Automated Author ProfileRamey, Valerie
University of California-San Diego
Ramey, Valerie
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: 1.6 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We analyze whether government spending multipliers differ by the sign of the shock. Using aggregate historical U.S. data, we apply Ben Zeev’s (2020) nonlinear diagnostic tests and find evidence of nonlinearities in the impulse response functions of both government spending and GDP. We then extend Ramey and Zubairy’s (2018) framework to allow for asymmetric effects as a type of state dependence to estimate multipliers. While we find differences in the impulse response functions, the resulting multipliers do not differ by sign of the shock. Thus, we find no evidence of asymmetry of government spending multipliers.
Authors
- Ben Zeev, Nadav ;
- Ramey, Valerie ;
- Zubairy, Sarah
We analyze whether government spending multipliers differ by the sign of the shock. Using aggregate historical U.S. data, we apply Ben Zeev’s (2020) nonlinear diagnostic tests and find evidence of nonlinearities in the impulse response functions of both government spending and GDP. We then extend Ramey and Zubairy’s (2018) framework to allow for asymmetric effects as a type of state dependence to estimate multipliers. While we find differences in the impulse response functions, the resulting multipliers do not differ by sign of the shock. Thus, we find no evidence of asymmetry of government spending multipliers.
Authors
- Ben Zeev, Nadav ;
- Ramey, Valerie ;
- Zubairy, Sarah
We analyze whether government spending multipliers differ by the sign of the shock. Using aggregate historical U.S. data, we apply Ben Zeev’s (2020) nonlinear diagnostic tests and find evidence of nonlinearities in the impulse response functions of both government spending and GDP. We then extend Ramey and Zubairy’s (2018) framework to allow for asymmetric effects as a type of state dependence to estimate multipliers. While we find differences in the impulse response functions, the resulting multipliers do not differ by sign of the shock. Thus, we find no evidence of asymmetry of government spending multipliers.
Authors
- Ben Zeev, Nadav ;
- Ramey, Valerie ;
- Zubairy, Sarah