The Time Varying Volatility of Macroeconomic Fluctuations

In this paper we investigate the sources of the important shifts in the volatility of U.S. macroeconomic variables in the postwar period. To this end, we propose the estimation of DSGE models allowing for time variation in the volatility of the structural innovations. We apply our estimation strategy to a large-scale model of the business cycle and and that investment specific technology shocks account for most of the sharp decline in volatility of the last two decadesGreat Moderation, Stochastic Volatility, Investment Specific Technology Shock, Relative Price of Investment, DSGE Models

October 26, 1946 Football Program, UOP vs. Northwestern

https://scholarlycommons.pacific.edu/ua-football/1223/thumbnail.jp

Informational Hold-Up, Disclosure Policy, and Career Concerns on theExample of Open Source Software Development

We consider software developers who can either work on an open source project or on a closed source project. The former provides a publicly available signal about their talent, whereas the latter provides a signal only observed by their employer. We show that a talented employee may initially prefer a less paying job as an open source developer to commercial closed source projects, because a publicly available signal gives him a better bargaining position when renegotiating wages with his employer after the signal has been revealed. Also, we derive conditions under which two effects suggested by standard intuition are reversed: a 'pooling equilibrium' (with both talented and untalented workers doing closed source) is less likely if differences in talent are large; a highly visible open source job leads to more effort in a career concerns setup. The former effect is because a higher productivity of talented workers raises not only the value but also the cost of signaling; the latter stems from more effort and the choice of a high visibility job being substitutes for the purpose of signaling. Results naturally apply to other industries with high and low visibility jobs, e.g. academic rather than commercial research, consulting rather than management

Genome-Wide Mapping of Human DNA Replication by Optical Replication Mapping Supports a Stochastic Model of Eukaryotic Replication Timing [preprint]

DNA replication is regulated by the location and timing of replication initiation. Therefore, much effort has been invested in identifying and analyzing the sites of human replication initiation. However, the heterogeneous nature of eukaryotic replication kinetics and the low efficiency of individual initiation site utilization in metazoans has made mapping the location and timing of replication initiation in human cells difficult. A potential solution to the problem of human replication mapping is single-molecule analysis. However, current approaches do not provide the throughput required for genome-wide experiments. To address this challenge, we have developed Optical Replication Mapping (ORM), a high-throughput single-molecule approach to map newly replicated DNA, and used it to map early initiation events in human cells. The single-molecule nature of our data, and a total of more than 2000-fold coverage of the human genome on 27 million fibers averaging ~300 kb in length, allow us to identify initiation sites and their firing probability with high confidence. In particular, for the first time, we are able to measure genome-wide the absolute efficiency of human replication initiation. We find that the distribution of human replication initiation is consistent with inefficient, stochastic initiation of heterogeneously distributed potential initiation complexes enriched in accessible chromatin. In particular, we find sites of human replication initiation are not confined to well-defined replication origins but are instead distributed across broad initiation zones consisting of many initiation sites. Furthermore, we find no correlation of initiation events between neighboring initiation zones. Although most early initiation events occur in early-replicating regions of the genome, a significant number occur in late-replicating regions. The fact that initiation sites in typically late-replicating regions have some probability of firing in early S phase suggests that the major difference between initiation events in early and late replicating regions is their intrinsic probability of firing, as opposed to a qualitative difference in their firing-time distributions. Moreover, modeling of replication kinetics demonstrates that measuring the efficiency of initiation-zone firing in early S phase suffices to predict the average firing time of such initiation zones throughout S phase, further suggesting that the differences between the firing times of early and late initiation zones are quantitative, rather than qualitative. These observations are consistent with stochastic models of initiation-timing regulation and suggest that stochastic regulation of replication kinetics is a fundamental feature of eukaryotic replication, conserved from yeast to humans