Michal Kolesár

1.2k total citations · 1 hit paper
28 papers, 417 citations indexed

About

Michal Kolesár is a scholar working on Statistics and Probability, Economics and Econometrics and General Economics, Econometrics and Finance. According to data from OpenAlex, Michal Kolesár has authored 28 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics and Probability, 7 papers in Economics and Econometrics and 3 papers in General Economics, Econometrics and Finance. Recurrent topics in Michal Kolesár's work include Statistical Methods and Inference (17 papers), Advanced Causal Inference Techniques (11 papers) and Advanced Statistical Methods and Models (10 papers). Michal Kolesár is often cited by papers focused on Statistical Methods and Inference (17 papers), Advanced Causal Inference Techniques (11 papers) and Advanced Statistical Methods and Models (10 papers). Michal Kolesár collaborates with scholars based in United States and Germany. Michal Kolesár's co-authors include Guido W. Imbens, John N. Friedman, Edward L. Glaeser, Timothy B. Armstrong, Raj Chetty, Joshua D. Angrist, Rodrigo Adão, Eduardo Morales, Timothy R. Armstrong and Peter Hull and has published in prestigious journals such as American Economic Review, Econometrica and The Review of Economics and Statistics.

In The Last Decade

Michal Kolesár

26 papers receiving 397 citations

Hit Papers

Robust Standard Errors in Small Samples: Some Practical A... 2016 2026 2019 2022 2016 50 100 150
What are hit papers?

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.

  1. Robust Standard Errors in Small Samples: Some Practical Advice
    2016 161 citations Guido W. Imbens, Michal Kolesár The Review of Economics and Statistics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michal Kolesár United States 9 156 144 69 61 42 28 417
John Greenlees United States 13 280 1.8× 133 0.9× 84 1.2× 89 1.5× 32 0.8× 36 511
Frank Vella United States 7 189 1.2× 39 0.3× 51 0.7× 33 0.5× 24 0.6× 14 332
Alexander Torgovitsky United States 11 234 1.5× 191 1.3× 53 0.8× 53 0.9× 12 0.3× 29 428
Leandro M. Magnusson Australia 7 123 0.8× 21 0.1× 52 0.8× 84 1.4× 40 1.0× 20 265
Ralf A. Wilke Germany 14 290 1.9× 189 1.3× 61 0.9× 31 0.5× 53 1.3× 60 539
Debopam Bhattacharya United Kingdom 10 195 1.3× 121 0.8× 177 2.6× 30 0.5× 15 0.4× 27 453
Ugo Trivellato Italy 11 235 1.5× 29 0.2× 89 1.3× 58 1.0× 31 0.7× 48 365
Jinook Jeong South Korea 10 136 0.9× 56 0.4× 28 0.4× 79 1.3× 31 0.7× 28 259
Simon Freyaldenhoven United States 4 151 1.0× 28 0.2× 39 0.6× 44 0.7× 34 0.8× 6 276
Nikolai Cook Canada 8 102 0.7× 42 0.3× 35 0.5× 19 0.3× 20 0.5× 17 285

Countries citing papers authored by Michal Kolesár

Since Specialization
Citations

This map shows the geographic impact of Michal Kolesár'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 Michal Kolesár with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michal Kolesár more than expected).

Fields of papers citing papers by Michal Kolesár

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michal Kolesár. 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 Michal Kolesár. The network helps show where Michal Kolesár may publish in the future.

Co-authorship network of co-authors of Michal Kolesár

This figure shows the co-authorship network connecting the top 25 collaborators of Michal Kolesár. A scholar is included among the top collaborators of Michal Kolesár 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 Michal Kolesár. Michal Kolesár 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.
Goldsmith-Pinkham, Paul, Peter Hull, & Michal Kolesár. (2024). Contamination Bias in Linear Regressions. American Economic Review. 114(12). 4015–4051. 8 indexed citations
2.
Angrist, Joshua D. & Michal Kolesár. (2023). One instrument to rule them all: The bias and coverage of just-ID IV. Journal of Econometrics. 240(2). 105398–105398. 18 indexed citations
3.
Dong, Yingying & Michal Kolesár. (2023). When can we ignore measurement error in the running variable?. Journal of Applied Econometrics. 38(5). 735–750. 4 indexed citations
4.
Armstrong, Timothy B. & Michal Kolesár. (2021). Finite‐Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness. Econometrica. 89(3). 1141–1177. 9 indexed citations
5.
Armstrong, Timothy B. & Michal Kolesár. (2019). Sensitivity Analysis using Approximate Moment Condition Models. SSRN Electronic Journal. 8 indexed citations
6.
Armstrong, Timothy B. & Michal Kolesár. (2018). Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness. SSRN Electronic Journal. 3 indexed citations
7.
Kolesár, Michal. (2018). Minimum distance approach to inference with many instruments. Journal of Econometrics. 204(1). 86–100. 12 indexed citations
8.
Armstrong, Timothy R. & Michal Kolesár. (2018). Simple and Honest Confidence Intervals in Nonparametric Regression. SSRN Electronic Journal. 2 indexed citations
9.
Armstrong, Timothy B. & Michal Kolesár. (2018). Sensitivity Analysis using Approximate Moment Condition Models. SSRN Electronic Journal. 2 indexed citations
10.
Armstrong, Timothy B. & Michal Kolesár. (2017). A Simple Adjustment for Bandwidth Snooping. The Review of Economic Studies. 85(2). 732–765. 10 indexed citations
11.
Armstrong, Timothy B. & Michal Kolesár. (2017). Optimal Inference in a Class of Regression Models. SSRN Electronic Journal. 1 indexed citations
12.
Armstrong, Timothy R. & Michal Kolesár. (2016). Simple and Honest Confidence Intervals in Nonparametric Regression. SSRN Electronic Journal. 1 indexed citations
13.
Imbens, Guido W. & Michal Kolesár. (2016). Robust Standard Errors in Small Samples: Some Practical Advice. The Review of Economics and Statistics. 98(4). 701–712. 161 indexed citations breakdown →
14.
Armstrong, Timothy R. & Michal Kolesár. (2016). A Simple Adjustment for Bandwidth Snooping. SSRN Electronic Journal. 3 indexed citations
15.
Armstrong, Timothy R. & Michal Kolesár. (2015). A Simple Adjustment for Bandwidth Snooping. SSRN Electronic Journal. 1 indexed citations
16.
Kolesár, Michal, Raj Chetty, John N. Friedman, Edward L. Glaeser, & Guido W. Imbens. (2014). Identification and Inference With Many Invalid Instruments. Journal of Business and Economic Statistics. 33(4). 474–484. 79 indexed citations
17.
Armstrong, Timothy R. & Michal Kolesár. (2014). A Simple Adjustment for Bandwidth Snooping. SSRN Electronic Journal. 1 indexed citations
18.
Kolesár, Michal. (2013). INTEGRATED LIKELIHOOD APPROACH TO INFERENCE WITH MANY INSTRUMENTS.
19.
Imbens, Guido W. & Michal Kolesár. (2012). Robust Standard Errors in Small Samples: Some Practical Advice. RePEc: Research Papers in Economics. 6 indexed citations
20.
Kolesár, Michal, Raj Chetty, John N. Friedman, et al.. (2011). Identification and Inference with Many Invalid Instruments. RePEc: Research Papers in Economics. 6 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.

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