Interrelations of Demand, Price, and Income

Schultz, Henry

doi:10.1086/254812

Cited by 35 publications

(9 citation statements)

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“…Note that it is impractical to put all influence factors into the model, only some of the most important factors are selected as inputs (to make the panel data model). Since there is evidence that price is the most critical element affecting demand [4], [5], we chose it as one of key influence factors in constructing the panel data forecasting model. In order to evaluate the forecasting performance and forecasting features of our proposed model, we conduct a sales forecasting experiment in terms of item and color data category by using nine months of real sales data from the fashion industry.…”

Section: Introductionmentioning

confidence: 99%

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Ren

Choi

Liu

2015

IEEE Trans. Syst. Man Cybern, Syst.

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In this paper, we propose and explore a novel panel data-based particle-filter (PDPF) model to conduct fashion sales forecasting. We evaluate the performance of proposed model by using real data collected from the fashion industry. The experimental results indicate that the proposed panel data models outperform both the traditional statistical and intelligent methods, which provide strong evidence on the importance of employing the panel-data approach. Further analysis reveals that: 1) our proposed PDPF method yields a better forecasting result in item-based sales forecasting than in color-based sales forecasting; 2) a larger degree of Granger causality relationship between sales and price will imply a better sales forecasting result of the PDPF model; 3) increasing the amount of historical data does not necessarily improve forecasting accuracy; and 4) the PDPF method is suitable for conducting fashion sales forecasting with limited data. These findings provide novel insights on the use of panel data for conducting fashion sales forecasting.Index Terms-Fashion sales forecasting, industrial problems, panel data analysis, particle filter.

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Section: Introductionmentioning

confidence: 99%

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Ren

Choi

Liu

2015

IEEE Trans. Syst. Man Cybern, Syst.

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“…In the first case, he shows that these imply that, if marginal utility functions are decreasing, then demand for every good is a decreasing function of the price of the same good and an increasing function of the price of the other goods, which means that, to use today's language, all goods are gross substitutes and there are no Giffen goods. The derivatives for the general case (like the more elaborate ones listed in the appendix of the Manuel) are so similar to those developed by Slutsky (1915) that there has been significant debate on whether Pareto anticipated Slutsky's analysis (see, among others, Schultz 1935, Dooley 1983.…”

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confidence: 52%

Book reviews

Dobusch

2008

The European Journal of the History of Economic Thought

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“…It becomes an integral part of mainstream economics and consumer theory. The papers by Allen (1936 and1950), Hicks and Allen (1934), Schultz (1935), Dooley (1983) and Epps (1975) propagated Slutsky's classic work. Another milestone in consumer theory is Roy's identity (1947) which provides an often invoked mathematical result in consumer theory.…”

Section: Introductionmentioning

confidence: 98%

Random horizon stochastic dynamic Slutsky equation under preference uncertainty

Yeung¹

2014

ams

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This paper extends Slutsky's classic work on consumer theory to a random horizon stochastic dynamic framework in which the consumer has an inter-temporal planning horizon with uncertainties in future incomes, preferences and lifespan. Utility maximization leading to a set of ordinary wealth-dependent demand functions is performed. A dual problem is set up to derive the wealth compensated demand functions. This represents the first time that wealth-dependent ordinary demand functions and wealth compensated demand functions are obtained under these uncertainties. The corresponding Roy's identity relationships and Slutsky equations in a random horizon stochastic dynamic framework with uncertain preferences are derived. The analysis incorporates realistic characteristics in consumer theory and advances the conventional microeconomic study on consumption to a more realistic optimal control framework.

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Interrelations of Demand, Price, and Income

Cited by 35 publications

References 0 publications

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model

Book reviews

Random horizon stochastic dynamic Slutsky equation under preference uncertainty

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