I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the “see-saw” or “waterbed” effect...

I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the “see-saw” or “waterbed” effect...

I introduce a reduced form two-sided market model to study prediction and identification in two-sided markets. The model generates the hallmark features of two-sided markets: potentially below cost or even negative prices to one side of the market, and the "see-saw" or "waterbed" effect of a...

Accurate forecasts of incoming calls are crucial to optimal staffing decisions in call centers. This paper evaluates a wide range of models and forecast combination techniques by means of statistical and economic criteria. Relative to the previous literature, this paper is novel in several...

We set out in this study to review a vast amount of recent literature on machine learning (ML) approaches to predicting financial distress (FD), including supervised, unsupervised and hybrid supervised-unsupervised learning algorithms. Four supervised ML models including the traditional support...