Uncertain Demand, Consumer Loss Aversion, and Flat-Rate Tariffs

The so called flat-rate bias is a well documented phenomenon caused by consumers' desire to be insured against fluctuations in their billing amounts. This paper shows that expectation-based loss aversion provides a formal explanation for this bias. We solve for the optimal two-part tariff when contracting with loss-averse consumers who are uncertain about their demand. The optimal tariff is a flat rate if marginal cost of production is low compared to a consumer's degree of loss aversion and if there is enough variation in the consumer's demand. Moreover, if consumers differ with respect to the degree of loss aversion, firms' optimal menu of tariffs typically comprises a flat-rate contract

Inflation, Prices, and Information in Competitive Search

We study the effects of inflation in a competitive search model where each buyer’s utility is private information, and money is essential. The equilibrium is efficient at the Friedman rule, but inflation creates an inefficiency in the terms of trade. Buyers experience a preference shock after they are matched with a seller, and thus they have a precautionary motive for holding money. Sellers, who compete to attract buyers, post non-linear price schedules. As inflation rises, sellers post relatively flat price schedules, which reduce the need for precautionary balances. These price schedules induce buyers with a low desire to consume to purchase inefficiently high quantities because of the low marginal cost of purchasing goods. In contrast, buyers with a high desire to consume purchase inefficiently low quantities as they face binding liquidity constraints. The model fits historical US data on velocity and interest rates.Publicad

A Better Alternative to Piecewise Linear Time Series Segmentation

Time series are difficult to monitor, summarize and predict. Segmentation organizes time series into few intervals having uniform characteristics (flatness, linearity, modality, monotonicity and so on). For scalability, we require fast linear time algorithms. The popular piecewise linear model can determine where the data goes up or down and at what rate. Unfortunately, when the data does not follow a linear model, the computation of the local slope creates overfitting. We propose an adaptive time series model where the polynomial degree of each interval vary (constant, linear and so on). Given a number of regressors, the cost of each interval is its polynomial degree: constant intervals cost 1 regressor, linear intervals cost 2 regressors, and so on. Our goal is to minimize the Euclidean (l_2) error for a given model complexity. Experimentally, we investigate the model where intervals can be either constant or linear. Over synthetic random walks, historical stock market prices, and electrocardiograms, the adaptive model provides a more accurate segmentation than the piecewise linear model without increasing the cross-validation error or the running time, while providing a richer vocabulary to applications. Implementation issues, such as numerical stability and real-world performance, are discussed.Comment: to appear in SIAM Data Mining 200

A cosmological concordance model with dynamical vacuum term

We demonstrate that creation of dark-matter particles at a constant rate implies the existence of a cosmological term that decays linearly with the Hubble rate. We discuss the cosmological model that arises in this context and test it against observations of the first acoustic peak in the cosmic microwave background (CMB) anisotropy spectrum, the Hubble diagram for supernovas of type Ia (SNIa), the distance scale of baryonic acoustic oscillations (BAO) and the distribution of large scale structures (LSS). We show that a good concordance is obtained, albeit with a higher value of the present matter abundance than in the \Lambda CDM model. We also comment on general features of the CMB anisotropy spectrum and on the cosmic coincidence problem.Comment: Revised version. Accepted for publication in Physics Letters