Estimation Of Demand Function Research Articles

Competitive Demand Learning: A Noncooperative Pricing Algorithm with Coordinated Price Experimentation

We consider a periodical equilibrium pricing problem for multiple firms over a planning horizon of [Formula: see text] periods. At each period, firms set their selling prices and receive stochastic demand from consumers. Firms do not know their underlying demand curve, but they wish to determine the selling prices to maximize total revenue under competition. Hence, they have to do some price experiments such that the observed demand data are informative to make price decisions. However, uncoordinated price updating can render the demand information gathered by price experimentation less informative or inaccurate. We design a nonparametric learning algorithm to facilitate coordinated dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. We show that the pricing decisions, determined by estimated demand functions, converge to underlying equilibrium as time progresses. We obtain a bound of the revenue difference that has an order of [Formula: see text] and a regret bound that has an order of [Formula: see text] with respect to the number of the competitive firms [Formula: see text] and [Formula: see text]. We also develop a modified algorithm to handle the situation where some firms may have the knowledge of the demand curve.

Revealed preference tests for price competition in multi-product differentiated markets

Assumptions of competitive structure are often crucial for marginal cost estimation and counterfactual predictions. This paper introduces tests for price competition among multi-product firms. The tests are based on the firm’s revealed preference (revealed profit function). In contrast to other approaches based on estimated demand functions such as conduct parameter estimation, the proposed tests do not require any instrumental variables, even though the models can accommodate structural error terms. In this paper, I employ a demand structure introduced by Nocke and Schutz (Econometrica 86(2):523–557, 2018. https://doi.org/10.3982/ECTA14720), the discrete/continuous choice model, which nests the multinomial logit demand and CES demand functions. Any price and quantity data can be rationalized by price competition under a discrete/continuous choice model and increasing marginal costs. Adding more assumptions to the demand functions, such as logit, CES, or the co-evolving and log-concave property produces some falsifiable restrictions.

SVOD OTT 선택 결정요인 및 오리지널 콘텐츠에 관한 논의

The importance of original contents of OTT(Over-The-Top) services has increased as a way to strengthen their competitiveness. This paper estimates the consumer demand function for the SVOD(subscription VOD) OTT services considering that SVOD service is a differentiated goods and that consumer multi-homing behaviour exists. We find that consumers have different preferences for product attributes when selecting SVOD OTT services, and that there is heterogeneity of preferences depending on consumers’ demographic characteristics. This study also estimates the consumers’ WTP(willingness to pay) for the original contents of OTT services based on the estimated demand function and the change in the number of subscribers due to the increase in the number of original contents by domestic OTT firms. We find that consumers have a higher WTP for original contents than non-original contents and the increase rate of OTT selection in accordance with the increase in the number of original contents is also higher than that of non-original contents. The results provide that there is room for the SVOD OTT market to expand, if domestic SVOD OTT services increase the number of original contents, but the decline in the selection rate of Netflix, the leading service in the industry, is small.

Willingness to Pay for Primary Education in Rural Pakistan

Highly subsidised public schools are the principal provider of education in the rural areas of Pakistan. Steady growth of school age population over time coupled with stagnant public funding has put enormous pressure on this system. The alternative of cost recovery through user charges has its own critics. They argue that introduction of tuition fees would substantially reduce the already small representation of low-income households in primary schools due to high price elasticity of their demand for schooling. Moreover, the revenuegenerating potential of this policy may also be limited due to same reason. The present study uses a discrete choice random utility model of household utility maximising behaviour to evaluate feasibility and consequences of introducing user fees in primary schools in rural Pakistan, particularly with reference to above criticisms. The demand function for school enrolment derived from this model allows us to test the hypothesis that price elasticity of demand for schooling varies with income. It also provides estimates of the parameters of the utility function needed for measuring parents’ willingness to pay for their childrens’ education if money generated from tuition fees is reinvested in education. The estimated demand function takes into account total price of education, including opportunity cost. Estimation results show that price elasticity of demand for school enrolment is higher for lower-income groups. Hence school enrolment of the poorest children would bear the main brunt of user fees policy. Children’s gender and age, father’s education, presence of T.V. in the household, and community variables like the presence of an elected district council member, electricity, and public transport in the village turn out to be significant influences on the probability of primary school enrolment. Willingness to pay for education is lower for poorer households and can generate revenues to cover only a fraction of the cost of running a school. Hence the need to search for other sources of financing primary education in rural Pakistan.

Selecting correct functional form in consumption function: Analysis of energy demand at household level.

In the estimation of demand functions for energy resources, parametric econometric models of energy demand are commonly used to predict future energy needs. The functional forms commonly assumed in parametric energy demand models include linear functional forms, log-linear forms, trans-log models, and an almost ideal demand system. It is frequently debated which is the "best" functional form to employ in order to accurately represent the underlying relationships between the demand for various energy resources and explanatory variables such as energy prices, income, and other demographic factors. The study has focused on developing proper non-nested tests to compare the two demand systems, the double log model and the LA-AIDS model. C-test is used to test the validity of using the two parametric functional forms in models of residential energy demand. Cross-sectional household-level data of the Pakistan Social and Living Standards Measurement (PSLM) 2013-14 and Asian Development Bank (ADB) Asia and Pacific 2018 are used. Results indicate that the LA-AIDS model is better than the double log model. The estimated elasticity of own prices, cross-prices, and income in terms of spending, family size, and equipment are particularly important to producers and policymakers in making investment and incentive choices. A significant part of the budget for families is for electricity, natural gas, firewood, and other fuels; smaller budget shares are set down to other items such as kerosene oil, cylinder gas, and diesel. Household per capita demand for energy resources will rise over the next decade; therefore, the government needs to make progress on developing energy-saving strategies. If not addressed the issue properly, we may face energy shortages and high energy import bills.

Pricing implications of intervention and debt management in the primary market of Japanese government bonds

The purpose of this paper is to empirically examine the pricing implications of the Bank of Japan's (BOJ) intervention in the primary market of Japanese government bonds (JGBs). Our analysis explicitly considers the Japanese government's debt management. We model the demand and supply of JGBs in multiple maturities as a simultaneous equation system and estimate it by using the price and quantity data on JGBs. The estimated demand function supports the preferred-habitat hypothesis, while the estimated supply function indicates that the Japanese government actively manages debt. The effect of the BOJ's intervention on the yield curve, which could have been sizable under weak arbitrage, is significantly offset by the Japanese government's debt management.

Analysis of social value for nursing robots with the estimates of demand functions

Analysis of social value for nursing robots with the estimates of demand functions

Estimating the demand for informal public transport: evidence from Antananarivo, Madagascar

Informal public transport has been growing rapidly in many developing countries. Because urban infrastructure development tends to lag behind rapid population growth, informal public transport often meets the growing gap between demand and supply in urban mobility. Despite the rich literature primarily focused on formal transport modes, the informal transport sector is relatively unknown. The paper analyzes the demand behavior in the “informal” minibus sector in Antananarivo, Madagascar, taking advantage of a recent user survey of thousands of people. It is found that the demand for informal public transport is generally inelastic. Essentially, people have no other choice but to use this kind of public transport. While the time elasticity is estimated at − 0.02 to − 0.05, the price elasticity is − 0.05 to − 0.06 for short-distance travelers, who may have alternative choices, such as motorcycle taxi or walking. Unlike formal public transportation, the demand also increases with income. Regardless of the income level, everyone uses minibuses. The estimated demand functions indicate that people prefer safety and more flexibility in transit. The paper shows that combining these improvements and fare adjustments, the informal transport sector can contribute to increasing people’s mobility and reducing traffic congestion in the city.

Estimating the Demand Function for Residential City Gas in South Korea: Findings from a Price Sensitivity Measurement Experiment

The main cooking and heating fuel in the urban area of South Korea is city gas (CG), whose main ingredient is natural gas. A total of 34 operators in the country supply residential CG (RCG) in the form of a regional monopoly. Both the government and the operators need information on the demand function for RCG to establish policies and business plans. In response to this need, this article empirically estimates the demand function for RCG. There are two difficulties in securing data necessary for this estimation. First, since all RCG operators are private and most past performance data are trade secrets, the data required for the estimation are not usually available. Second, the prices of RCG are strongly controlled by local governments and price management authorities rather than determined in the marketplace. To overcome these two difficulties, this study applied the price sensitivity measurement experiment and a survey on RCG demand was conducted with 886 households nationwide. More specifically, each household was asked about current RCG usage and rates and then how they would adjust demand for the four alternative rising RCG prices. Therefore, a total of five observations were obtained for each household. The total number of observations used in this study was 4430. The estimated demand function for RCG held statistical significance. In addition, the price and income elasticities of demand were obtained as −0.570 and 0.038, respectively, securing statistical significance. The demand for RCG was inelastic to both price change and income change. This information can be useful in various fields of policymaking related to RCG.

Sieve BLP: A semi-nonparametric model of demand for differentiated products

We develop a semi-nonparametric approach to identify and estimate the demand for differentiated products. The proposed method adopts a random coefficients discrete choice logit model (i.e., mixed logit model) in which the distribution of random coefficients is nonparametrically specified. Our method minimizes misspecification error in the distribution to which routinely used parametric approach is subject. In addition, it overcomes the practical challenge of dimensionality in the number of products that remains the main hurdle in the nonparametric estimation of demand functions. We propose a sieve estimation procedure (referred to as sieve BLP) that remains simple to implement. Extensive Monte Carlo simulations show its robust finite-sample performance under various data generating processes. We use the method to investigate the welfare implications of a sugar tax in the ready-to-eat cereal industry in the US. This application underscores the usefulness of sieve BLP due to its ability to allow for flexibly specified individual heterogeneity in demand, especially when the researcher aims to quantify the distributional effects of a policy change.

The demand for a COVID-19 vaccine

Taking willingness to pay as primitive, this paper establishes an analytical framework for demand estimation, where the estimator is robust to endogeneity of price. Applying the framework, this paper then estimates demand functions for a COVID-19 vaccine and compute the consumer surplus in both China and the UAE. We find that the price elasticities of demand are mostly greater than one in both countries. An elastic demand suggests subsidy is likely to be successful in promoting vaccination. The consumer surplus is sizeable, around 58 billion US$ in China and 646 million US$ in the UAE. The figures can inform policymakers in assessing their vaccine programs.

Estimating short-run (SR) and long-run (LR) demand elasticities of phosphate

Many empirical exercises estimating demand functions are concerned with estimating dynamic effects of price and income changes over time. Researchers are typically interested in getting estimates of both short-run (SR) and long-run (LR) elasticities, along with their standard errors. This study aimed to contribute with estimations of demand elasticities of world phosphate fertilisers. It answered the research question “How world demand of phosphate fertilisers is affected by its own price changes, cross price changes, income changes and other variables?”. Short and long run elasticities are calculated using an economic model in this paper. The findings indicate that global phosphate demand is price inelastic both in the short and long run. In addition, in both the short and long run, income elasticity, cross price elasticity, and cross yield elasticity are inelastic. Our modeling predicts that phosphate consumption for fertilisers will increase from 45.35 million tons in 2018 to around 59.16 million tons by 2028; which implies that an average annual growth rate of 2.7% of P 2 O 5 will be required every year by phosphate consumers and that additional production capacity may be needed in order to meet this future demand. The results suggest that phosphate prices are forecast to increase, as long as demand continues to rise and no new production facilities are built.

Aggregation Bias in Estimating Log‐Log Demand Function

In this study, we consider the problem of estimating demand functions using historical sales data. In particular, we study how using aggregate data may result in bias in such estimations. We consider a log‐log demand model, and two ways of how the price in the aggregate data is calculated, simple average price or weighted average price. We study whether there exists estimation bias in each of the two cases as well as the direction of the bias. We show that in general, the correlation between “average price” and “price dispersion” in each time period has an effect on the direction of the bias. We then propose ways to reduce the aggregation bias. We prove that when the price processes satisfy certain mild conditions, our debiasing procedure can recover the true parameters asymptotically. We also obtain statistical properties of the debiased parameters. We perform numerical experiments on both synthetic and real data to demonstrate the effectiveness of our approach. The numerical results show that our proposed bias mitigation approach has the potential of effectively reducing the bias in both synthetic and practical settings.

Price Discovery of Consignment Auctions for Emission Permits

A consignment auction aims to increase political feasibility by reducing the financial burden of initial permits allocation and to do the role of price discovery. However, previous analytical models presented contradictory results for the price discovery function of a consignment auction. Thus, this study reexamines whether a consignment auction can perform its price discovery function. The study uses a simple game model with several assumptions differentiated from previous analytical models: explicit consideration of the secondary market and firms as price-takers with various behaviors to respond to uncertainty about the price in the secondary market. Firms are classified into three types: speculators who seek arbitrage, doctrinarians who determine a permit demand based on an estimation of their marginal abatement cost, and neutralists who keep a permit demand the same as initial emission endowments. The results reveal that when a consignment auction was introduced, the expected equilibrium price was identical to that of the secondary market price, demonstrating that the auction could deliver the price discovery function. This is because speculators and doctrinarians provide information about their price expectations and marginal abatement cost through their estimated demand functions. Additionally, the smaller number of neutralists is, and the higher the risk-seeking propensity of speculators is, the more effective the price discovery function is.

Novel pricing strategies for revenue maximization and demand learning using an exploration-exploitation framework.

The price demand relation is a fundamental concept that models how price affects the sale of a product. It is critical to have an accurate estimate of its parameters, as it will impact the company’s revenue. The learning has to be performed very efficiently using a small window of a few test points, because of the rapid changes in price demand parameters due to seasonality and fluctuations. However, there are conflicting goals when seeking the two objectives of revenue maximization and demand learning, known as the learn/earn trade-off. This is akin to the exploration/exploitation trade-off that we encounter in machine learning and optimization algorithms. In this paper, we consider the problem of price demand function estimation, taking into account its exploration–exploitation characteristic. We design a new objective function that combines both aspects. This objective function is essentially the revenue minus a term that measures the error in parameter estimates. Recursive algorithms that optimize this objective function are derived. The proposed method outperforms other existing approaches.

Identifying the Distribution of Random Coefficients in BLP Demand Models Using One Single Variation in Product Characteristics

We develop a semi-nonparametric approach to identify and estimate the demand for differentiated products. The proposed method adopts a random coefficients discrete choice logit model (i.e., mixed logit model) in which the distribution of random coefficients is nonparametrically specified. Our method minimizes misspecification error in this distribution to which routinely used parametric approach is subject. In addition, it overcomes the practical challenge of dimensionality in the number of products that remains the main hurdle in the nonparametric estimation of demand functions. We propose a sieve estimation procedure (referred to as sieve BLP) that remains simple to implement. Extensive Monte Carlo simulations show its robust finite-sample performance under various data generating processes. We use our method to investigate the welfare implications of a sugar tax in the ready-to-eat cereal industry in the US. This application underscores the usefulness of sieve BLP due to its ability to allow for flexibly specified individual heterogeneity in demand, especially when the researcher aims to quantify the distributional effects of a policy change.

Demand elasticity for deposit services at U.S. retail banks in high and low rate environments

ABSTRACT Using techniques from industrial organization, we capture the sensitivity of deposit balances to own and competitor pricing by estimating demand functions in U.S. retail deposit markets. The demand functions are used to compute elasticities of demand for bank deposits in order to study the relative importance of deposit rates over different interest rate environments. We find that the magnitudes of own- and cross-price elasticities are higher in high rate periods (when the federal funds rate is above 2%) than in low rate periods. We also compare different sized firms and find that smaller firms’ elasticities are more sensitive to the interest rate cycle.

ESTIMATING DEMAND FOR GOOD CLIMATE AND AIR QUALITY IN TAIWAN

We measure the amenity values of climate and air quality in Taiwan using the two-step hedonic price method (HPM). Separate hedonic housing price equations, one for each of three metropolitan areas that represent respective housing markets in Northern, Central, and Southern Taiwan, are estimated. We find evidence of a significant impact of climate and air quality on housing prices. As expected, residents in Taiwan prefer a warmer winter, cooler summer, lower humidity, and better air quality. Based on the results of the housing price regression, we compute implicit prices for environmental amenities, and then pool observations from all three metropolitan areas to estimate the demand for environmental amenities. The estimated demand functions for environmental amenities are used to evaluate the benefits and losses from changes in climate and air quality in Taiwan. Our results show that, compared to the benefits of a higher temperature in January, the losses from the rise in temperature in July are much higher in such a way that global warming is resulting in significant net losses in Taiwan. The area-specific welfare analysis in this study will help in informing climate change policy formulation and decision-making in Taiwan.

Empirical Study of Relationship Between Price Elasticity of Electric Demand and Seasonal Effects

Purpose - This study investigates the estimation results of price elasticity in previous studies and examines the relationship between temperature effects, or seasonal effect, for the demand function of electricity and price elasticity of electricity demand. Design/Methodology/Approach - We use the monthly data of electricity demand and price from Jan, 1981to Dec, 2018 in KESIS and KEPCO. Estimates of demand functions for electricity using Stata 14 might be biased because of identification problems. It is necessary to analyze whether control of temperature or seasonal effects utilized in many studies significantly deal with the identification of demand functions for electricity. Findings - Past literature showed that controlled temperature or seasonal effects have tended to be more sensitive to price elasticity of electricity than those that did not. Moreover, except for industrial power, estimates of long-term price elasticity on electricity demand is more sensitive when temperature or seasonality is controlled. Research Implications - The results imply that price elasticity estimates are likely to be downward biased in demand function model on electricity with only price and income as variables and these also highlight that control of temperature and seasonality is one of the ways to identify demand.

Investment, depreciation and obsolescence of R&D

Time-varying depreciation rates are estimated for research and development of the United States aggregate economy and innovation-intensive industries. Mean annual R&D depreciation rates are 31.5% for software, 41% for pharmaceuticals, 42% for semiconductors, and 30.4% for aggregate economy during 1978–2014. R&D depreciation rates vary across industries.R&D investment demand has separate elasticities in time-varying depreciation, interest rate and price growth, allowing for different rates of technology shifts across industries. Software R&D investment has the same magnitude of elasticities to depreciation, interest rate and price, supporting a user cost to apply. For pharmaceuticals and semiconductors, depreciation leads to more R&D investment that implies the effect of scale. For aggregate economy, depreciation reduces R&D investment more than interest rate, indicating obsolescence.Forecasting of R&D investment is improved at both industry and aggregate level. Forecastable time-varying depreciation, interest rate and price growth predict R&D investment based on the estimated demand function. The in-sample forecast comparison for 2015–2019 confirms the superiority to the alternative methods. Out-of-sample forecasts of R&D investment are carried out through 2025, and R&D capital stocks are constructed across industries and aggregate U.S. economy.