What is he best known for?
The fields of study he is best known for:
- Statistics
- Finance
- Mathematical analysis
Christian Gourieroux mainly focuses on Econometrics, Estimator, Statistics, Autoregressive model and Econometric model.
His research ties Variables and Econometrics together.
His Estimator study integrates concerns from other disciplines, such as Inference, Applied mathematics and Homoscedasticity.
His Wald test, Test statistic and Factor analysis study in the realm of Statistics connects with subjects such as Goldfeld–Quandt test and Multinomial test.
His Autoregressive model research incorporates themes from Wishart distribution, State-space representation, Stock exchange, Ergodicity and Autocorrelation.
His Econometric model study combines topics from a wide range of disciplines, such as Variety and Mathematical economics.
His most cited work include:
- PSEUDO MAXIMUM LIKELIHOOD METHODS: THEORY (982 citations)
- Simulation-based econometric methods (862 citations)
- Pseudo Maximum Likelihood Methods: Applications to Poisson Models (781 citations)
What are the main themes of his work throughout his whole career to date?
Christian Gourieroux mostly deals with Econometrics, Applied mathematics, Estimator, Statistics and Actuarial science.
The concepts of his Econometrics study are interwoven with issues in Portfolio and Credit risk.
His Applied mathematics research focuses on subjects like Nonlinear system, which are linked to Series.
He regularly links together related areas like Moment in his Estimator studies.
His Statistics study is mostly concerned with Univariate and Bivariate analysis.
His work deals with themes such as Wishart distribution, Markov chain and Marginal distribution, which intersect with Autoregressive model.
He most often published in these fields:
- Econometrics (46.14%)
- Applied mathematics (19.65%)
- Estimator (16.56%)
What were the highlights of his more recent work (between 2013-2021)?
- Econometrics (46.14%)
- Applied mathematics (19.65%)
- Estimator (16.56%)
In recent papers he was focusing on the following fields of study:
Econometrics, Applied mathematics, Estimator, Autoregressive model and Portfolio are his primary areas of study.
His work on Unobservable as part of general Econometrics study is frequently connected to Estimation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Applied mathematics research includes themes of Identification, Mathematical optimization, Asymptotic distribution, Consistency and Nonlinear system.
His work deals with themes such as Maximum likelihood, Covariance, Copula and Multivariate statistics, which intersect with Estimator.
His research on Autoregressive model also deals with topics like
- Marginal distribution that intertwine with fields like Series,
- Markov chain which connect with Bivariate analysis, Negative binomial distribution, Wishart distribution, Moment and Stochastic volatility.
Christian Gourieroux combines subjects such as Fund administration, Performance fee, Returns-based style analysis and Hedge fund with his study of Portfolio.
Between 2013 and 2021, his most popular works were:
- Noncausal Autoregressive Model in Application to Bitcoin/USD Exchange Rates (48 citations)
- Statistical inference for independent component analysis: Application to structural VAR models (28 citations)
- Local explosion modelling by non‐causal process (26 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Finance
- Mathematical analysis
His primary areas of study are Applied mathematics, Econometrics, Autoregressive model, Estimator and Nonlinear system.
His work carried out in the field of Applied mathematics brings together such families of science as Identification, Impulse response, Mathematical optimization, Asymptotic distribution and Consistency.
His Asymptotic distribution research incorporates themes from Indirect Inference, Likelihood function, Model selection and Asymptotic analysis.
He studies Econometrics, focusing on Factor analysis in particular.
His research integrates issues of Univariate, Marginal distribution, Digital currency, Markov chain and Exchange rate in his study of Autoregressive model.
His Estimator study combines topics from a wide range of disciplines, such as Transformation, Multivariate statistics, Mixed model and Stochastic volatility.
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