Elasticity Estimates Research Articles

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

SUBSTITUTION BETWEEN CLEAN AND DIRTY ENERGY WITH BIASED TECHNICAL CHANGE

AbstractThe elasticity of substitution between clean and dirty energy lies at the center of leading economic analyses of climate policy. Despite the importance, empirical assessments of this key parameter remain scarce. This article estimates the clean‐dirty elasticity of substitution using data on French manufacturing firms. Using the elasticity estimates, I then measure the bias in technical change at the firm level and find that technical change was largely biased toward dirty energy, despite a recent shift toward clean technologies. Finally, I recover the aggregate elasticity that combines substitution within firms and reallocation across firms from the micro elasticity estimates.

Assessing the Impact of Distance Modelling Approaches on Active Travel Predictions

Trip distance is widely considered a key determinant of active travel choice, although distance is handled in choice modelling in various ways. This study examines how linear, categorical, quadratic, and piecewise representations for distance influence mode choice. The piecewise approach demonstrates superior fit and provides more stable elasticity estimates, ranging from -1% to -3%, compared to the broader range of -0.17% to -36% observed in categorical treatments and occasional positive values in quadratic treatments. These results underscore the piecewise model’s advantage in capturing critical behavioural shifts and offering more reliable insights to inform policy.

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.

A hazai gyermekvédelmi rendszer fejlesztésében rejlő potenciális gazdasági hasznok

The study approaches child protection involvement from an economic perspective, and is thus a niche study in the Hungarian literature. It seeks to quantify the social benefits to society of mitigating the negative impact of involvement in the provision of child protection, and the maximum return on investment in the child protection system for a given payback period and discount rate. As in international research, the evaluation of the project is based on a cost-benefit analysis of the increase in educational attainment. The social benefit is estimated by determining the number of people affected and the social benefit per person affected, and the social discount rate is determined using two types of elasticity estimates. The results suggest that a greater allocation of public resources to child protection provision not only benefits the individuals concerned, but can also result in a positive net present value investment for society as a whole.

Regionalization of Climate Elasticity Preserves Dooge's Complementary Relationship

AbstractClimate elasticity of streamflow represents a nondimensional measure of the sensitivity of streamflow to climatic factors. Estimation of such elasticities from observational records has become an important alternative to scenario‐based methods of evaluating streamflow sensitivity to climate. Nearly all previous elasticity studies have used a definition of elasticity known as arc elasticity, which measures changes in streamflow about mean values of streamflow and climate. Using observational records in the western U.S., our findings reveal that elasticity definitions based on power law models lead to both regional and basin specific estimates of elasticity which are physically more realistic than estimates based on arc elasticity. Evaluating the ability of arc and power law elasticity estimators in reproducing Dooge's complementary relationship (DCR) between potential evapotranspiration and precipitation elasticities reveal that power law elasticities estimated from at‐site, panel, and hierarchical statistical models reproduce DCR, whereas corresponding estimators based on arc elasticity cannot reproduce DCR. Importantly, our regional elasticity formulations using panel and hierarchical formulations led to estimates of both regional and basin specific estimates of elasticities, enabling and contrasting streamflow sensitivity to climate across both basins and regions.

The gravity of distance: evidence from a trade embargo

Abstract On 5 June 2017, an airspace blockade was imposed on the State of Qatar by Bahrain, Saudi Arabia, United Arab Emirates (neighboring countries), and Egypt. We exploit this exogenous increase in air transportation costs toward non-blockading countries to examine the effect of increased travel distance, due to re-routing, on bilateral trade. Based on a gravity model estimated using a Poisson pseudo-maximum likelihood estimator, we find a distance elasticity of imports between −0.3 and −0.5. Overcoming the limitations of cross-sectional studies and taking advantage of this quasi-natural experiment, our findings are robust and revise downward previous estimates of the distance elasticity.

Understanding the Residential Water Demand Response to Price Changes: Measuring Price Elasticity with Social Simulations

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand.

Estimating the viscoelastic anisotropy of human skin through high-frequency ultrasound elastography.

The skin is the largest organ of the human body and serves distinct functions in protecting the body. The viscoelastic properties of the skin play a key role in supporting the skin-healing process, also it may be changed due to some skin diseases. In this study, high-frequency ultrasound (HFUS) elastography based on a Lamb wave model was used to noninvasively assess the viscoelastic anisotropy of human skin. Elastic waves were generated through an external vibrator, and the wave propagation velocity was measured through 40MHz ultrafast HFUS imaging. Through the use of a thin-layer gelatin phantom, HFUS elastography was verified to produce highly accurate estimates of elasticity and viscosity. In a human study involving five volunteers, viscoelastic anisotropy was assessed by rotating an ultrasound transducer 360°. An oval-shaped pattern in the elasticity of human forearm skin was identified, indicating the high elastic anisotropy of skin; the average elastic moduli were 24.90±6.63 and 13.64±2.67kPa along and across the collagen fiber orientation, respectively. The average viscosity of all the recruited volunteers was 3.23±0.93Pa·s. Although the examined skin exhibited elastic anisotropy, no evident viscosity anisotropy was observed.

A novel stress sensor enables accurate estimation of micro-scale tissue mechanics in quantitative micro-elastography.

Quantitative micro-elastography (QME) is a compression-based optical coherence elastography technique enabling the estimation of tissue mechanical properties on the micro-scale. QME utilizes a compliant layer as an optical stress sensor, placed between an imaging window and tissue, providing quantitative estimation of elasticity. However, the implementation of the layer is challenging and introduces unpredictable friction conditions at the contact boundaries, deteriorating the accuracy and reliability of elasticity estimation. This has largely limited the use of QME to ex vivo studies and is a barrier to clinical translation. In this work, we present a novel implementation by affixing the stress sensing layer to the imaging window and optimizing the layer thickness, enhancing the practical use of QME for in vivo applications by eliminating the requirement for manual placement of the layer, and significantly reducing variations in the friction conditions, leading to substantial improvement in the accuracy and repeatability of elasticity estimation. We performed a systematic validation of the integrated layer, demonstrating >30% improvement in sensitivity and the ability to provide mechanical contrast in a mechanically heterogeneous phantom. In addition, we demonstrate the ability to obtain accurate estimation of elasticity (<6% error compared to <14% achieved using existing QME) in homogeneous phantoms with mechanical properties ranging from 40 to 130 kPa. Furthermore, we show the integrated layer to be more robust, exhibiting increased temporal stability, as well as improved conformity to variations in sample surface topography, allowing for accurate estimation of elasticity over acquisition times 3× longer than current methods. Finally, when applied to ex vivo human breast tissue, we demonstrate the ability to distinguish between healthy and diseased tissue features, such as stroma and cancer, confirmed by co-registered histology, showcasing the potential for routine use in biomedical applications.

A frequent mistake in aggregate tourism demand modeling: The use of a composite price index of competing destinations

Price is a cornerstone of aggregate tourism modelling, and yet its measurement poses considerable challenges. In this study, we propose a theoretical framework for accurately assessing destination and substitute destination prices, comparing it with empirical methodologies and examining their impact on elasticity estimations. Our findings show that while destination price can be gauged by comparing the consumer price indices of the destination and origin adjusted by the nominal exchange rate, using a weighted measure of substitute prices across various destinations can bias the elasticity estimations of the remaining variables. We advocate separate assessments of the prices of alternative destinations, as opposed to an aggregate one.

A novel suggestion on how to adequately treat stochastic non-stationary seasonality in tourism export forecasting

Using a quarterly data sample of 28 EU countries ranging from 2004Q1 to 2016Q4, real tourism exports are modeled and estimated as a function of relative tourism export prices and aggregate real GDP. For almost all countries, HEGY tests applied to first-differenced variables cannot reject the null hypothesis of the presence of seasonal unit roots. Seasonal differencing would be necessary to remove the seasonal patterns from the data, yet employing seasonally differenced data results in information loss, distorted elasticity estimates, and mediocre forecast accuracy. Our suggestion is therefore to perform estimation and forecasting for each quarter separately. This approach avoids the use of seasonal differencing, results in plausible, seasonally varying estimates of income elasticities, and yields statistically significantly superior forecast accuracy, also in comparison to two data-driven benchmarks for seasonal data (i.e., SARIMA and ETS).

Agglomeration and the Italian North–South divide

Abstract This article offers new evidence on agglomeration economies by examining the link between total factor productivity (TFP) and employment density in Italy. We investigate whether and how the TFP–density nexus contributes to explaining a relevant share of the marked productivity gap between the northern and the southern Italian regions. We estimate TFP for a large sample of manufacturing firms and then aggregate it at the level of local labour market areas. We tackle the endogeneity issues stemming from the presence of omitted covariates and reverse causation with an innovative set of diagnostic tests and an instrumental variable approach that relies on geological and historical data. Our estimate of the TFP elasticity to the spatial concentration of economic activities is about 0.045, a magnitude comparable to those measured for other developed countries. We also show that no significant heterogeneity emerges in the intensity of agglomeration economies across the country and that the positive TFP difference in favour of the firms located in the North is not due to the tougher competition taking place in those areas.

Behavioral Efficiency and Residential Electricity Consumption: A Microdata Study

Sustainability requires that policy makers be able to use market signals to implement energy and environmental policy and that energy consumers respond rationally to these signals. Therefore, it is essential to understand how consumers’ responses to market signals are formed. We propose a new model to measure behavioral efficiency in residential electricity consumption derived from the individual householder indirect utility function. This leads to a pair of simultaneous stochastic demand frontiers for electricity consumption (kWh) and power demand (kVA). Each is a function of power demand (standing) charges and energy demand (running) charges together with the net income after demand charges, the stock of appliances and household characteristics. We estimate the model using two samples of household responses, each of which represents around one percent of the total national population available, and we also pool these samples using pseudo-panel data procedures. We demonstrate how the resulting elasticity and efficiency estimates are related to the theory of behavioral agents from recent developments in behavioral economics. These developments also use the individual indirect utility function to derive propositions based on internality and hyperbolic discounting. The econometric estimates permit the calibration of the individual welfare effects of policy initiatives using carbon tax and price incentives with behavioral agents.

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Phase-Domain Photoacoustic Mechanical Imaging for Quantitative Elastography and Viscography.

The role and importance of mechanical properties of cells and tissues in pathophysiological processes have widely been acknowledged. However, current elastography techniques most based on transverse elastic waves, diminish the translation of wave speed into elastic modulus due to its limited wave propagation direction. Here, we propose phase-domain photoacoustic mechanical imaging (PD-PAMI), leveraging the initial time and phase response characteristics of an omnidirectional photoacoustic elastic wave to quantitatively extract elastic and viscous moduli. Theoretical simulations and experiment on tissue-mimicking phantoms with different levels of viscoelastic properties were conducted to validate the approach with a precision in elasticity and viscosity estimation of 4.6% and 6.6%, respectively. The trans-scale viscoelasticity mappings over three length scales-covering cell, tissue section, and in vivo organ, were provided to demonstrate the scalability of the technique with different implementations of PD-PAMI. Experiments on animal models of breast tumour and atherosclerosis reveal that PD-PAMI technique enables effective monitoring of the viscoelastic parameters for examinations of the diseases involved with the variations in collagen or lipid composition and in inflammation level. PD-PAMI technique opens new perspectives of conventional PA imaging and provides new technical way for biomechanical imaging, prefiguring potential clinical applications in mechanopathology-involved disease diagnosis.

The demand for sparkling wine: insights on a diversified European market

PurposeThe article aims to study the demand for sparkling wines in Europe. The main objective is to estimate the classic demand parameters aggregately for the entire European area (European Union and the United Kingdom) and separately for groups of countries characterized by wine markets with similar characteristics.Design/methodology/approachUsing 15-years market data for different wine categories from the Euromonitor Passport database, the estimation of price and income elasticities is performed through a Quadratic Almost Ideal Demand System. In line with the objectives, the model is applied first to the whole European area and then separately to the considered groups of countries (subareas). To identify homogeneous subareas, a cluster analysis was performed on basic characteristics of the wine market.FindingsWhen considering the European market as a whole, sparkling wines should be considered a luxury category with a high own-price elasticity. However, the structure of their demand is rather different in different sub-areas. The observed heterogeneity suggests that differentiated policy and marketing considerations should be made. In addition, it widens the possibilities for producers, who can choose the submarkets that respond best to their needs to export their sparkling wines. This seems particularly important in markets, like the sparkling wine ones, that are experiencing a continuous expansion over the last decades.Originality/valueDespite using a methodology well-established to study wine and alcohol demand, the study fills a considerable gap in the literature. Although the demand for sparkling wine is growing worldwide, so far only a couple of studies have engaged in the analysis of its structure. In Europe, the largest market for sparkling wine, this kind of studies is completely lacking.

Trade Wars, Nominal Rigidities, and Monetary Policy

Abstract This paper shows that the outcome of trade wars for tariffs and welfare will be affected by the monetary policy regime. The key message is that trade policy interacts with monetary policy in a way that magnifies the welfare costs of discretionary monetary policy in an international setting. If countries follow monetary policies of flexible inflation targeting, trade wars are relatively mild, with low equilibrium tariffs and small welfare costs. Discretionary monetary policies imply much higher tariffs, high inflation rates, and substantially larger welfare costs. We quantify the effects of a global trade war among major economies using estimates of trade elasticities, economic size, net foreign assets, and trade openness. We find large welfare benefits of an inflation targeting monetary policy for all countries.

Development of a Novel Soft Tissue Measurement Device for Individualized Finite Element Modeling in Custom-Fit CPAP Mask Evaluation.

Individual facial soft tissue properties are necessary for creating individualized finite element (FE) models to evaluate medical devices such as continuous positive airway pressure (CPAP) masks. There are no standard tools available to measure facial soft tissue elastic moduli, and techniques in literature require advanced equipment or custom parts to replicate. We propose a simple and inexpensive soft tissue measurement (STM) indenter device to estimate facial soft tissue elasticity at five sites: chin, cheek near lip, below cheekbone, cheekbone, and cheek. The STM device consists of a probe with a linear actuator and force sensor, an adjustment system for probe orientation, a head support frame, and a controller. The device was validated on six ballistics gel samples and then tested on 28 subjects. Soft tissue thickness was also collected for each subject using ultrasound. Thickness and elastic modulus measurements were successfully collected for all subjects. The mean elastic modulus for each site is Ec = 53.04 ± 20.97kPa for the chin, El = 16.33 ± 8.37kPa for the cheek near lip, Ebc = 27.09 ± 11.38kPa for below cheekbone, Ecb = 64.79 ± 17.12kPa for the cheekbone, and Ech = 16.20 ± 5.09kPa for the cheek. The thickness and elastic modulus values are in the range of previously reported values. One subject's measured soft tissue elastic moduli and thickness were used to evaluate custom-fit CPAP mask fit in comparison to a model of that subject with arbitrary elastic moduli and thickness. The model with measured values more closely resembles in vivo leakage results. Overall, the STM provides a first estimate of facial soft tissue elasticity and is affordable and easy to build with mostly off-the-shelf parts. These values can be used to create personalized FE models to evaluate custom-fit CPAP masks.

The supply elasticity and housing price bubbles in New South Wales, Australia

AbstractIn this study, we use a supply and demand model to estimate housing supply elasticity in New South Wales Australia based on yearly data for the 126 local government areas from 1991 to 2019, and investigate effects of supply elasticity on responses of housing prices and new constructions to housing price bubbles from 2002 to 2004. In particular, when conducting supply elasticity estimation, we use multi‐factor error structures to address cross‐sectional dependence issues, which is ignored by previous studies. We find that the supply elasticity estimates are statistically significant at the state level (0.36), in the regional coastal area (0.97) and in the regional inland area (0.41 but can be inconsistent and inefficient due to cross‐sectional dependence), while perfectly inelastic in the metropolitan area. Effects of supply elasticity on the market responses to bubbles are largely affected by market heterogeneity such as overall supply elasticity levels and demand degrees. The findings may compensate for the limitations of the theory in Glaeser et al. (2008, J Urban Econ, 64:198–217) and offer potential avenues for future development of the theory. Our results implies that bubbles promote development in elastic areas of the metropolitan market and inelastic areas of the regional coastal market, and that balancing urban and regional development is important for dealing with bubble‐related affordability issues.