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This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities.

To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics.

The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods.

Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

Access to daily store level scanner data has been increasingly easier in recent years in Japan and time series analysis based on a sales response model is becoming realistic. Introduces a new method of combining time series analysis and regression analysis on the price promotion effect, which enables simultaneous decomposition of store level scanner sales into trend (including seasonality), day‐of‐the‐week effect and explanatory variable effect due to price promotion. The method was applied to daily store level scanner sales of milk, showing evidence of the existence of day‐of‐the‐week effect. Further, a method of incorporating several kinds of price‐cut variables in regression analysis and the analyzed results were presented.

This paper aims to study the impact of existence and lack of discount on the relationships between one manufacturer and one retailer under the cooperative and the non-cooperative games and the members’ profits are compared.

In the first approach, the manufacturer’s price function is constant, and in the second approach, this price function is a decreasing function with respect to lot size. These approaches are modeled through three games structure, including two Stackelberg games and one cooperative game.

Some numerical instances comprising sensitivity analysis are provided, and then the members’ profits in different scenarios are compared. This paper reveals that in the presented models, whether the members are inclined to change their profits.

This paper presents a tool of decision-making for the type of relationships of members in two different circumstances, and an approach is also presented to maximize the members’ profit.

In this paper, the relationships between one manufacturer and one retailer are studied under six different circumstances, where pricing, cooperative advertising and inventory cost are considered simultaneously. Also, a different model is presented to make a balance in individual profits and gain more profit for each member compared to the cooperative and non-cooperative game.

The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010).

The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.

As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.

In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.

Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.

It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.

While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

The study develops a mathematical model of the firm to derive theoretical foundations for the balanced scorecard concept (BSC). The model is based on several parts which are integrated into a company model. This model includes the demand function, the production function and the objective function of the firm which are depicted by traditional microeconomic concepts. Demand is presented as a function of price and customer relationship management (CRM) costs. Production is assumed to depend on labor, capital, and development and learning (D&L) costs. Simple dynamics is included both in the demand and production function. The strategy of the firm is depicted by the objective function based on profit and net sales. The output variables of the model are classified as the four perspectives of BSC. The effects of the objectives (strategies) on the importance (shadow prices) of the constraints are analysed. It is shown that a change in the objectives may alter the order of their importance. Thus, a change in the strategy should be accompanied with a change in the focus of BSC. Furthermore, non‐financial and financial performance ratios may change in opposite directions, when the strategy is shifted towards revenue maximization. Thus, inconsistencies with the interpretation of cause and effects may emerge, when the strategy is shifted. Numerical examples are presented to demonstrate the results.

This is the first of two parts of a paper in which we shall develop microeconomic price models of monopolistic competition using methods and tools of feedback theory. The models discussed are based on an approach presented by Billstroem and Thore in 1964. In the second part we will develop a different approach which can better deal with real problems. All feedback models will be simulated by means of the simulation language CSMP/360 III.

Traditionally, full-service broker/dealers catering to institutional investors have bundled trade execution with investment research. Since 2018, new market regulation has forced broker/dealers to unbundle and to sell research separately. The purpose of this paper is to shed some light on the expected pricing of research.

A stylized model is presented in this study in which a monopolist fixed income, currencies and commodities (FICC) research provider faces a linear demand function and picks an appropriate price schedule.

It is shown that it is important to initiate the price discovery process using a low price and that some broker/dealers will not be able to identify a regulatory compliant price/quantity solution because their research-production fixed cost is very high compared to the research demand function they face.

There are three main findings from our model: pricing research at cost is not always possible; if there is a unique solution, an iterative approach only works when starting off with a low-enough initial price; and if there are two solutions, only the low-cost/high-volume solution can be discovered in an iterative process.

The results presented are important to broker/dealers about to discover the market demand for their FICC research publications on the back of the implementation of MiFID II. Having distributed FICC research for free in the past, they have no knowledge about the demand function (other than what is demanded at a price of zero). Because research publications are highly differentiated products, observing the pricing of competitors is insufficient. Iteratively gaining knowledge about the demand function using price adjustments and customer questionnaires becomes the most likely mean for discovering the demand function. It is important to initiate the price discovery process with a low price. Some broker/dealers will not be able to identify a regulatory compliant price/quantity solution because their research-production fixed cost is too high compared to the research demand function they face. Finally, it is shown that these broker/dealers with two possible equilibriums face difficulty in identifying the high-price/low-volume research equilibrium because of the non-converging nature of the iterative process.

Although the wind power industry has been booming in China during the last decade, the development of wind turbine aftermarket service is still lagging behind, which seriously affects the operational efficiency of wind farms. If wind turbine manufacturers get involved in the aftermarket, the service pricing policy will impact the profits of both the manufacturer and the wind farm. Therefore, it is necessary to discuss an optimal service pricing strategy in the wind turbine aftermarket and design a method to improve electricity generation efficiency through service contract design. The paper aims to discuss these issues.

In order to decide the maintenance quantity and channel effort level, the authors design a normal Stackelberg game and an efficiency value-added revenue-sharing contract and discuss two kinds of revenue increment sharing models under situations, in which the supply chain’s leaders are the wind farm and the wind turbine manufacturer, respectively.

The results show that in either case, there exist optimal power generation revenue-sharing ratios that can maximize profit. At the same time, the authors outline an optimal service pricing policy, maintenance demand policy and channel service effort-level policy. The results summarize the influences of wind aftermarket services on wind farms’ and wind turbine manufacturers’ profit, which provides managerial insights into the process of manufacturing servitization.

The manufacturer’s channel effort level will influence the power generation increments very much, so the authors have developed a mechanism to stimulate the manufacturer improving the efficiency of aftermarket services.

Taking the power generation increment revenue as the profit increment function, the authors discuss the influence of service price on the profit increment of the wind farm and the wind turbine manufacturer and also consider the influence of service price on the wind farms maintenance quantity and wind turbine manufacturers channel effort level.

An attempt is made in this paper to discriminate among four groups of transfer pricing methods namely: market price, cost, negotiation, and other methods, according to the transfer pricing objectives and other environmental issues (i.e. transfer pricing determinants). Discrimination is attempted on the data collected in the study of UK companies described in the earlier paper “A Survey of UK Transfer Pricing Practice — Some Preliminary Findings”. The aim of the discrimination is to evaluate the relationship between the transfer pricing method and transfer pricing determinants. This evaluation enables the prediction of transfer pricing method for new cases according to the company's perception of the relative importance of the transfer pricing determinants.

For smooth running of business affairs, there needs to be a coordination among manufacturer, collector and retailer in forward and reverse supply chain. This paper handles the problem of making pricing, collecting and percentage sharing decisions in a closed-loop supply chain. The purpose of this paper is to examine the effect of responsibility sharing percentage on the profits of a manufacturer, a retailer and a collector. The paper further aims to understand the mutual interactions among decision variables and profit functions. It also determines the optimal selling price, optimal time, wholesale price, sharing percentage and optimal return rate in such a manner that the profit function is maximized.

The authors presented a three-echelon model consisting of a manufacturer, a retailer and a collector in the closed-loop supply chain and optimized the profits of each supply chain member. The authors introduced SRR models for the remanufacturing by providing some percentage of physical and financial support to the collector. Optimization techniques have been applied to obtain optimal solutions. Numerical examples and graphical representations of the optimal solutions are provided to illustrate the model.

This study stresses on profitable value retrieval from returned products, and it discusses how responsibility sharing can improve profitability and reduce the workload of an individual. In total, three main results are found. First, sharing and coordination among chain members can improve collector’s profit. Second, supply chain performance may also improve over time. Third, the profit of each member of the supply chain increases with an increase in sharing percentage up to a certain limit. So, the manufacturer can share the responsibility of the collector under a fixed limit.

The main limitation of this model is that there is no difference between manufactured and remanufactured products. There are many correlated issues that need to be further investigated. The future study in this direction may include multi-retailer, stochastic demand patterns.

It is directly utilized by supply chain industries in which coordination among chain members is still needed to maximize profits. This information enables the manufacturer to assist the collector financially or physically for the proper management of the three-layer supply chain. The present work will form a guideline to choose the appropriate parameter(s) and mathematical technique(s) in different situations for remanufacturable products.

From the management point of view, this study delivers the strongest result to remanufacturing companies and for whom effective and efficient coordination among chain members is vital to the overall performance of the supply chain.

There are very few studies that consider the remanufacturing of used products under a fixed time period. The authors considered selling price-sensitive and time-dependent exponentially declining demand. This model is developed by considering all possible help to a collector from manufacturer to collect used products from consumers. This research complements past research by showing coordination among supply chain members within a fixed time horizon.