What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Econometrics
His main research concerns Econometrics, Statistics, Unit root, Null hypothesis and Structural break.
His work on Augmented Dickey–Fuller test as part of general Econometrics study is frequently linked to Null, bridging the gap between disciplines.
The concepts of his Unit root study are interwoven with issues in Mean reversion, Autoregressive model and Unit root test.
In his research on the topic of Null hypothesis, Trend stationary, Constant and Asymptotic distribution is strongly related with Consistency.
Stephen J. Leybourne usually deals with Structural break and limits it to topics linked to Spurious relationship and Cointegration.
His research in Consensus forecast intersects with topics in Test, Forecast skill and Non normality.
His most cited work include:
- Testing the equality of prediction mean squared errors (1149 citations)
- Tests for Forecast Encompassing (540 citations)
- Unit roots and smooth transitions (307 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Econometrics, Unit root, Statistics, Unit root test and Null.
His work on Range expands to the thematically related Econometrics.
His studies in Unit root integrate themes in fields like Order of integration, Estimator, Applied mathematics and Autoregressive model.
His study in the field of Statistic, Monte Carlo method, Statistical hypothesis testing and Autocorrelation is also linked to topics like Initial value problem.
Within one scientific family, Stephen J. Leybourne focuses on topics pertaining to Augmented Dickey–Fuller test under Unit root test, and may sometimes address concerns connected to Test.
His Null hypothesis study also includes fields such as
- KPSS test that intertwine with fields like Portmanteau test,
- Spurious relationship that intertwine with fields like Structural break.
He most often published in these fields:
- Econometrics (58.24%)
- Unit root (51.65%)
- Statistics (46.70%)
What were the highlights of his more recent work (between 2014-2021)?
- Econometrics (58.24%)
- Statistics (46.70%)
- Unit root (51.65%)
In recent papers he was focusing on the following fields of study:
Stephen J. Leybourne mainly investigates Econometrics, Statistics, Unit root, Explosive material and Monte Carlo method.
Stephen J. Leybourne regularly ties together related areas like Predictability in his Econometrics studies.
His Null distribution and Statistical hypothesis testing study in the realm of Statistics connects with subjects such as Null.
The Trend break research he does as part of his general Unit root study is frequently linked to other disciplines of science, such as Magnitude, therefore creating a link between diverse domains of science.
Stephen J. Leybourne combines subjects such as Estimator and Sample size determination with his study of Monte Carlo method.
His Alternative hypothesis study, which is part of a larger body of work in Null hypothesis, is frequently linked to Rank, bridging the gap between disciplines.
Between 2014 and 2021, his most popular works were:
- Tests for explosive financial bubbles in the presence of non-stationary volatility (62 citations)
- Recursive Right-Tailed Unit Root Tests for an Explosive Asset Price Bubble (17 citations)
- Improving the accuracy of asset price bubble start and end date estimators (15 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Econometrics
Stephen J. Leybourne focuses on Econometrics, Economic bubble, Volatility, Explosive material and Unit root.
His Econometrics research is multidisciplinary, relying on both Sample, Statistics and Estimator.
His work is connected to Monte Carlo method, Sample size determination and Autocorrelation, as a part of Statistics.
His Estimator research includes themes of Variance decomposition of forecast errors, Autoregressive model and Unit root test.
His work carried out in the field of Volatility brings together such families of science as Spurious relationship, Statistic and Limit distribution.
His Statistic study incorporates themes from Nominal level and False positive rate.
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