What is he best known for?
The fields of study he is best known for:
- Statistics
- Econometrics
- Finance
Clive W. J. Granger mainly investigates Econometrics, Series, Cointegration, Applied mathematics and Mathematical economics.
His Econometrics study combines topics in areas such as Statistics, Time series and Autocorrelation.
His biological study spans a wide range of topics, including Test, Score test, Moving average, Artificial neural network and Spectrum.
Clive W. J. Granger has included themes like Autoregressive fractionally integrated moving average, Long memory, Function, Linear function and White noise in his Applied mathematics study.
His research investigates the link between Error correction model and topics such as Vector autoregression that cross with problems in Unit root test.
His work deals with themes such as Autoregressive model and Portfolio, which intersect with Unit root.
His most cited work include:
- Co-integration and error correction: representation, estimation and testing (22764 citations)
- Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. (12457 citations)
- Spurious regressions in econometrics (4178 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Econometrics, Series, Cointegration, Statistics and Nonlinear system.
His Econometrics study combines topics from a wide range of disciplines, such as Bivariate analysis and Mathematical economics.
His Series research incorporates themes from Statistical physics, Applied mathematics and Time series.
His work in Cointegration addresses subjects such as Error detection and correction, which are connected to disciplines such as Error correction model.
His specific area of interest is Statistics, where he studies Autocorrelation.
His Moving average research extends to the thematically linked field of Autoregressive model.
He most often published in these fields:
- Econometrics (62.25%)
- Series (27.42%)
- Cointegration (15.28%)
What were the highlights of his more recent work (between 2001-2016)?
- Econometrics (62.25%)
- Volatility (7.87%)
- Financial economics (7.42%)
In recent papers he was focusing on the following fields of study:
Clive W. J. Granger focuses on Econometrics, Volatility, Financial economics, Amazon rainforest and Conditional probability distribution.
His Econometrics research includes themes of Bivariate analysis, Statistics and Series.
His work focuses on many connections between Volatility and other disciplines, such as Financial market, that overlap with his field of interest in Working hypothesis, Valuation of options and Long memory.
His Amazon rainforest study integrates concerns from other disciplines, such as Deforestation, Econometric model, Public policy and Environmental protection.
His Conditional probability distribution research also works with subjects such as
- Market data that intertwine with fields like Modern portfolio theory and Investment theory,
- Conditional expectation which intersects with area such as Distribution, Conditional independence and Time series.
His Heteroscedasticity research includes elements of Autoregressive model, Volatility clustering, Variables and Financial asset.
Between 2001 and 2016, his most popular works were:
- Forecasting Volatility in Financial Markets: A Review (1243 citations)
- Occasional structural breaks and long memory with an application to the S&P 500 absolute stock returns (618 citations)
- Handbook of Economic Forecasting (557 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Econometrics
- Finance
His primary areas of study are Econometrics, Volatility, Financial economics, Series and Time series.
While working in this field, Clive W. J. Granger studies both Econometrics and Market activity.
His study in the fields of Portfolio and Diversification under the domain of Financial economics overlaps with other disciplines such as Risk level and As is.
The various areas that Clive W. J. Granger examines in his Series study include Bivariate analysis, Measure, Positive economics, Variety and Estimation.
His Time series research incorporates elements of Statistical hypothesis testing, Mathematical economics, Outlier, Conditional probability distribution and Conditional expectation.
His Autoregressive model research is multidisciplinary, relying on both Curse of dimensionality, Aggregate, Efficiency and A priori and a posteriori.
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