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Economics and Finance

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

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

Arthur Lewbel
Arthur Lewbel Boston College
Jiti Gao
Jiti Gao Monash University
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Peter M. Robinson
Peter M. Robinson London School of Economics and Political Science
Esfandiar Maasoumi
Esfandiar Maasoumi Emory University
Ariel Pakes
Ariel Pakes Harvard University
Daniel McFadden
Daniel McFadden University of California, Berkeley
Thierry Post
Thierry Post Nazarbayev University
Xihong Lin
Xihong Lin Harvard University
Michael F. Ashby
Michael F. Ashby University of Cambridge

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