World's Best Scientists 2026 revealed!
Dag Tjøstheim

Dag Tjøstheim

Award Badge
Mathematics
Norway
2026

D-Index & Metrics

Mathematics

D-Index
47
Citations
8049
World Ranking
1285
National Ranking
9

Research.com Recognitions

  • 2026 - Research.com Mathematics in Norway Leader Award
  • 2025 - Research.com Mathematics in Norway Leader Award

Overview

Dag Tjøstheim is affiliated with the University of Bergen in Norway. Their research spans several fields of study, including Economics, Econometrics and Finance, Mathematics, and Computer Science.

Their work covers various subfields such as Statistics and Probability, Economics and Econometrics, Finance, Artificial Intelligence, and Analytical Chemistry. The main topics of research include Financial Risk and Volatility Modeling, Complex Systems and Time Series Analysis, Statistical Methods and Inference, Advanced Statistical Methods and Models, Bayesian Methods and Mixture Models, Spectroscopy and Chemometric Analyses, and Market Dynamics and Volatility.

Recent papers authored or coauthored by Dag Tjøstheim include:

  • Statistical Dependence: Beyond Pearson's ρ, 2022, Statistical Science
  • Some notes on nonlinear cointegration: A partial review with some novel perspectives, 2020, Econometric Reviews
  • The Locally Gaussian Partial Correlation, 2021, Journal of Business and Economic Statistics
  • The Measurement Error Of Marine Survey Catches: The Bottom Trawl Case, 2024, AquaDocs (United Nations Educational, Scientific and Cultural Organization)
  • Pairwise local Fisher and naive Bayes: Improving two standard discriminants, 2020, Journal of Econometrics

Frequent co-authors collaborating with Dag Tjøstheim include:

  • Håkon Otneim
  • Bård Støve
  • Martin Jullum
  • Jiti Gao
  • Anders Løland

The scientist's publications often appear in venues such as:

  • RePEc: Research Papers in Economics
  • arXiv (Cornell University)
  • Statistical Science
  • Entropy
  • Journal of Econometrics

Best Publications

  • Modelling nonlinear economic time series

    Timo Teräsvirta;Dag Tjøstheim;Clive W. J. Granger

  • Poisson Autoregression

    Unknown

  • Nonparametric Estimation and Identification of Nonlinear ARCH Time Series Strong Convergence and Asymptotic Normality: Strong Convergence and Asymptotic Normality

    Elias Masry;Dag Tjøstheim

  • A Cautionary Note on the Use of the Kolmogorov–Smirnov Test for Normality

    Dag J. Steinskog;Dag B. Tjøstheim;Nils G. Kvamstø

  • Log-linear Poisson autoregression

    Konstantinos Fokianos;Dag Tjøstheim

  • Nonparametric Identification of Nonlinear Time Series: Projections

    Unknown

  • Nonparametric Estimation in a Nonlinear Cointegration Type Model

    Hans Arnfinn Karlsen;Terje Myklebust;Dag Tjøstheim

  • Nonparametric estimation in null recurrent time series

    Hans Arnfinn Karlsen;Dag Tjøstheim

  • ‘Bias of some commonly-used time series estimates’

    Dag Tjøstheim;Jostein Paulsen

  • Nonparametric Estimation and Testing of Interaction in Additive Models

    Stefan Andréas Sperlich;D. Tjostheim;L. Yang

  • Nonparametric tests of linearity for time series

    Vidar Hjellvik;Dag Tjøstheim

  • Statistical spatial series modelling

    Unknown

  • Estimation in nonlinear time series models

    Unknown

  • Linearity testing using local polynomial approximation

    Vidar Hjellvik;Qiwei Yao;Dag Tjøstheim

  • Nonparametric Identification of Nonlinear Time Series: Selecting Significant Lags

    Dag Tjøstheim;Bjørn H. Auestad

  • Additive Nonlinear ARX Time Series and Projection Estimates

    Elias Masry;Dag Tjøstheim

  • On weak dependence conditions for Poisson autoregressions

    Paul Doukhan;Konstantinos Fokianos;Dag Tjøstheim

  • Local Gaussian correlation: A new measure of dependence

    Dag Tjøstheim;Karl Ove Hufthammer

  • A nonparametric test of serial independence based on the empirical distribution function

    Unknown

  • Nonlinear Poisson autoregression

    Konstantinos Fokianos;Dag Tjøstheim

  • ASPECTS OF MODELLING NONLINEAR TIME SERIES

    Timo Teräsvirta;Dag Tjøstheim;Clive W J Granger

  • Estimation in Semiparametric Spatial Regression

    Jiti Gao;Zudi Lu;Dag Tjostheim

  • Specification testing in nonlinear and nonstationary time series autoregression

    Jiti Gao;Maxwell Leslie King;Zudi Lu;Dag Tjostheim

  • Chapter 48 Aspects of modelling nonlinear time series

    Timo Teräsvirta;Dag Tjøstheim;Clive W.J. Granger

  • NOTES AND CORRESPONDENCE A Cautionary Note on the Use of the Kolmogorov-Smirnov Test for Normality

    Dag J. Steinskog;Dag B. Tjøstheim;Nils G. Kvamstø

Frequent Co-Authors

Timo Teräsvirta
Timo Teräsvirta Aarhus University
Jiti Gao
Jiti Gao Monash University
Qiwei Yao
Qiwei Yao London School of Economics and Political Science
Olav Rune Godø
Olav Rune Godø Norwegian Institute of Marine Research
Konstantinos Fokianos
Konstantinos Fokianos University of Cyprus
Clive W. J. Granger
Clive W. J. Granger University of California, San Diego
Howell Tong
Howell Tong London School of Economics and Political Science
Elias Masry
Elias Masry University of California, San Diego
Douglas Nychka
Douglas Nychka Colorado School of Mines
David B. Stephenson
David B. Stephenson University of Exeter

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students pursuing Mathematics in the USA, exploring related online degrees can open diverse career pathways. For instance, a masters in digital marketing combines analytical skills with marketing expertise, ideal for data-driven decision-making roles.

For those interested in expanding their business acumen quickly, the availability of cheapest 1 year online mba programs offers an accelerated path to leadership positions. These programs often provide flexibility without compromising on quality.

Transferring credits between MBA programs can also be a practical consideration. Understanding how can you transfer mba programs works ensures smoother academic progress and better alignment with career goals.

Moreover, a masters data analytics is a highly complementary degree, as it builds on mathematical foundations to analyze big data, fueling growth in fields like finance, healthcare, and technology.

Best Scientists Citing Dag Tjøstheim

Trending Scientists

Recently Published Articles