What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Regression analysis
Liudas Giraitis mainly investigates Applied mathematics, Statistics, Mathematical analysis, Asymptotic distribution and Moving average.
His Applied mathematics study combines topics from a wide range of disciplines, such as Estimation theory, Asymptotic expansion, Limit theory and Autoregressive model.
His study on Heteroscedasticity, Sample size determination and Null hypothesis is often connected to Magnitude as part of broader study in Statistics.
Liudas Giraitis works mostly in the field of Mathematical analysis, limiting it down to concerns involving Mathematical finance and, occasionally, Pure mathematics, Limit, Quadratic form, Covariance and Normality.
His Asymptotic distribution research incorporates themes from Volatility, Statistical hypothesis testing, Statistic and Monte Carlo method.
The concepts of his Moving average study are interwoven with issues in Central limit theorem and Long memory.
His most cited work include:
- A central limit theorem for quadratic forms in strongly dependent linear variables and its application to asymptotical normality of Whittle's estimate (324 citations)
- Rescaled variance and related tests for long memory in volatility and levels (262 citations)
- STATIONARY ARCH MODELS: DEPENDENCE STRUCTURE AND CENTRAL LIMIT THEOREM (235 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Applied mathematics, Estimator, Econometrics, Statistics and Asymptotic distribution.
The study incorporates disciplines such as Central limit theorem, Series, Long memory and Spectral density in addition to Applied mathematics.
His Estimator course of study focuses on Autocovariance and Asymptotic theory.
Liudas Giraitis has researched Econometrics in several fields, including Monte Carlo method and Moving average.
His Moving average research incorporates themes from Martingale and Stationary sequence.
His research in Asymptotic distribution intersects with topics in Mathematical analysis, Consistency and Martingale difference sequence.
He most often published in these fields:
- Applied mathematics (47.01%)
- Estimator (26.12%)
- Econometrics (25.37%)
What were the highlights of his more recent work (between 2013-2021)?
- Econometrics (25.37%)
- Applied mathematics (47.01%)
- Series (20.90%)
In recent papers he was focusing on the following fields of study:
Liudas Giraitis mainly focuses on Econometrics, Applied mathematics, Series, Asymptotic distribution and Heteroscedasticity.
The Econometrics study combines topics in areas such as Kernel density estimation and Dynamic stochastic general equilibrium.
His Applied mathematics research incorporates elements of Nonparametric statistics, Covariance, Bounded function, Limit and Monte Carlo method.
His Series study also includes
- Bivariate analysis which is related to area like Univariate, Correlogram, White noise, Autocorrelation and Position,
- Time series, which have a strong connection to Mean squared error, Consensus forecast and Moving average.
To a larger extent, Liudas Giraitis studies Estimator with the aim of understanding Asymptotic distribution.
His research integrates issues of Pointwise and Conditional variance in his study of Estimator.
Between 2013 and 2021, his most popular works were:
- Inference on stochastic time-varying coefficient models (59 citations)
- Asymptotic normality for weighted sums of linear processes (22 citations)
- Estimating the Dynamics and Persistence of Financial Networks, with an Application to the Sterling Money Market (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Real number
Liudas Giraitis mostly deals with Econometrics, Asymptotic distribution, Autoregressive model, Heteroscedasticity and Series.
Liudas Giraitis focuses mostly in the field of Econometrics, narrowing it down to matters related to Kernel and, in some cases, Multivariate statistics, Conditional variance, Pointwise, Position and Interbank lending market.
His Asymptotic distribution study incorporates themes from Martingale and Martingale difference sequence.
Liudas Giraitis interconnects Volatility, Spurious relationship and Stability in the investigation of issues within Heteroscedasticity.
His Series study integrates concerns from other disciplines, such as Bivariate analysis and Autoregressive conditional heteroskedasticity.
His biological study spans a wide range of topics, including Triangular array, Moving average and Applied mathematics.
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