Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Has Monetary Policy Become More Effective?
2006517 citationsJean Boivin, Marc Giannoniprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Marc Giannoni's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Marc Giannoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc Giannoni more than expected).
This network shows the impact of papers produced by Marc Giannoni. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Marc Giannoni. The network helps show where Marc Giannoni may publish in the future.
Co-authorship network of co-authors of Marc Giannoni
This figure shows the co-authorship network connecting the top 25 collaborators of Marc Giannoni.
A scholar is included among the top collaborators of Marc Giannoni based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Marc Giannoni. Marc Giannoni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Negro, Marco Del, Marc Giannoni, & Christina Patterson. (2023). The Forward Guidance Puzzle. RePEc: Research Papers in Economics. 1(1). 43–79.25 indexed citations
4.
Giannoni, Marc. (2019). Comment on "Optimal Inflation and the Identification of the Phillips Curve". NBER Chapters. 256–266.1 indexed citations
5.
Crump, Richard K., Stefano Eusepi, Marc Giannoni, & Ayşegül Şahin. (2019). A Unified Approach to Measuring u*. SSRN Electronic Journal.1 indexed citations
6.
Negro, Marco Del, Domenico Giannone, Marc Giannoni, & Andrea Tambalotti. (2019). Global trends in interest rates. Journal of International Economics. 118. 248–262.114 indexed citations
7.
Negro, Marco Del, et al.. (2019). DSGE forecasts of the lost recovery. International Journal of Forecasting. 35(4). 1770–1789.14 indexed citations
8.
Negro, Marco Del, Domenico Giannone, Marc Giannoni, & Andrea Tambalotti. (2018). Global Trends in Interest Rates. SSRN Electronic Journal.3 indexed citations
9.
Negro, Marco Del, Domenico Giannone, Marc Giannoni, & Andrea Tambalotti. (2017). Safety, Liquidity, and the Natural Rate of Interest. RePEc: Research Papers in Economics.
10.
Negro, Marco Del, et al.. (2015). Why Are Interest Rates So Low. Liberty Street Economics.13 indexed citations
11.
Negro, Marco Del, et al.. (2014). The FRBNY DSGE Model Forecast. Liberty Street Economics.1 indexed citations
Giannoni, Marc & Michael Woodford. (2003). Optimal Interest-Rate Rules: I. General Theory. RePEc: Research Papers in Economics.90 indexed citations
18.
Giannoni, Marc & Michael Woodford. (2003). Notes on Model for "Optimal Inflation Targeting Rules".2 indexed citations
19.
Boivin, Jean & Marc Giannoni. (2002). Assessing Changes in the Monetary Transmission Mechanism: A VAR Approach. Federal Reserve Bank of New York Economic policy review. 8(1). 97–111.97 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.