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Mathematics

D-Index
36
Citations
5690
World Ranking
2634
National Ranking
176

Overview

Liudas Giraitis is affiliated with Queen Mary University of London in the United Kingdom. Their research focuses extensively on areas within economics, econometrics, and finance, with a strong interdisciplinary overlap in mathematics.

The main fields of study for Liudas Giraitis include:

  • Economics, Econometrics and Finance
  • Mathematics

Within these broad areas, subfields of specialization are identified as:

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

The principal topics addressed in their work cover:

  • Financial Risk and Volatility Modeling
  • Complex Systems and Time Series Analysis
  • Market Dynamics and Volatility
  • Monetary Policy and Economic Impact
  • Statistical Methods and Inference
  • Advanced Statistical Methods and Models
  • Economic theories and models

Liudas Giraitis has been published in several academic venues, with multiple contributions in the following journals:

  • Journal of Econometrics
  • Econometric Theory
  • Journal of Time Series Analysis
  • SSRN Electronic Journal
  • The Annals of Statistics

Recent publications by Liudas Giraitis include:

  • Time-varying instrumental variable estimation, 2020, Journal of Econometrics
  • ROBUST TESTS FOR WHITE NOISE AND CROSS-CORRELATION, 2020, Econometric Theory
  • ESTIMATION OF TIME-VARYING COVARIANCE MATRICES FOR LARGE DATASETS, 2021, Econometric Theory
  • Asymptotic theory for time series with changing mean and variance, 2020, Journal of Econometrics
  • Testing Mean Stability of Heteroskedastic Time Series, 2025, Journal of Time Series Analysis

Their collaborative research network includes frequent co-authors such as:

  • Peter C.B. Phillips
  • Violetta Dalla
  • George Kapetanios
  • Yufei Li
  • Massimiliano Marcellino

Best Publications

  • Rescaled variance and related tests for long memory in volatility and levels

    Liudas Giraitis;Piotr Kokoszka;Remigijus Leipus;Gilles Teyssière

  • A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotical normality of Whittle's estimate

    L. Giraitis;D. Surgailis

  • STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM

    Liudas Giraitis;Piotr Kokoszka;Remigijus Leipus

  • Large Sample Inference for Long Memory Processes

    Liudas Giraitis;Hira L. Koul;Donatas Surgailis

  • Nonstationarity-extended local Whittle estimation

    Karim M. Abadir;Walter Distaso;Liudas Giraitis

  • CLT and other limit theorems for functionals of Gaussian processes

    L. Giraitis;D. Surgailis

  • A generalized fractionally differencing approach in long-memory modeling

    L. Giraitis;R. Leipus

  • Inference on stochastic time-varying coefficient models

    L. Giraitis;G. Kapetanios;Tony Yates

  • A model for long memory conditional heteroscedasticity

    Liudas Giraitis;Peter M. Robinson;Donatas Surgailis

  • Whittle Estimation of ARCH Models

    Liudas Giraitis;Peter M. Robinson

  • Uniform Limit Theory for Stationary Autoregression

    Liudas Giraitis;Peter C. B. Phillips

  • Asymptotic normality of regression estimators with long memory errors

    Liudas Giraitis;Hira L Koul;Donatas Surgailis

  • Testing for long memory in the presence of a general trend

    Liudas Giraitis;Piotr Kokoszka;Remigijus Leipus

  • Recent Advances in ARCH Modelling

    Liudas Giraitis;Remigijus Leipus;Donatas Surgailis

  • Multivariate Appell polynomials and the central limit theorem

    L. Giraitis;D. Surgailis

  • LARCH, Leverage, and Long Memory

    Liudas Giraitis;Remigijus Leipus;Peter M. Robinson;Donatas Surgailis

  • ARCH-type bilinear models with double long memory

    Liudas Giraitis;Donatas Surgailis

  • RATE OPTIMAL SEMIPARAMETRIC ESTIMATION OF THE MEMORY PARAMETER OF THE GAUSSIAN TIME SERIES WITH LONG‐RANGE DEPENDENCE

    Liudas Giraitis;Peter M. Robinson;Alexander Samarov

  • Gaussian Estimation of Parametric Spectral Density with Unknown Pole

    Liudas Giraitis;Javier Hidalgo;Peter M Robinson

  • Central limit theorem for the empirical process of a linear sequence with long memory

    Liudas Giraitis;Liudas Giraitis;Donatas Surgailis

  • A MODEL FOR LONG MEMORY CONDITIONAL HETEROSCEDASTICITY by

    Liudas Giraitis;Peter Robinson;Donatas Surgailis

Frequent Co-Authors

Peter M. Robinson
Peter M. Robinson London School of Economics and Political Science
Donatas Surgailis
Donatas Surgailis Vilnius University
Piotr Kokoszka
Piotr Kokoszka Colorado State University
George Kapetanios
George Kapetanios King's College London
Hira L. Koul
Hira L. Koul Michigan State University
Murad S. Taqqu
Murad S. Taqqu Boston University
Peter C. B. Phillips
Peter C. B. Phillips Yale University

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