Automated Author ProfileWilliams, Noah
Williams, Noah
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 3.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We use a Bayesian Markov Chain Monte Carlo algorithm to estimate the parameters of a true data-generating mechanism and those of a sequence of approximating models that a monetary authority uses to guide its decisions. Gaps between a true expectational Phillips curve and the monetary authoritys approximating nonexpectational Phillips curve models unleash inflation that a monetary authority that knows the true model would avoid. A sequence of dynamic programming problems implies that the monetary authoritys inflation target evolves as its estimated Phillips curve moves. Our estimates attribute the rise and fall of post- WWII inflation in the United States to an intricate interaction between the monetary authoritys beliefs and economic shocks. Shocks in the 1970s made the monetary authority perceive a tradeoff between inflation and unemployment which ignited big inflation. The monetary authoritys beliefs about the Phillips curve changed in ways that account for former Federal Reserve Chairman Paul Volckers conquest of U.S. inflation. (JEL E24, E31, E52, N12)
Authors
- Sargent, Thomas ;
- Williams, Noah ;
- Zha, Tao
We use a Bayesian Markov Chain Monte Carlo algorithm to estimate the parameters of a true data-generating mechanism and those of a sequence of approximating models that a monetary authority uses to guide its decisions. Gaps between a true expectational Phillips curve and the monetary authoritys approximating nonexpectational Phillips curve models unleash inflation that a monetary authority that knows the true model would avoid. A sequence of dynamic programming problems implies that the monetary authoritys inflation target evolves as its estimated Phillips curve moves. Our estimates attribute the rise and fall of post- WWII inflation in the United States to an intricate interaction between the monetary authoritys beliefs and economic shocks. Shocks in the 1970s made the monetary authority perceive a tradeoff between inflation and unemployment which ignited big inflation. The monetary authoritys beliefs about the Phillips curve changed in ways that account for former Federal Reserve Chairman Paul Volckers conquest of U.S. inflation. (JEL E24, E31, E52, N12)
Authors
- Sargent, Thomas ;
- Williams, Noah ;
- Zha, Tao