Price Changes Research Articles

Dynamic Price Monitoring in E-Commerce: Leveraging Web Scraping, Data Analysis, and Email Automation for Dynamic Pricing Insights

This paper focuses on the development of a web application that leverages web scraping, data storage, and email automation technologies to track real-time price changes for specific products on e-commerce platforms. Built using Next.js for the frontend, MongoDB as the database, and Nodemailer for email notifications, the system aims to provide users with a comprehensive and user-friendly interface to monitor price fluctuations. By scraping data from product pages, the application gathers key pricing metrics, including the average, lowest, highest, and current prices, and presents this information visually to help users make informed purchasing decisions. By utilizing the combination of web scraping and real-time data analysis, this project provides a dynamic solution for both consumers and businesses, offering valuable insights into price changes and trends in the competitive e-commerce landscape.

Research on the Factors Affecting House Price using Multiple Linear Regression Model

This article aims to identify the factors that affect the change in house prices. In this paper, the Multiple Linear Regression Model is used to analyze the factors with 1000 random samples from the USA which was collected from the 2nd of May in 2014 to the 10th of July in 2014. Based on the datasets, 13 variables (bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, sqft-above, sqft_basement, yr_built, yr_renovated, street, city) are selected as independent variables, and used in scatter diagram to show the correlation with the dependent variable price. Turns out that 3 variables have not shown a significant correlation with the price (yr_built, yr_renovate, city). To find which of the factors have the most significant impact on the change in house prices, VIF value is used to compare the collinearity of those 10 variables. Finally turns out that only 5 variables (bedrooms, view, condition, waterfront, sqft_lot) show the most significant effect on the price of housing. Overall, the main factors affecting the price of housing in the USA were the number of bedrooms and views next to the house, as well as the conditions needed for buying the house.

House Prices and Rents

Abstract Variation in monthly metro area house prices unrelated to expected rents clouds the information about future rents in price-rent ratios and lagged changes in house prices. The variation in house prices unrelated to expected rents is, however, correlated across areas, and the problem is mitigated by measuring rent growth regression variables net of their monthly cross-section (across-area) means. This control for price variation unrelated to expected rents substantially enhances the information about future rents that we extract from price-rent ratios and lagged changes in house prices. (JEL C51, C58, D53, G14)

Crude Price Changes and Nigeria’s Foreign Reserves, 2008-2023

The study investigated the correlational association between crude price and foreign reserves using the Nigerian economy as the jurisdiction of interest. The study covered the period 2008 to 2023 representing a 25year period. Bivariate pairwise correlational matrix was used to find that a highly significant positive linear association exists between crude price and foreign reserves. Following the findings arising from this study, it is recommended that the country should take advantage of crude price movement in building its foreign reserves given that increase in crude price will likely lead to a rise in revenue from crude sale. A high foreign reserve can act as stabilization fund for exchange rate, debt management and other shocks from international financial flows. Building foreign reserves can be beneficial in managing such exposures to the advantage of the Nigerian economy.

Maintaining Agricultural Production Profitability—A Simulation Approach to Wheat Market Dynamics

Profitability is a complex notion, intertwined with theoretical, socio-economic, and modern economic considerations. While traditionally linked to monopoly advantages, contemporary views broaden this to include shareholder value creation. In agriculture, profitability faces impediments such as rising input costs, market volatility, and economic disparities, emphasizing technological innovation, market conditions, and policy as crucial determinants. This study presents an econometric simulation model to analyze and forecast wheat production profitability in Poland from 2023 to 2027, providing insights into production efficiencies, market dynamics, and policy impacts. Using a system of recursive equations, the model forecasts profitability, integrating econometric techniques with expert insights. It examines variables like resource use, production levels, and price changes, validated through FADN data. The findings reveal that price relations and production efficiencies are the key to profitability. The model highlights market volatility, particularly through “price scissors”, as the main factor influencing profitability. Favorable price conditions significantly boost profitability and incentivize production. The model is a critical tool for analyzing wheat profitability, highlighting the influence of market and policy changes. Future work could extend the model’s use to other regions or crops, incorporating advanced technologies to improve accuracy. This study offers valuable insights for agricultural economics, aiding stakeholders in strategic decision-making.

Investor demand for stocks in Initial Public Offerings and their price behaviour after debut. Evidence from Poland

This article investigates the relationship between investor demand during an IPO and the long-term behaviour of share prices following a company’s stock market debut. Specifically, the study examines the impact of the demand observed during the public offering and the size of the offering on abnormal return rates for a sample of 116 IPOs on the Warsaw Stock Exchange between 2012 and mid-2022. The primary research hypothesis tests whether the medians of abnormal return rates, influenced by these factors, are statistically significant over several time intervals ranging from 3 to 12 months after the debut. The findings confirm that certain information from the completed public offering, particularly the relatively low number of new shares offered, plays a significant role in predicting abnormal changes in share prices during these periods. This research offers valuable insights for investors, emphasising the key factors within the IPO process that can influence the trajectory of share prices following a company’s market debut.

Research on the Relationship Between Average Cigarette Price per Box and Government Procurement in City A Based on a Regression Model

This study aims to analyze and predict the relationship between the average price per box in the cigarette market of City A and government procurement, providing a scientific basis and support for decision-making. By reviewing relevant theories and literature, qualitative prediction methods, regression prediction models, and other related theories were explored. Through the analysis of annual cigarette sales data and government procurement data in City A, a comprehensive understanding of the development of the tobacco industry and the economic trends of tobacco companies in the county was obtained. By predicting and analyzing the average price per box of cigarette sales across different years, corresponding prediction results were derived and compared with actual sales data. The prediction results indicate that the correlation coefficient between the average price per box of cigarette sales and government procurement is 0.982, implying that government procurement accounts for 96.4% of the changes in the average price per box of cigarettes. These findings offer an in-depth exploration of the relationship between the average price per box of cigarettes in City A and government procurement, providing a scientific foundation for corporate decision-making and market operations.

Climate policy uncertainty and economic growth, a moderating role of crude oil price changes: evidence from the Asian economies

Purpose This study aims to inspect the impact of US climate policy uncertainty (CPU) on the economic growth of Asian countries with the moderating role of crude oil price (COP) changes. Design/methodology/approach The Im-Pesaran Sin and Fisher-type tests are used for stationarity check, while Kao and Pedroni tests are used for cointegration analysis. The Hausman test is applied for model selection, where pooled mean group autoregressive distributed lag (PMG/ARDL) has been selected and applied. Besides, the fully modified ordinary least squares is also used for robustness analysis. Additionally, the literature review and descriptive statistics have been used. Findings The main findings disclosed that US CPU negatively impacted the economic growth of Asian economies with high significance in the long run whereas insignificant in the short run. The results further concluded that COP positively affected economic growth both in the short and long run. Furthermore, the results also revealed that COP significantly and positively moderates the relationship between CPU and COP in the long and short run. Originality/value The study is the first of its kind to examine the impact of the US CPU on the economic growth of Asian economies. Second, it further revealed the moderating role of COP between US CPU and economic growth. Third, a large panel of data from Asian countries has been considered. Fourth, the study adds to the current literature by using the PMG/ARDL model to determine the impact of US CPU on economic growth. Additionally, this study focuses on the US CPU because it is a developed country playing a significant role in energy and climate issues, and has been very uncertain.

The impacts of New York's balance billing regulation on ground ambulance pricing.

To examine the effects of New York's surprise billing regulations on price changes by emergency ground ambulance service providers. We exploited a natural experiment using a difference-in-differences design with randomization inference (RI) to examine the effects of New York state regulations on the prices of emergency ground ambulances, analyzing 2012-2019 commercial claims data. In March 2015, New York implemented a law protecting patients from surprise out-of-network (OON) balance bills, including ground ambulance services. New York's policy tied OON ground ambulance reimbursements to usual, customary, and reasonable rates that typically reflect charges. The control group consisted of similar states that never enacted surprise billing laws. Although self-funded plans are exempted from state laws, we also examined for spillover effects on self-funded plans. Multi-payer national commercial plan claims data were used. The study sample was selected by isolating claims involving an emergency ground ambulance activation code. The event study findings indicated that New York's law led to a continuous increase in prices, relative to controls. The law's implementation was associated with an overall emergency ground ambulance price increase of 13 percentage points (RI p-value: 0.07). We observed a 21-percentage-point increase in in-network prices (RI p-value: 0.07) and a 19-percentage-point increase in OON prices (RI p-value: 0.07), relative to controls, for fully insured health plans. Similar changes in overall prices and in in-network prices were observed in self-insured plans. Last, our study did not find statistically significant evidence of changes in out-of-pocket cost-sharing amounts under New York's regulation. Balance billing laws based on charges can lead to price increases for emergency ground ambulance services. Legislation intended to inoculate patients from these surprise billings for ground ambulance transportation may have unintended consequences for costs of care. Emergency ground ambulances are a major source of surprise billing. The federal No Surprises Act of 2020 excluded emergency ground ambulance services. Some states have regulations that prohibit out-of-network balance bills for ground ambulance services. The study provides the first empirical evidence on the potential impacts of regulations on price changes among emergency ground ambulance providers. The study offers evidence of state policies' effects on fully insured plans and potential spillover effects on self-funded plans. Experiences from New York's state ambulance out-of-network billing regulation indicate that tying reimbursement policies to charges may have the unintended consequences of increasing health care costs.

Price Setting and Price Stickiness: A Behavioral Foundation of Inaction Bands

Abstract This paper puts forward a behavioral theory of price setting where managers maximize perceived profits following a process of mental accounting. The theory predicts a pricing rule that is similar to—but crucially different from—that of a standard menu cost theory: there is an inaction band, but there are two rather than just one target prices, depending on whether the firm updates its price upwards or downwards. The calibrated model replicates two patterns of price microdata that standard menu cost models have difficulty accounting for: (i) The distribution of price changes has both small and large price changes, and (ii) the hazard function of price changes is downward sloping initially, that is, firms that have just recently changed their price have a higher probability of changing it again, while this probability becomes constant thereafter.

Analysis of Influential Factors of House Prices in Shenzhen

The change of house prices in Shenzhen can reflect the trend of house price changes in China, to a certain extent. This text chose the Factor Analysis Method to Specific analyze influential factors. This text put F1 as a variable, which included Total Floor Space of Residential Buildings Sold, Permanent Population, Total Floor Space under Construction of Commodity Housing, Investment in Real Estate Development, Gross Domestic Product, Local Budgetary Revenue and Expenditure. Through analyzing the data, this text deduced that Longgang, Baoan and Nanshan districts had above average values on F1. This text put F2 as a variable, which included Per Capital Disposable Income of Permanent Households and Growth Rate of Real Estate Investment, etc. Through analyzing the data, this text deduced that Nanshan, Luohu, Futian districts had above average values on F2. This text can conclude that Nanshan, Baoan and Futian Districts had a great influence on house prices in Shenzhen.

The evaluation of competition effect on rail fares using the difference-in-difference method through symmetric and lagged spans

Long-distance railway markets in Europe have become more competitive since the EU liberalisation packages. Countries like Austria, Czechia, Sweden, and Italy have experienced sustained competition, providing valuable insights into how open-access competition works. Italy's non-subsidised high-speed competition is especially interesting to study. While some researchers have studied the minimum price changes before and after a newcomer enters a route, more is needed about the duration of the effect and the impact on all fare types. Our paper estimates the competition's impact by observing the price dynamics of the incumbent company (Trenitalia) for different fare types over an extended period. We examine the Turin-Milan-Venice route where the competitor (Italo) entered the market during observation. We distinguish between different fare types through detailed descriptive analyses of the incumbent company's price behaviour. Using the difference-in-difference method, we obtain estimates of the effect for different time spans and check their robustness.Two methods are used to design difference-in-difference models: a “classical approach” that compares symmetrical intervals before and after entry and another approach based on the comparison of lagged intervals. The second method compares the specific span with the corresponding one, a year before to exclude the seasonal component. Our findings reveal significant price-reduction effects compared to the price dynamics in other market routes where competition had been introduced.

On the Use of SMAP Soil Moisture for Forecasting NDVI Over CONUS Cropland Regions

AbstractVegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Structural transformation without industrialization? Evidence from Tanzanian consumers

AbstractExport‐oriented industrialization was central to many Asian countries' structural transformation processes, but many African countries are bypassing industrialization, and the prospects for competing in global manufacturing markets are poor. Alternative structural transformation pathways all rely on domestic markets, and thus understanding their trajectories requires uncovering consumer preferences. Using detailed household expenditures data from a nationally representative panel survey in Tanzania, I estimate a flexible, stylized consumer demand system. I recover estimates of expenditure elasticities of demand for goods, services, and food in the aggregate, along with price elasticities of demand, identified using within‐household variation in prices and expenditures. I find that, across the expenditures distribution and in both rural and urban areas, consumer preferences are service facing. In particular, I show that (1) consumers sharply increase spending on services relative to goods and food as incomes increase; (2) demand for services in the aggregate is somewhat sensitive to changes in service prices; and (3) food, goods, and services are substitutes for each other. On one hand, a high propensity to consume the types of services that generate large local economic growth multipliers is consistent with driving growth in service sector employment. However, sustained growth in the long run may be limited because these services are low productivity and nontradable, and domestic markets are size constrained.

Study on the Optimization Strategy of Chinese Film Ticket Price Pricing

Film, as an important cultural industry and entertainment mode, has developed over the years and formed a relatively complete industrial chain. However, in the long-term development process, movie ticket pricing has always been a hot issue that has attracted much attention. In recent years, China's film market has shown a trend of rapid development, and the box office has repeatedly reached a new high, but the level of movie ticket prices has also been rising, which has triggered widespread social concern and discussion.Research and optimization of movie ticket pricing strategy is of great significance to improve the competitiveness of the movie market and promote the sustainable development of the movie industry.This paper first reviews the current research status of the predecessors, summarizes the relevant theoretical basis needed for the study, analyzes the existing problems in movie ticket pricing, conducts an analysis of the change in domestic ticket prices and compares it with international prices, and finally concludes with an optimal strategy.

Soliton wave profiles and dynamical analysis of fractional Ivancevic option pricing model

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.

A joint economic evaluation and FP2O techno-economic resilience approach for evaluation of suspension PVC production

The production of polyvinyl chloride (PVC) involves obtaining a thermoplastic polymer from vinyl chloride monomer (VCM) and ethylene, primarily through the suspension method. This process is vital for manufacturing various products, such as pipes and coatings. However, its high-energy consumption necessitates economic evaluations to assess feasibility. In this work, a dual approach is proposed: a Feedstock-Processing-Product-Operation (FP2O) based techno-economic resilience analysis via sensitivity analysis alongside economic evaluation. The study evaluated a PVC production process with a capacity of 518,400 t/y of VCM over a 15-year plant lifespan. The techno-economic assessment revealed a Total Capital Investment of $609, 882, 203, a Payback Period of 4.22 years, and a Return on Investment of 14.38 %. Positive Net Present Value and a cost/benefit ratio >1 indicate the plant's installation under proposed conditions is favorable. Additionally, the technical-economic resilience analysis identified the process's response to changes in PVC price, raw material costs, and production capacity, guiding improvement actions pre- and post-plant assembly.

Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach

This study introduces a novel application of a temporal convolutional network (TCN) for projecting carbon tax prices, addressing the critical need for accurate forecasting in climate policy. Utilizing data from the World Carbon Pricing Database, we demonstrate that the TCN significantly outperformed traditional time series models in capturing the complex dynamics of carbon pricing. Our model achieved a 31.4% improvement in mean absolute error over ARIMA baselines, with an MAE of 2.43 compared to 3.54 for ARIMA. The TCN model also showed superior performance across different time horizons, demonstrating a 30.0% lower MAE for 1-year projections, and enhanced adaptability to policy changes, with only a 39.8% increase in prediction error after major shifts, compared to ARIMA’s 95.6%. These results underscore the potential of deep learning for enhancing the precision of carbon price projections, thereby supporting more informed and effective climate policy decisions. Our findings have significant implications for policymakers and stakeholders in the realm of carbon pricing and climate change mitigation strategies, offering a powerful tool for navigating the complex landscape of environmental economics.

The Relationship Between Interest Rates and Agricultural Commodity Price Dynamics

ABSTRACTThe U.S. Federal Reserve has undertaken several interest rate interventions in the past decade. This study explores the relationship between U.S. corn and soybean prices and Federal Reserve monetary policy interventions, in the short and long run. We use a nonlinear autoregressive distributed lag model to capture three key features of agricultural commodity price responses to changes in interest rates. First, we observe that corn and soybean prices initially overreact to changes in interest rates, overshooting their long‐run equilibrium before gradually reverting. Second, in the short run, the relationship between soybean prices and interest rates is asymmetric; the initial price change is more pronounced for an interest rate increase than for a decrease of the same magnitude. Lastly, both corn and soybean prices display symmetry in the long run. These results help to understand how changes in interest rates affect commodity prices. Assessing the relationship between agricultural commodity prices and interest rates can assist farmers and processors in planning investments and loans based on interest rate forecasts.

Air travellers’ attitudes towards carbon emissions: evidence from the Google Flights interface

Reducing carbon dioxide emissions in the aviation industry is a formidable challenge. Understanding airline travellers’ willingness to pay for emission reductions could lead to impactful changes in flight pricing and government policies. In this study, we employ a discrete choice experiment to more accurately gauge this willingness to pay. The experiment features a comprehensive analysis of both leisure and business traveller categories, utilising a realistic interface mirroring Google Flights for journeys between Los Angeles and New York. The findings demonstrate that, on average, business flyers exhibit a significantly higher willingness to pay for emission reductions compared to leisure flyers. Notably, a subset of emissions conscious leisure flyers also exhibits a markedly higher willingness to pay. Latent class modelling indicates that this emissions conscious group accounts for about 42% of the leisure flyer sample. The larger group of price focused leisure flyers does not display any significant willingness to pay for emission reduction. Another key finding is that ethical consumption behaviour, not just stronger perceptions related to climate change, is linked to emissions-reducing preferences among both types of flyers. These results provide valuable insights for policy and pricing strategies, emphasising the heterogeneity across and within the business and leisure flyer segments.