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.
Learning and Expectations in Macroeconomics
20011.3k citationsGeorge W. Evans, Seppo Honkapohjaprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by George W. Evans
Since
Specialization
Citations
This map shows the geographic impact of George W. Evans'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 George W. Evans with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites George W. Evans more than expected).
This network shows the impact of papers produced by George W. Evans. 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 George W. Evans. The network helps show where George W. Evans may publish in the future.
Co-authorship network of co-authors of George W. Evans
This figure shows the co-authorship network connecting the top 25 collaborators of George W. Evans.
A scholar is included among the top collaborators of George W. Evans 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 George W. Evans. George W. Evans is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Evans, George W., Seppo Honkapohja, & Kaushik Mitra. (2020). Expectations, Stagnation and Fiscal Policy: A Nonlinear Analysis. SSRN Electronic Journal.1 indexed citations
4.
Sargent, Thomas J., George W. Evans, Seppo Honkapohja, & Noah Williams. (2012). Bayesian Model Averaging, Learning, and Model Selection. SSRN Electronic Journal.3 indexed citations
Evans, George W. & Seppo Honkapohja. (2008). Robust Learning Stability with Operational Monetary Policy Rules. RePEc: Research Papers in Economics. 13(504). 1–170.9 indexed citations
Evans, George W. & Seppo Honkapohja. (2003). Policy Interaction, Expectation and Liquidity Trap. SSRN Electronic Journal.2 indexed citations
11.
Honkapohja, Seppo & George W. Evans. (1996). Economic Dynamics with Learning: New Stability Results. SSRN Electronic Journal.14 indexed citations
12.
Honkapohja, Seppo & George W. Evans. (1996). Convergence of Learning Algorithms without a Projection Facility. SSRN Electronic Journal.1 indexed citations
Evans, George W. & Seppo Honkapohja. (1993). Adaptive forecasts, hysteresis, and endogenous fluctuations. Econometric Reviews. 3–13.40 indexed citations
15.
Evans, George W. & Garey Ramey. (1992). Expectation Calculation and Macroeconomic Dynamics. American Economic Review. 82(1). 207–224.89 indexed citations
16.
Evans, George W.. (1991). Pitfalls in Testing for Explosive Bubbles in Asset Prices. American Economic Review. 81(4). 922–930.494 indexed citations
17.
Evans, George W.. (1986). A Test for Speculative Bubbles in the Sterling-Dollar Exchange Rate: 1981-84. American Economic Review. 76(4). 621–636.91 indexed citations
18.
Evans, George W.. (1985). The algebra of ARMA processes and the structure of ARMA solutions to a general linear model with rational expectations.1 indexed citations
Evans, George W., et al.. (1962). Bounding Studies of Cementing Compositions to Pipe and Formations.36 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.