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.
Prior Selection for Vector Autoregressions
2014374 citationsDomenico Giannone, Michèle Lenza et al.The Review of Economics and Statisticsprofile →
How to estimate a vector autoregression after March 2020
2022112 citationsMichèle Lenza, Giorgio E. PrimiceriJournal of Applied Econometricsprofile →
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 Michèle Lenza'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 Michèle Lenza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michèle Lenza more than expected).
This network shows the impact of papers produced by Michèle Lenza. 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 Michèle Lenza. The network helps show where Michèle Lenza may publish in the future.
Co-authorship network of co-authors of Michèle Lenza
This figure shows the co-authorship network connecting the top 25 collaborators of Michèle Lenza.
A scholar is included among the top collaborators of Michèle Lenza 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 Michèle Lenza. Michèle Lenza is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Haghighi, Manouchehr, Andreas Joseph, George Kapetanios, et al.. (2025). Machine Learning for Economic Policy. Journal of Econometrics. 249. 105970–105970.1 indexed citations
Lenza, Michèle & Giorgio E. Primiceri. (2022). How to estimate a vector autoregression after March 2020. Journal of Applied Econometrics. 37(4). 688–699.112 indexed citations breakdown →
Negro, Marco Del, Michèle Lenza, Giorgio E. Primiceri, & Andrea Tambalotti. (2020). Why has inflation in the United States been so stable since the 1990s. ETS Research Bulletin Series. 74(74).
Slačálek, Jiří & Michèle Lenza. (2019). Quantitative easing did not increase inequality in the euro area. ETS Research Bulletin Series. 54(54).3 indexed citations
Jarociński, Marek & Michèle Lenza. (2016). How large is the output gap in the euro area. ETS Research Bulletin Series. 24.1 indexed citations
13.
Giannone, Domenico, Michèle Lenza, & Giorgio E. Primiceri. (2014). Prior Selection for Vector Autoregressions. The Review of Economics and Statistics. 97(2). 436–451.374 indexed citations breakdown →
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.