Tucker McElroy

96 papers receiving 676 citations

Peers

Tucker McElroy
Comparison fields: 5 of 81
  • Finance 215
  • General Economics, Econometrics and Finance 153
  • Statistics and Probability 118
  • Management Science and Operations Research 152
  • Applied Mathematics 121
Replace Siegfried Hörmann with:
Siegfried Hörmann United States
Guy Mélard Belgium
Gregory Rice Canada
D. S. Poskitt Australia
Yuzo Hosoya Japan
Neville Davies United Kingdom
Estela Bee Dagum Italy
Wilfredo Palma Chile
Agustı́n Maravall Spain
Jens‐Peter Kreiß Germany
Tucker McElroy relative to Siegfried Hörmann United States Siegfried Hörmann's profile →
Citations per field
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Siegfried Hörmann · 1×
Citations per year

Countries citing papers authored by Tucker McElroy

Since Specialization
Citations

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

Fields of papers citing papers by Tucker McElroy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Tucker McElroy, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tucker McElroy Line = papers co-authored together Tucker McElroy links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 104 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012105
2 201936
3 200833
4 200830
5 201125
6 201722
7 200518
8 201118
9 200217
10 201317
11 201114
12 200813
13 201813
14 201412
15 200612
16 201512
17
Matrix Formulas for Nonstationary Signal Extraction
200512
18 200811
19 201911
20 201410

About Tucker McElroy

Tucker McElroy is a scholar working on Economics and Econometrics, Finance, General Economics, Econometrics and Finance, Management Science and Operations Research and Statistics and Probability, having authored 104 papers that have together received 722 indexed citations. Recurring topics across this work include Financial Risk and Volatility Modeling (30 papers), Complex Systems and Time Series Analysis (30 papers), Monetary Policy and Economic Impact (29 papers), Forecasting Techniques and Applications (23 papers), Market Dynamics and Volatility (20 papers), Statistical and numerical algorithms (18 papers), Statistical Methods and Inference (9 papers) and Advanced Statistical Methods and Models (9 papers). The work is most often cited by research in Finance (215 citations), General Economics, Econometrics and Finance (153 citations), Statistics and Probability (118 citations), Management Science and Operations Research (152 citations) and Applied Mathematics (121 citations). Tucker McElroy has collaborated with scholars based in United States, United Kingdom and Switzerland. Frequent co-authors include Dimitris N. Politis, Scott H. Holan, Peter Maaß, Theodore Alexandrov, Silvia Bianconcini, Estela Bee Dagum, Marc Wildi, Xuguang Simon Sheng, Thomas Trimbur and Scott Baker. Their work appears in journals such as Journal of Time Series Analysis, Journal of Statistical Planning and Inference, Computational Statistics & Data Analysis, Econometric Reviews and Statistica Sinica.

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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