World's Best Scientists 2026 revealed!

D-Index & Metrics

Economics and Finance

D-Index
56
Citations
12269
World Ranking
1014
National Ranking
110

Oliver Linton publication distribution in Economics and Finance in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Economics and Finance in 2026. The highlighted bar marks where Oliver Linton sits on this spectrum.

42–46 publications: 1 scientists 47–51 publications: 12 scientists 52–56 publications: 7 scientists 57–61 publications: 23 scientists 62–66 publications: 32 scientists 67–71 publications: 36 scientists 72–76 publications: 56 scientists 77–81 publications: 57 scientists 82–86 publications: 70 scientists 87–91 publications: 86 scientists 92–96 publications: 72 scientists 97–101 publications: 106 scientists 102–106 publications: 97 scientists 107–111 publications: 96 scientists 112–116 publications: 101 scientists 117–121 publications: 103 scientists 122–126 publications: 102 scientists 127–131 publications: 108 scientists 132–136 publications: 107 scientists 137–141 publications: 111 scientists 142–146 publications: 94 scientists 147–151 publications: 96 scientists 152–156 publications: 100 scientists 157–161 publications: 74 scientists 162–166 publications: 68 scientists 167–171 publications: 91 scientists 172–176 publications: 76 scientists 177–181 publications: 75 scientists 182–186 publications: 61 scientists 187–191 publications: 66 scientists 192–196 publications: 68 scientists 197–201 publications: 71 scientists 202–206 publications: 60 scientists 207–211 publications: 56 scientists 212–216 publications: 44 scientists 217–221 publications: 51 scientists 222–226 publications: 65 scientists 227–231 publications: 48 scientists 232–236 publications: 59 scientists 237–241 publications: 37 scientists 242–246 publications: 37 scientists 247–251 publications: 41 scientists 252–256 publications: 43 scientists 257–261 publications: 33 scientists 262–266 publications: 34 scientists 267–271 publications: 39 scientists 272–276 publications: 25 scientists 277–281 publications: 29 scientists 282–286 publications: 29 scientists 287–291 publications: 26 scientists 292–296 publications: 31 scientists 297–301 publications: 31 scientists 302–306 publications: 24 scientists 307–311 publications: 28 scientists 312–316 publications: 24 scientists 317–321 publications: 13 scientists 322–326 publications: 14 scientists 327–331 publications: 25 scientists 332–336 publications: 18 scientists 337–341 publications: 16 scientists 342–346 publications: 18 scientists 347–351 publications: 15 scientists 352–356 publications: 17 scientists 357–361 publications: 19 scientists 362–366 publications: 16 scientists 367–371 publications: 17 scientists 372–376 publications: 7 scientists 377–381 publications: 18 scientists 382–386 publications: 13 scientists 387–391 publications: 19 scientists 392–396 publications: 9 scientists 397–401 publications: 10 scientists 402–406 publications: 13 scientists 407–411 publications: 13 scientists 412–416 publications: 5 scientists 417–421 publications: 10 scientists 422–426 publications: 6 scientists 427–431 publications: 12 scientists 432–436 publications: 9 scientists 437–441 publications: 9 scientists 442–446 publications: 10 scientists 447–451 publications: 12 scientists 452–456 publications: 8 scientists 457–461 publications: 4 scientists 462–466 publications: 7 scientists 467–471 publications: 4 scientists 472–476 publications: 5 scientists 477–481 publications: 6 scientists 482–486 publications: 7 scientists 487–491 publications: 8 scientists 492–496 publications: 5 scientists 497–501 publications: 4 scientists 502–506 publications: 4 scientists 507–511 publications: 5 scientists 512–516 publications: 7 scientists 517–520 publications: 7 scientists 521+ publications: 99 scientists
42 publications 521+

This scientist: 383 publications — 91st percentile

91% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 521 publications or more.

Oliver Linton D-index placement in Economics and Finance in 2026

The chart shows the D-index (discipline H-index) distribution of Economics and Finance scientists ranked by Research.com in 2026. The highlighted bar marks where Oliver Linton sits on this spectrum.

30 D-Index: 126 scientists 31 D-Index: 147 scientists 32 D-Index: 157 scientists 33 D-Index: 178 scientists 34 D-Index: 171 scientists 35 D-Index: 141 scientists 36 D-Index: 132 scientists 37 D-Index: 129 scientists 38 D-Index: 130 scientists 39 D-Index: 120 scientists 40 D-Index: 117 scientists 41 D-Index: 113 scientists 42 D-Index: 114 scientists 43 D-Index: 97 scientists 44 D-Index: 115 scientists 45 D-Index: 86 scientists 46 D-Index: 78 scientists 47 D-Index: 83 scientists 48 D-Index: 62 scientists 49 D-Index: 74 scientists 50 D-Index: 50 scientists 51 D-Index: 66 scientists 52 D-Index: 69 scientists 53 D-Index: 55 scientists 54 D-Index: 67 scientists 55 D-Index: 61 scientists 56 D-Index: 62 scientists 57 D-Index: 45 scientists 58 D-Index: 34 scientists 59 D-Index: 47 scientists 60 D-Index: 46 scientists 61 D-Index: 36 scientists 62 D-Index: 37 scientists 63 D-Index: 56 scientists 64 D-Index: 50 scientists 65 D-Index: 32 scientists 66 D-Index: 28 scientists 67 D-Index: 30 scientists 68 D-Index: 24 scientists 69 D-Index: 25 scientists 70 D-Index: 22 scientists 71 D-Index: 21 scientists 72 D-Index: 26 scientists 73 D-Index: 24 scientists 74 D-Index: 23 scientists 75 D-Index: 12 scientists 76 D-Index: 14 scientists 77 D-Index: 15 scientists 78 D-Index: 15 scientists 79 D-Index: 17 scientists 80 D-Index: 17 scientists 81 D-Index: 11 scientists 82 D-Index: 10 scientists 83 D-Index: 16 scientists 84 D-Index: 11 scientists 85 D-Index: 5 scientists 86 D-Index: 9 scientists 87 D-Index: 13 scientists 88 D-Index: 9 scientists 89 D-Index: 7 scientists 90 D-Index: 6 scientists 91 D-Index: 4 scientists 92 D-Index: 8 scientists 93 D-Index: 10 scientists 94 D-Index: 11 scientists 95 D-Index: 6 scientists 96 D-Index: 4 scientists 97 D-Index: 4 scientists 98 D-Index: 6 scientists 99 D-Index: 6 scientists 100 D-Index: 8 scientists 101+ D-Index: 100 scientists
30 D-Index 101+

This scientist: 56 D-Index — 74th percentile

74% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 101 D-Index or more.

Overview

Oliver Linton is affiliated with the University of Cambridge in the United Kingdom. Their research spans multiple areas within economics, econometrics, and finance, with a significant focus on quantitative methods and applications related to financial markets and economic systems.

The scientist's recent papers include:

  • A ReMeDI for Microstructure Noise, 2022, Econometrica
  • When will the Covid-19 pandemic peak?, 2020, Journal of Econometrics
  • Estimation and inference in semiparametric quantile factor models, 2020, Journal of Econometrics
  • Multiscale clustering of nonparametric regression curves, 2020, Journal of Econometrics
  • A weighted sieve estimator for nonparametric time series models with nonstationary variables, 2020, Journal of Econometrics

Frequent coauthors with whom Oliver Linton has collaborated are:

  • Jiti Gao
  • Degui Li
  • Chaohua Dong
  • Jiajing Sun
  • Shaoran Li

Oliver Linton regularly publishes in the following venues:

  • Journal of Econometrics
  • arXiv (Cornell University)
  • Journal of the Royal Statistical Society Series A (Statistics in Society)
  • Economics Letters
  • Econometric Reviews

The primary fields of study for the scientist include:

  • Economics, Econometrics and Finance
  • Mathematics

Their subfields of study focus on:

  • Finance
  • Economics and Econometrics
  • Statistics and Probability
  • General Economics, Econometrics and Finance
  • Artificial Intelligence

Main topics of Oliver Linton's work cover:

  • Financial Risk and Volatility Modeling
  • Statistical Methods and Inference
  • Monetary Policy and Economic Impact
  • Complex Systems and Time Series Analysis
  • Financial Markets and Investment Strategies
  • Stochastic processes and financial applications
  • Advanced Causal Inference Techniques

Best Publications

  • Consistent Testing for Stochastic Dominance under General Sampling Schemes

    Oliver Linton;Esfandiar Maasoumi;Yoon-Jae Whang

  • A kernel method of estimating structured nonparametric regression based on marginal integration

    Oliver Linton;Jens Perch Nielsen

  • Estimation of semiparametric models when the criterion function is not smooth

    Xiaohong Chen;Oliver Linton;Ingrid Van Keilegom

  • The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series

    Heejoon Han;Oliver Linton;Tatsushi Oka;Yoon-Jae Whang

  • The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions

    Enno Mammen;Oliver Linton;J Nielsen

  • APPLIED NONPARAMETRIC METHODS

    Wolfgang H;Humboldt-Universitiit Berlin;Oliver Linton

  • Quantile autoregression. Commentary

    Roger Koenker;Zhijie Xiao;Jianqing Fan;Yingying Fan

  • Semiparametric Regression Analysis With Missing Response at Random

    Qihua Wang;Oliver Linton;Wolfgang Härdle

  • Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems

    Steve Berry;Oliver B. Linton;Ariel Pakes

  • Evaluating Value-at-Risk Models via Quantile Regression

    Wagner Piazza Gaglianone;Luiz Renato Lima;Oliver Linton;Daniel R. Smith;Daniel R. Smith

  • Adaptive Estimation in ARCH Models

    Oliver B. Linton

  • The quantilogram: With an application to evaluating directional predictability

    O. Linton;Yoon-Jae Whang

  • Estimation of additive regression models with known links

    Oliver Linton;W Hardle

  • An improved bootstrap test of stochastic dominance

    Oliver Linton;Kyungchul Song;Yoon-Jae Whang

  • EFFICIENT SEMIPARAMETRIC ESTIMATION OF THE FAMA-FRENCH MODEL AND EXTENSIONS

    Gregory Connor;Matthias Hagmann;Oliver Linton

  • Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos

    Mototsugu Shintani;Oliver Linton

  • A smoothed least squares estimator for threshold regression models

    Myung Hwan Seo;Oliver Linton

  • Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods

    Oliver Linton;Enno Mammen

  • SECOND ORDER APPROXIMATION IN THE PARTIALLY LINEAR REGRESSION MODEL

    Oliver Linton

  • UNIFORM BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATES OF M-REGRESSION AND ITS APPLICATION TO THE ADDITIVE MODEL

    Efang Kong;Oliver Linton;Yingcun Xia

  • Local Regression Models

    Oliver B. Linton

Frequent Co-Authors

Enno Mammen
Enno Mammen Heidelberg University
Arthur Lewbel
Arthur Lewbel Boston College
Christian M. Hafner
Christian M. Hafner Université Catholique de Louvain
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Jiti Gao
Jiti Gao Monash University
Yingcun Xia
Yingcun Xia National University of Singapore
Peter M. Robinson
Peter M. Robinson London School of Economics and Political Science
Raymond J. Carroll
Raymond J. Carroll Texas A&M University
Esfandiar Maasoumi
Esfandiar Maasoumi Emory University

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