a proxy of housing demand, a key yet so far mostly unobserved feature of housing markets. We analyze data from an … affects time on market, liquidity and prices in the same way as actual demand, we deduce that it is a good proxy. We then turn … to a standard econometric problem: what difference in demand is caused by a difference in price? We use machine learning …

fraction of demand that can be satisfied from stock.Best-known are the approximations derived from renewal theory by Tijms … specifically, the cases of normally and gamma distributed demand.Here, an exact expression for the fill rate is derived, holding … generally in the situation that demand has a gamma distribution with known integer-valued parameters, while lead time is …

demand distribution parameters. It provides and compares several methods that use the sequence of past prices and observed …

A major obstacle to wider adoption of the newsvendor model is the difficulty of obtaining its key input---the demand … of demand quantity from a panel of experts and uses the degree of disagreement between experts as a proxy for demand … based on a tripartite decomposition of the demand vector into total demand, ordered proportions, and ranking. We also …

revenues if capacity is fixed and the demand to come difficult to forecast. We present several revenue management models and … approaches can mitigate the negative impact of demand forecast errors …

We propose a tractable, data-driven demand estimation procedure based on the use maximum entropy (ME) distributions … the underlying demand distribution is discrete, we show that the sequence of protection levels converges to the optimal … one almost surely, and that the ME demand forecast converges to the true demand distribution for all values below the …

In this paper, we compare two fundamentally different judgmental demand forecasting approaches used to estimate demand … and their corresponding demand distributions. In the first approach, parameters are obtained from a linear regression and … approach ignores dispersion and instead estimates the demand distribution based on the mean demand forecast and the historic …

capture the actual demand as excess demand is usually unobservable. Meanwhile, demand-related features are available in the … demand to be any function of the features. To solve the problem, we develop a neural network-based non-parametric online … compared to existing methods. The advantage is more significant with more complex demand functions …

growth. In seeking to minimize supply-demand mismatch costs and satisfy borrowing constraints, management adopted a …’s systematic bias across selling seasons, and on two alternative product segmentation approaches. Demand distributions forecast …