What is she best known for?
The fields of study she is best known for:
- Statistics
- Normal distribution
- Econometrics
Her primary areas of investigation include Econometrics, Statistics, Factor analysis, Dynamic factor and Panel data.
Her work carried out in the field of Econometrics brings together such families of science as Estimator, Series and Yield.
Her study ties her expertise on Inference together with the subject of Statistics.
Her biological study spans a wide range of topics, including Sample, Consistent Estimation and Principal component analysis.
Serena Ng has researched Dynamic factor in several fields, including Econometric model and Risk premium.
Her work deals with themes such as Information Criteria, Autoregressive model and Akaike information criterion, which intersect with Unit root.
Her most cited work include:
- LAG LENGTH SELECTION AND THE CONSTRUCTION OF UNIT ROOT TESTS WITH GOOD SIZE AND POWER (2985 citations)
- Determining the Number of Factors in Approximate Factor Models (2468 citations)
- Unit Root Tests in ARMA Models with Data-Dependent Methods for the Selection of the Truncation Lag (1369 citations)
What are the main themes of her work throughout her whole career to date?
Serena Ng mostly deals with Econometrics, Estimator, Statistics, Series and Unit root.
Her study in the fields of Cointegration under the domain of Econometrics overlaps with other disciplines such as Estimation.
Her study in Estimator is interdisciplinary in nature, drawing from both Consistency, Conditional expectation, Least squares and Autoregressive model.
Serena Ng works mostly in the field of Unit root, limiting it down to concerns involving Spectral density and, occasionally, Kernel.
While the research belongs to areas of Principal component analysis, Serena Ng spends her time largely on the problem of Factor analysis, intersecting her research to questions surrounding Dimensionality reduction and Econometric model.
Serena Ng focuses mostly in the field of Information Criteria, narrowing it down to matters related to Akaike information criterion and, in some cases, Degrees of freedom.
She most often published in these fields:
- Econometrics (59.55%)
- Estimator (23.18%)
- Statistics (23.18%)
What were the highlights of her more recent work (between 2018-2021)?
- Econometrics (59.55%)
- Consumer confidence index (4.55%)
- Estimator (23.18%)
In recent papers she was focusing on the following fields of study:
Serena Ng mainly focuses on Econometrics, Consumer confidence index, Estimator, Dirichlet distribution and Categorical variable.
Particularly relevant to Covariate is her body of work in Econometrics.
The Consumer confidence index study combines topics in areas such as Univariate, Yield, Aggregate, Seasonality and Artificial intelligence.
Her research in Univariate tackles topics such as Scanner which are related to areas like Machine learning and Panel data.
Her Panel data study is concerned with the field of Statistics as a whole.
Her research integrates issues of Boosting and Conditional expectation in her study of Estimator.
Between 2018 and 2021, her most popular works were:
- Covid19 and the Macroeconomic Effects of Costly Disasters (27 citations)
- Rank regularized estimation of approximate factor models (18 citations)
- Rank regularized estimation of approximate factor models (18 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Regression analysis
Serena Ng focuses on Sample, Least squares, Applied mathematics, Coronavirus disease 2019 and Econometrics.
Her Sample research includes elements of Shock and Monetary economics.
Her Least squares study incorporates themes from Principal component analysis and Factor analysis.
Her Applied mathematics research integrates issues from Computation and Instrumental variable.
Her biological study focuses on Covariate.
Her research investigates the connection between Covariate and topics such as Estimator that intersect with problems in Boosting, Range, Series and Conditional expectation.
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