Growth: With or Without Scale Effects?

December 15, 1998 -- Version 1.0 The property that ideas are nonrivalrous leads to a tight link between idea-based growth models and increasing returns to scale. In particular, changes in the size of an economy's population generally affect either the long-run growth rate or the long-run level of income in such models. This paper provides a partial review of the expanding literature on idea-based models and scale effects. It presents simple versions of various recent idea-based growth models and analyzes their implications for the relationship between scale and growth. Prepared for the AEA Meetings, January 3, 1999. Forthcoming in the AER Papers and Proceedings, May 1999.

Economic outlook

Presented by Charles I. Plosser, President and Chief Executive Officer, Federal Reserve Bank of Philadelphia, Business Leaders Forum, Villanova School of Business, September 29, 2011>Monetary policy ; Inflation (Finance) ; Consumers

Why Have Health Expenditures as a Share fo GDP Risen So Much?

Aggregate health expenditures as a share of GDP have risen in the United States from about 5 percent in 1960 to nearly 14 percent in recent years. Why? This paper explores a simple explanation based on technological progress. Medical advances allow diseases to be cured today, at a cost, that could not be cured at any price in the past. When this technological progress is combined with a Medicare- like transfer program to pay the health expenses of the elderly, the model is able to reproduce the basic facts of recent U.S. experience, including the large increase in the health expenditure share, a rise in life expectancy, and an increase in the size of health-related transfer payments as a share of GDP.

Bayesian inference for queueing networks and modeling of internet services

Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where each queue models one of the computers in the system. A key challenge is that the data are incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo. We evaluate our framework on data from a benchmark Web application. We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inference and Learning in Networks of Queues

Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. We demonstrate the effectiveness of our sampler on real-world data from a benchmark Web application.

Commentary on "Economic projections and rules of thumb for monetary policy"

Monetary policy

Probabilistic Inference in Queueing Networks

Although queueing models have long been used to model the performance of computer systems, they are out of favor with practitioners, because they have a reputation for requiring unrealistic distributional assumptions. In fact, these distributional assumptions are used mainly to facilitate analytic approximations such as asymptotics and large-deviations bounds. In this paper, we analyze queueing networks from the probabilistic modeling perspective, applying inference methods from graphical models that afford significantly more modeling flexibility. In particular, we present a Gibbs sampler and stochastic EM algorithm for networks of M/M/1 FIFO queues. As an application of this technique, we localize performance problems in distributed systems from incomplete system trace data. On both synthetic networks and an actual distributed Web application, the model accurately recovers the system’s service time using 1 % of the available trace data.

Visual Molecular Dynamics Investigations of the Impact of Hydrophobic Nanoparticles on Prognosis of Alzheimer’s Disease and Cancers

The possible impact of hydrophobic lectin nanoparticles on the prognosis and progression of Alzheimer's disease (AD) and cancers was investigated by Visual Molecular Dynamics (VMD) computer modeling programs available from the Beckmann Advanced Research Institute at the University of Illinois at Urbana. Our results indicate the possibility of impeding pathological aggregation of certain proteins such as modified tau- or beta-amyloid that are currently being considered as possible causes of Alzheimer's disease. VMD programs serve as useful tools for investigation hydrophobic protein aggregation that may play a role in aging of human populations