. The result is a lack of differentiation in pricing across brands of a medicine on the Schedule of Pharmaceutical Benefits. The government could improve the structure of the generics market and encourage greater competition by ceasing to disclose competitor firms? offers to other competitors. The government could conduct pricing reviews of each generic product relatively infrequently (eg, only once annually or every 18 months). At the time of the pricing review, the government would request confidential offers on price for a generic from all players in the market. Brands should then all be listed under the Pharmaceutical Benefits Scheme (PBS) at the offered price. Prices offered by the individual supplier would apply until the next pricing review. The PBS would continue to subsidise up to the price of the lowest priced brand, with brand premiums applying to all brands priced higher than the benchmark price. Such an approach would provide opportunity for players in the market to capture market share by being the lowest priced brand.

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Guan, Xiaodong, Haishaerjiang Wushouer, Mingchun Yang, Sheng Han, Luwen Shi, Dennis Ross-Degnan, and Anita Katharina Wagner. "Influence of government price regulation and deregulation on the price of antineoplastic medications in China: a controlled interrupted time series study." BMJ Open 9, no. 11 (November 2019): e031658. http://dx.doi.org/10.1136/bmjopen-2019-031658.

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BackgroundIn October 2012, the Chinese government established maximum retail prices for specific products, including 30 antineoplastic medications. Three years later, in June 2015, the government abolished price regulation for most medications, including all antineoplastic medications. This study examined the impacts of regulation and subsequent deregulation of prices of antineoplastic medications in China.MethodsUsing hospital procurement data and an interrupted time series with comparison series design, we examined the impacts of the policy changes on relative purchase prices (Laspeyres price index) and volumes of and spending on 52 antineoplastic medications in 699 hospitals. We identified three policy periods: prior to the initial price regulation (October 2011 to September 2012); during price regulation (October 2012 to June 2015); and after price deregulation (July 2015 to June 2016).ResultsDuring government price regulation, compared with price-unregulated cancer medications (n=22, mostly newer targeted products), the relative price of price-regulated medications (n=30, mostly chemotherapeutic products) decreased significantly (β=−0.081, p<0.001). After the government price deregulation, no significant price change occurred. Neither government price regulation nor deregulation had a significant impact on average volumes of or average spending on all antineoplastic medications immediately after the policy changes or in the longer term (p>0.05).ConclusionCompared with unregulated antineoplastics, the prices of regulated antineoplastic medications decreased after setting price caps and did not increase after deregulation. To control the rapid growth of oncology medication expenditures, more effective measures than price regulation through price caps for traditional chemotherapy are needed.

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Dissertations / Theses on the topic "Time price"

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Yiu, Fu-keung. "Time series analysis of financial index /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.

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MALUF, KELLY CRISTINA FERNANDES. "SAZONAL ADJUSTEMENT OF PRICE ÍNDICES TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8683@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Esta tese tem como objetivo a comparação entre procedimentos para dessazonalização de séries temporais. As metodologias usadas serão a de Modelos Estruturais Clássicos e Bayesianos e a metodologia padrão de dessazonalização X11 ARIMA. Os dados utilizados são as 35 séries reais de índice de preços ao consumidor - IPC para a Região Metropolitana do Rio de Janeiro, fornecidas pelo Instituto Brasileiro de Geografia e Pesquisa - IBGE, no período de janeiro de 1991 até dezembro de 1997. Os pacotes computacionais utilizados no decorrer do trabalho são FORECAST PRO (X11 ARIMA0, STAMP (Estruturais Clássicos) e BATS (Estruturais Bayesianos). Além disso, foram também utilizadas séries simuladas com sazonalidade, para melhor analisar os resultados desejados.
The aim of this thesis is a comparisson study among three existing procedures for seasonal adjustment of time series, namely: the tradicional X11 ARIMA and those based on the structural model formulation, i.e., the classical approach of A. Harvey and the Bayesian counterpart of Harrison and Stevens. The data used are 25 real time series of Consumer Price Index for Metropolitan area from Rio de Janeiro from 1991 to 1997, supllied by the Instituto Brasileiro de Geografia e Estatística - IBGE. The computacional packages used during the thesis were SPSS and FORECAST PRO (X11 ARIMA), STAMP (structural classical approach) and BATS (structural bayesian approach). Also, simulated seasonal data were to provide a better understanding of the procedures.

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Blanck, Andreas. "American Option Price Approximation for Real-Time Clearing." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-144435.

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American-style options are contracts traded on financial markets. These are derivatives of some underlying security or securities that in contrast to European-style options allow their holders to exercise at any point before the contracts expire. However, this advantage aggravates the mathematical formulation of an option's value considerably, explaining why essentially no exact closed-formed pricing formulas exist. Numerous price approximation methods are although available, but their possible areas of application as well as performance, measured by speed and accuracy, differ. A clearing house offering real-time solutions are especially dependent on fast pricing methods to calculate portfolio risk, where accuracy is assumed to be an important factor to guarantee low-discrepancy estimations. Conversely, overly biased risk estimates may worsen a clearing house's ability to manage great losses, endangering the stability of a financial market it operates. The purpose of this project was to find methods with optimal performance and to investigate if price approximation errors induce biases in option portfolios' risk estimates. Regarding performance, a Quasi-Monte Carlo least squares method was found suitable for at least one type of exotic option. Yet none of the analyzed closed-form approximation methods could be assessed as optimal because of their varying strengths, where although the Binomial Tree model performed most consistently. Moreover, the answer to which method entails the best risk estimates remains inconclusive since only one set of parameters was used due to heavy calculations. A larger study involving a broader range of parameter values must therefore be performed in order to answer this reliably. However, it was revealed that large errors in risk estimates are avoided only if American standard options are priced with any of the analyzed methods and not when a faster European formula is employed. Furthermore, those that were analyzed can yield rather different risk estimates, implying that relatively large errors may arise if an inadequate method is applied.

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Kwon, Oh-Bok. "A time series analysis on interrelationships among U.S. and Korean livestock prices /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3025631.

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Yiu, Fu-keung, and 饒富強. "Time series analysis of financial index." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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Hisham, Abdelradi Khalaf Fadi Mohamed. "Understanding Recent Food Price Patterns: A Time-Series Approach." Doctoral thesis, Universitat de Barcelona, 2014. http://hdl.handle.net/10803/287226.

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The guiding theme of this thesis is the empirical analysis of recent food price behavior. It is composed of three applied studies that address the impacts of energy prices on both food price levels and volatility, as well as the impact of public information release on futures markets of major agricultural commodities. Non-structural time series econometric techniques are applied for such purpose. In the first chapter, the impact of the Spanish biodiesel industry on agricultural feedstock prices is investigated. Both price level and volatility interactions are evaluated. Three relevant prices are considered: the international crude oil price, the Spanish biodiesel blend price and the Spanish sunflower oil price. Weekly Prices are observed from November 2006 to October 2010, yielding a total of 205 observations. Blended biodiesel, sunflower and crude oil prices are found to be interrelated in the long-run. This parity is preserved by the biodiesel industry in order to be in equilibrium. The impact of biodiesel on sunflower oil price levels is found to be very modest, which is reasonable given the small size of the Spanish biodiesel industry. Volatility spillovers between sunflower and biodiesel markets are found to be significant. Evidence of asymmetries in price volatility patterns is also found, with price declines causing more price instability than price increases. Asymmetries can be triggered by the availability of alternative feedstocks in the market, as well as by the unwillingness of biodiesel producers to increase food prices when feedstocks become more expensive. In the second chapter, the impact of the EU biodiesel market on agricultural feedstock prices is analyzed. The study comprises the period between 06/11/2008 to 14/06/2012, and is based on 189 weekly prices. Cointegration analysis suggests that the three prices have a long-run equilibrium relationship that is preserved by the pure biodiesel price. Biodiesel prices are not found to have an effect on rapeseed oil prices. Volatility of pure biodiesel price is affected by its own past volatility and past pure biodiesel and rapeseed market shocks. Also, evidence is found of asymmetries in price volatility, with negative market shocks having a greater impact than positive ones. While pure biodiesel prices cannot affect rapeseed oil price-levels, they can bring instability to these prices. Inventory building and the euro-dollar exchange rate are found to be relevant risk management instruments that can be used to mitigate the biodiesel and rapeseed oil price volatilities. In the third chapter, the impact of public information in the form of USDA-NASS crop production reports on daily corn and soybeans futures prices is evaluated. The study period is between 1970 to 2004, with a total of 700 observations. Results show that USDA-NASS crop production reports significantly affect futures price levels. Report releases at the beginning and at the end of the harvest season are usually the ones exerting a stronger impact. Report releases are not however found to have an effect on price volatility, which suggests gradual price-level changes as a response to published information. Cross-market effects of news are also found to be significant.

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Agoitia, Hurtado Maria Fernanda del Carmen [Verfasser], and Thorsten [Akademischer Betreuer] Schmidt. "Time-inhomogeneous polynomial processes in electricity spot price models." Freiburg : Universität, 2017. http://d-nb.info/1140735438/34.

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Raykhel, Ilya. "Real-time automatic price prediction for eBay online trading /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2697.pdf.

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Raykhel, Ilya Igorevitch. "Real-Time Automatic Price Prediction for eBay Online Trading." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1631.

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While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model.

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Dickamore, Justin Edward. "Price Slides Within Cattle Markets Over Time and Space." DigitalCommons@USU, 2015. https://digitalcommons.usu.edu/etd/4606.

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The production of cattle in the United State is a very large business. Production begins at the cow-calf level, where a calf is born and raised to a specific weight. This weight is the weaning weight and averages between 300-600 pounds. The calf is then typically shipped to a feedlot, where it is fed a high corn ration which increases the weight of animal quickly and cost effectively to reach a sufficient slaughter weight. Cattle production takes place primarily in 5 different geographical locations which include the North Central, Southeast, Northern Plains, Southern Plains, and West regions. Due to the relationships between fed cattle prices, feeder cattle prices and feed costs, lighter weight feeder cattle typically sell for a higher price per pound than heavier weight feeder cattle. This decrease in price per pound for heavier feeders is often referred to as a feeder cattle price slide. This study is to determine how price slides have reacted over time and space due to the relative changes in fed and feeder cattle prices and the cost of feed. Weekly data was obtained from the Livestock Marketing Information Center (LMIC) on the auction price for feeder cattle at different weights for both steers and heifers. Weekly data on the futures price of live cattle and corn were also obtained from the LMIC. To determine if feeder cattle price slides had changed over time, regression analysis was used to evaluate the relationship between feeder cattle prices at varying weights with the price of fed cattle and the price of corn. Two different time periods were used for the same location: the first period was from 1992 to 1996 and the second period was from 2005 to 2015. Price slides were also examined across space. There were five different geographical locations analyzed: Oklahoma, Nebraska, Georgia, Kansas, and Montana. Each region was regressed individually and then compared. Prices slides were calculated as the difference in the regressed feeder cattle price at each weight. A combination of the time and space was used to evaluate changes in the same model. Results from the regression models returned feeder cattle prices at varying weights for steers and heifers and price slides were calculated from those estimated prices. It was found that price slides are not constant over time and that price slides are geographically specific. In the third objective, it is shown that time and space are both factors in determining price slides for feeder cattle. The implications of this study are to help cattle producers be more aware of market conditions specific to changes in feeding cost. It is also to make aware that price slides are not constant over time and space and therefore, must be reevaluated on a consistent basis.

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Books on the topic "Time price"

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Hyerczyk, James A., ed. Pattern, Price & Time. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119198499.

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Hyerczyk, James A. Pattern, Price and Time. New York: John Wiley & Sons, Ltd., 2009.

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Forecasting profits using price & time. New York: Wiley, 1998.

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Biggeri, Luigi, and Guido Ferrari, eds. Price Indexes in Time and Space. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2140-6.

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Mahy, E. PRICE S: A debrief report. Manchester: NCC, 1985.

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Unit, Low Pay, ed. Part-timers under pressure: Paying the price of flexibility. London: Low Pay Unit, 1987.

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Hurstfield, Jennifer. Part-timers under pressure: Paying the price of flexibility. London: Low Pay Unit, 1987.

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Taipalus, Katja. Detecting asset price bubbles with time-series methods. Helsinki: Finlands Bank, 2012.

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Tōkeikyoku, Japan Sōmuchō. Heisei 7-nen kijun shōhisha bukka setsuzoku shisū sōran: 1995-base linked consumer price index time series. Tōkyō: Sōmuchō Tōkeikyoku, 1996.

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Baeyens, Walter J. RSI: Logic, signals & time frame correlation. Greenville, S.C: Traders Press, 2007.

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Book chapters on the topic "Time price"

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Brown, Constance. "Price and Time." In Breakthroughs in Technical Analysis, 83–113. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9781119204749.ch5.

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Jarrow, Robert A. "Asset Price Bubbles." In Continuous-Time Asset Pricing Theory, 69–78. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77821-1_3.

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Jarrow, Robert A. "Asset Price Bubbles." In Continuous-Time Asset Pricing Theory, 75–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74410-6_3.

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Olsen, Borgar Tørre. "Component price versus time." In Broadband Access Networks, 87–98. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5795-1_8.

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Zaremba, Adam, and Jacob “Koby” Shemer. "To Time or Not to Time? Tactical Allocation Across Strategies." In Price-Based Investment Strategies, 227–41. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91530-2_8.

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Brockwell, Peter J. "An Overview of Asset–Price Models." In Handbook of Financial Time Series, 403–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-71297-8_17.

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Deng, Xiaotie, Li-Sha Huang, and Minming Li. "On Walrasian Price of CPU Time." In Lecture Notes in Computer Science, 586–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11533719_60.

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Antoniadis, I., N. Sariannidis, and S. Kontsas. "The Effect of Bitcoin Prices on US Dollar Index Price." In Advances in Time Series Data Methods in Applied Economic Research, 511–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02194-8_34.

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Carrasco, Raúl, Manuel Vargas, Ismael Soto, Diego Fuentealba, Leonardo Banguera, and Guillermo Fuertes. "Chaotic Time Series for Copper’s Price Forecast." In IFIP Advances in Information and Communication Technology, 278–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94541-5_28.

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Tsuda, Hiroshi. "Time Series Analysis of Financial Asset Price Fluctuations." In The Practice of Time Series Analysis, 283–97. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-2162-3_17.

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Conference papers on the topic "Time price"

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"Offer Price, Transaction Price and Time-On-Market." In 10th European Real Estate Society Conference: ERES Conference 2003. ERES, 2003. http://dx.doi.org/10.15396/eres2003_118.

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Combi, Carlo, Romeo Rizzi, and Pietro Sala. "The Price of Evolution in Temporal Databases." In 2015 22nd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2015. http://dx.doi.org/10.1109/time.2015.24.

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Widergren, S., K. Subbarao, D. Chassin, J. Fuller, and R. Pratt. "Residential real-time price response simulation." In 2011 IEEE Power & Energy Society General Meeting. IEEE, 2011. http://dx.doi.org/10.1109/pes.2011.6038964.

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Chen, Lu, Qilu Zhong, Xiaokui Xiao, Yunjun Gao, Pengfei Jin, and Christian S. Jensen. "Price-and-Time-Aware Dynamic Ridesharing." In 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00099.

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Mohanty, Pavitra, Darshan Patel, Parth Patel, and Sudipta Roy. "Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices." In 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2018. http://dx.doi.org/10.1109/icrito.2018.8748792.

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Tengiz, Yusuf Ziya, and Zehra Meliha Tengiz. "A Study on Beef Price in Turkey." In International Conference on Eurasian Economies. Eurasian Economists Association, 2018. http://dx.doi.org/10.36880/c10.02213.

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Abstract:

Beef prices in Turkey is increased for long time. That outcome is caused to find out the reasons which are variables related to beef prices. The target of this study is to clarify relationship of beef price and its independent variables with a regression model. Data analyzed with SPSS 23.0. Based on the model’s equation, the independent variables which are average world beef unit price (USD), per capita beef consumption in Turkey (kg), government livestock incentives (TRY), consumer price index on beef (%), exchange rate (USD/TRY), real effective exchange rate (%) and gross domestic national product (%) are found as direct proportion with beef prices. On the other hand, poultry meat as an alternative to beef has inverse proportion with the prices.

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Mannermaa, J., K. Kalliomaki, and T. Mansten. "Long term stability of medium price commercial GPS receivers." In 18th European Frequency and Time Forum (EFTF 2004). IEE, 2004. http://dx.doi.org/10.1049/cp:20040831.

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Thongkairat, Sukrit, and Roengchai Tansuchat. "Time-Varying Effect of Gold and Crude Oil prices to Stock Price Index." In 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017). Paris, France: Atlantis Press, 2017. http://dx.doi.org/10.2991/icefs-17.2017.50.

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Roy, Shaily, Samiha Nanjiba, and Amitabha Chakrabarty. "Bitcoin Price Forecasting Using Time Series Analysis." In 2018 21st International Conference of Computer and Information Technology (ICCIT). IEEE, 2018. http://dx.doi.org/10.1109/iccitechn.2018.8631923.

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Deylamsalehi, Arash, Thilo Schondienst, and Vinod M. Vokkarane. "Real-time energy price aware network routing." In 2014 11th Annual High-Capacity Optical Networks and Emerging/Enabling Technologies (HONET). IEEE, 2014. http://dx.doi.org/10.1109/honet.2014.7029353.

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Reports on the topic "Time price"

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Goldberg, Linda, and Christian Grisse. Time Variation in Asset Price Responses to Macro Announcements. Cambridge, MA: National Bureau of Economic Research, October 2013. http://dx.doi.org/10.3386/w19523.

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Hamermesh, Daniel, and Jeff Biddle. Taking Time Use Seriously: Income, Wages And Price Discrimination. Cambridge, MA: National Bureau of Economic Research, November 2018. http://dx.doi.org/10.3386/w25308.

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Rotemberg, Julio. Customer Anger at Price Increases, Time Variation in the Frequency of Price Changes and Monetary Policy. Cambridge, MA: National Bureau of Economic Research, November 2002. http://dx.doi.org/10.3386/w9320.

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Glower, Michel, Donald Haurin, and Patric Hendershott. Selling Price and Selling Time: The Impact of Seller Motivation. Cambridge, MA: National Bureau of Economic Research, March 1995. http://dx.doi.org/10.3386/w5071.

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Graves, Thomas E. It's Time for DoD to Sack Its Price Stabilization Policy,. Fort Belvoir, VA: Defense Technical Information Center, January 1995. http://dx.doi.org/10.21236/ada296148.

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Bachmann, Ruediger, Benjamin Born, Steffen Elstner, and Christian Grimme. Time-Varying Business Volatility and the Price Setting of Firms. Cambridge, MA: National Bureau of Economic Research, June 2013. http://dx.doi.org/10.3386/w19180.

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Korajczyk, Robert, Deborah Lucas, and Robert McDonald. Understanding Stock Price Behavior around the Time of Equity Issues. Cambridge, MA: National Bureau of Economic Research, November 1989. http://dx.doi.org/10.3386/w3170.

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Bajari, Patrick, Jane Cooley, Kyoo il Kim, and Christopher Timmins. A Theory-Based Approach to Hedonic Price Regressions with Time-Varying Unobserved Product Attributes: The Price of Pollution. Cambridge, MA: National Bureau of Economic Research, February 2010. http://dx.doi.org/10.3386/w15724.

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Fuster, Andreas, Stephanie Lo, and Paul Willen. The Time-Varying Price of Financial Intermediation in the Mortgage Market. Cambridge, MA: National Bureau of Economic Research, August 2017. http://dx.doi.org/10.3386/w23706.

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Andersen, Torben, Tim Bollerslev, Francis Diebold, and Clara Vega. Real-Time Price Discovery in Stock, Bond and Foreign Exchange Markets. Cambridge, MA: National Bureau of Economic Research, May 2005. http://dx.doi.org/10.3386/w11312.

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