SCREENING FOR A CHRONIC DISEASE: A MULTIPLE STAGE DURATION MODEL WITH PARTIAL OBSERVABILITY

We estimate a dynamic multistage duration model to investigate how early detection of diabetes can delay the onset of lower extremity complications and death. We allow for partial observability of the disease stage, unmeasured heterogeneity, and endogenous timing of diabetes screening. Timely diagnosis appears important. We evaluate the effectiveness of two potential policies to reduce the monetary costs of frequent screening in terms of lost longevity. Compared to the status quo, the more restrictive policy yields an implicit value for an additional year of life of about $50,000, whereas the less restrictive policy implies a value of about $120,000.     

The fragmentation of views in a democracy

Economics of Governance - Are voters in democracies more competent if there are more media outlets? To answer this question, I provide a game-theoretic model of media capture and political...     

Optimal order placement in limit order markets

To execute a trade, participants in electronic equity markets may choose to submit limit orders or market orders across various exchanges where a stock is traded. This decision is influenced by characteristics of the order flows and queue sizes in each limit order book, as well as the structure of transaction fees and rebates across exchanges. We propose a quantitative framework for studying this order placement problem by formulating it as a convex optimization problem. This formulation allows the study of how the optimal order placement decision depends on the interplay between the state of order books, the fee structure, order flow properties and the aversion to execution risk. In the case of a single exchange, we derive an explicit solution for the optimal split between limit and market orders. For the general case of order placement across multiple exchanges, we propose a stochastic algorithm that computes the optimal routing policy and study the sensitivity of the solution to various parameters. Our algorithm does not require an explicit statistical model of order flow but exploits data on recent order fills across exchanges in the numerical implementation of the algorithm to acquire this information through a supervised learning procedure.