What is he best known for?
The fields of study he is best known for:
- Statistics
- Mathematical analysis
- Econometrics
Statistics, Econometrics, Unit root, Series and Null hypothesis are his primary areas of study.
His studies link Null with Statistics.
His work on Volatility and Heteroscedasticity as part of his general Econometrics study is frequently connected to Invariant, thereby bridging the divide between different branches of science.
A. M. Robert Taylor works mostly in the field of Unit root, limiting it down to topics relating to Unit root test and, in certain cases, Augmented Dickey–Fuller test, as a part of the same area of interest.
His study on Order of integration is often connected to Univariate as part of broader study in Series.
His research in Null hypothesis intersects with topics in Consistency, Decision rule and Trend stationary.
His most cited work include:
- Tests of stationarity against a change in persistence (160 citations)
- Testing for unit roots in time series models with non-stationary volatility (116 citations)
- BOOTSTRAP UNIT ROOT TESTS FOR TIME SERIES WITH NONSTATIONARY VOLATILITY (104 citations)
What are the main themes of his work throughout his whole career to date?
A. M. Robert Taylor mainly investigates Statistics, Econometrics, Unit root, Series and Null.
His study in Monte Carlo method, Null hypothesis, Statistical hypothesis testing, Sample and Autocorrelation are all subfields of Statistics.
His work in the fields of Econometrics, such as Heteroscedasticity and Volatility, overlaps with other areas such as Inference.
His work deals with themes such as Regression, Unit root test, Estimator, Applied mathematics and Decision rule, which intersect with Unit root.
His study in the fields of Order of integration under the domain of Series overlaps with other disciplines such as Univariate and Persistence.
His Null study integrates concerns from other disciplines, such as Limit and Nyquist–Shannon sampling theorem.
He most often published in these fields:
- Statistics (48.94%)
- Econometrics (47.66%)
- Unit root (40.85%)
What were the highlights of his more recent work (between 2014-2021)?
- Econometrics (47.66%)
- Heteroscedasticity (17.45%)
- Statistics (48.94%)
In recent papers he was focusing on the following fields of study:
A. M. Robert Taylor spends much of his time researching Econometrics, Heteroscedasticity, Statistics, Unit root and Monte Carlo method.
In general Econometrics, his work in Volatility is often linked to Inference linking many areas of study.
A. M. Robert Taylor interconnects Statistical hypothesis testing, Covariance matrix and Autoregressive conditional heteroskedasticity in the investigation of issues within Heteroscedasticity.
His Statistic and Unit root test study, which is part of a larger body of work in Statistics, is frequently linked to Zero, Zero frequency and Kernel, bridging the gap between disciplines.
His Unit root study combines topics in areas such as Discrete time and continuous time, Series and Ordinary least squares.
His Series research integrates issues from Sampling, Null, Power function and Systematic sampling.
Between 2014 and 2021, his most popular works were:
- Tests for explosive financial bubbles in the presence of non-stationary volatility (62 citations)
- Inference on Co-integration Parameters in Heteroskedastic Vector Autoregressions (21 citations)
- Bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets (20 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Econometrics
His primary areas of study are Econometrics, Statistics, Monte Carlo method, Heteroscedasticity and Volatility.
His Unit root study in the realm of Econometrics interacts with subjects such as Inference.
His Statistics study frequently intersects with other fields, such as Null.
The various areas that A. M. Robert Taylor examines in his Heteroscedasticity study include Statistical hypothesis testing and Autoregressive fractionally integrated moving average.
His Statistical hypothesis testing study deals with Covariance matrix intersecting with Homoscedasticity.
His study in Volatility is interdisciplinary in nature, drawing from both Parametric model, Statistic and Infimum and supremum.
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