Prof Jiti Gao - Researcher Profile

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Address

Department of Econometrics and Business Statistics
Building 11E, Clayton

Biography

In search of a perfect model

As the sophisticated statistical models he builds are directed at questions about climate change and consumer demand, Professor Jiti Gao’s econometrics research gains a focus on real-life issues that he finds appealing. 

The challenge of finding, creating or finetuning the best models to use in analysing often highly complex data provides a satisfying level of intellectual stimulation for Jiti, an Australian Professorial Fellow and an internationally recognised expert in the fields of non- and semi-parametric econometrics as well as time-series econometrics and financial econometrics.

“I was initially trained in the statistical side of econometrics,” Jiti says. “Gradually my projects have become more motivated by practical issues.”

His interest in the theoretical side of econometrics grew from his training in applied mathematics, and he subsequently developed an interest in both methodology and the empirical application of his research.

Part of Jiti’s work involves an initial determination of the most appropriate class of models to apply to data that may come from disciplines as disparate as the social sciences and engineering. 

He has a particular interest in time series – data that is measured at successive times, such as the temperatures recorded by meteorological bureaus over the years and used in forecasting or the analysis of trends. They are relevant to collaborative work Jiti undertakes with CSIRO, using econometrical modelling to analyse temperature and rainfall series. 

Climate studies and econometrics have been closely linked in recent years, he says, because some econometric models are useful for dealing with the large climatology data sets.

Several Australian Research Council Discovery Project grants support Jiti’s research into new methodologies in time-series econometrics and financial econometrics. He also has some international funding, notably from China and Norway. 

In one recent research project that has had a particularly positive impact, he developed a new approach to continuous-time financial models, which among other things are used to improve forecasting of financial returns. Jiti’s new model estimation and specification methods mean that financial data may be allowed to “speak for themselves” in terms of choosing the best model from those already available. 

In his wider research, says Jiti, the search for a model does not always culminate in a perfect solution, particularly in microeconomics. 

“Sometimes there is no best model,” he says. “You may only be able to find the best approximation.”

It may be the case that several different models are needed for the same data, depending on the requirements. A model that is suitable for forecasting, for example, might not be the best for other aspects.

“You try to get the best model to make sure that you have a relatively accurate modelling procedure to provide solutions to the individual problems,” Jiti says. 

Research may indicate that the best model already exists and is ready to apply unaltered, or that it needs some modification to improve accuracy. 

But testing will sometimes indicate a need to go back to theory in order to develop a new model that takes into account the precise requirements of the task. This may also call for a deeper consideration of the data’s structure in order to fully understand its nature and its implications. 

“The general philosophy for my research is choosing by data,” Jiti says.  “The model is basically data driven rather than having a model imposed on the data.” 

Qualifications

PHD IN ECONOMETRICS
Institution: Monash University
Year awarded: 2005
DOCTORAL COURSE IN SCIENCE
Institution: The University of Science and Tech of China
Year awarded: 1993
MASTER COURSE IN SCIENCE
Institution: China University of Science and Tech
Year awarded: 1988

Publications

Books

Gao, J., 2007, Nonlinear Time Series: Semiparametric and Nonparametric Methods, Chapman & Hall/CRC, Florida USA.

Book Chapters

Gao, J., 2009, Recent developments on semiparametric regression model selection, in Exploration of a Nonlinear World: An Appreciation of Howell Tong's Contributions to Statistics, eds Kung-Sik Chan, World Scientific Publishing Co Pte Ltd, Singapore, pp. 137-146.

Journal Articles

Chen, J., Gao, J., Li, D., 2012, A new diagnostic test for cross-section uncorrelatedness in nonparametric panel data models, Econometric Theory [P], vol 28, issue 5, Cambridge University Press, Cambridge UK, pp. 1144-1163.

Gao, J., 2012, Comments on: Some recent theory for autoregressive count time series, Test [P], vol 21, issue 3, Springer, Heidelberg Germany, pp. 459-463.

Chen, J., Gao, J., Li, D., 2012, Estimation in semi-parametric regression with non-stationary regressors, Bernoulli [P], vol 18, issue 2, International Statistical Institute, The Hague Netherlands, pp. 678-702.

Chen, J., Gao, J., Li, D., 2012, Semiparametric trending panel data models with cross-sectional dependence, Journal of Econometrics [P], vol 171, issue 1, Elsevier BV, Amsterdam Netherlands, pp. 71-85.

Chen, J., Gao, J., Li, D., 2011, Estimation in semiparametric time series regression, Statistics and its Interface [P], vol 4, issue 2, International Press, Somerville MA USA, pp. 243-251.

Li, D., Chen, J., Gao, J., 2011, Non-parametric time-varying coefficient panel data models with fixed effects, Econometrics Journal [P], vol 14, issue 3, Wiley-Blackwell Publishing Ltd, Oxford UK, pp. 387-408.

Chen, S., Gao, J., 2011, Simultaneous specification testing of mean and variance structures in nonlinear time series regression, Econometric Theory [P], vol 27, issue 4, Cambridge University Press, United Kingdom, pp. 792-843.

Gao, J., Wang, Q., Yin, J., 2011, Specification testing in nonlinear time series with long-range dependence, Econometric Theory [P], vol 27, issue 2, Cambridge University Press, United Kingdom, pp. 260-284.

Lin, Z., Li, D., Gao, J., 2009, Local Linear M-estimation in non-parametric spatial regression, Journal of Time Series Analysis [P], vol 30, issue 3, Wiley-Blackwell Publishing Ltd, United Kingdom, pp. 286-314.

Gao, J., King, M.L., Lu, Z., Tjostheim, D., 2009, Nonparametric specification testing for nonlinear time series with nonstationarity, Econometric Theory [P], vol 25, issue 06, Cambridge University Press, United Kingdom, pp. 1869-1892.

Gao, J., Li, D., Lin, Z., 2009, Robust estimation in parametric time series models under long- and short-range-dependent structures, Australian & New Zealand Journal Of Statistics [P], vol 51, issue 2, Wiley-Blackwell Publishing Asia, Richmond Vic Australia, pp. 161-181.

Gao, J., King, M.L., Lu, Z., Tjostheim, D., 2009, Specification testing in nonlinear and nonstationary time series autoregression, Annals of Statistics [P], vol 37, issue 6B, Institute of Mathematical Statistics, United States, pp. 3893-3928.

Chen, S., Gao, J., Tang, C., 2008, A test for model specification of diffusion processes, Annals of Statistics [P], vol 36, issue 1, Institute of Mathematical Statistics, United States, pp. 167-198.

Gao, J., Gijbels, I., 2008, Bandwidth selection in nonparametric kernel testing, Journal Of The American Statistical Association [P], vol 103, issue 484, American Statistical Association, United States, pp. 1584-1594.

Gao, J., Hong, Y., 2008, Central limit theorems for generalized U-statistics with applications in nonparametric specification, Journal Of Nonparametric Statistics [P], vol 20, issue 1, Taylor & Francis Ltd, United Kingdom, pp. 61-76.

Casas, I., Gao, J., 2008, Econometric estimation in long-range dependent volatility models: Theory and practice, Journal of Econometrics [P], vol 147, issue 1, Elsevier BV, Netherlands, pp. 72-83.

Gao, J., Lu, Z., Tjostheim, D., 2008, Moment inequalities for spatial processes, Statistics and Probability Letters [P], vol 78, issue 6, Elsevier BV, Netherlands, pp. 687-697.

Gao, J., Gijbels, I., Van Bellegem, S., 2008, Nonparametric simultaneous testing for structural breaks, Journal of Econometrics [P], vol 143, issue 1, Elsevier BV, Netherlands, pp. 123-142.

Gao, J., Casas, I., 2008, Specification testing in discretized diffusion models: Theory and practice, Journal of Econometrics [P], vol 147, issue 1, Elsevier BV, Netherlands, pp. 131-140.

Chen, S., Gao, J., 2007, An adaptive empirical likelihood test for parametric time series regression models, Journal of Econometrics [P], vol 141, issue 2, Elsevier BV, Netherlands, pp. 950-972.

Casas, I., Gao, J., 2007, Nonparametric methods in continuous time model specification, Econometric Reviews [P], vol 26, issue 1, Taylor & Francis Inc, Philadelphia USA, pp. 91-106.

Dong, C., Gao, J., Tong, H., 2007, Semiparametric penalty function method in partially linear model selection, Statistica Sinica [P], vol 17, issue 1, Academia Sinica Institute of Statistical Science, Taiwan Republic of China, pp. 99-114.

Arapis, M., Gao, J., 2006, Empirical comparisons in short-term interest rate models using nonparametric methods, Journal of Financial Econometrics [P], vol 4, issue 2, Oxford University Press, Oxford UK, pp. 310-345.

Gao, J., Lu, Z., Tjostheim, D., 2006, Estimation in semiparametric spatial regression, Annals of Statistics [P], vol 34, issue 3, Institute of Mathematical Statistics, Beachwood OH USA, pp. 1395-1435.

Gao, J., Hawthorne, K., 2006, Semiparametric estimation and testing of the trend of temperature series, Econometrics Journal [P], vol 9, issue 2, Wiley-Blackwell Publishing Ltd, Oxford UK, pp. 332-355.

Yao, J., Gao, J., Alles, L., 2005, Dynamic investigation into the predictability of Australian industrial stock returns: Using financial and economic information, Pacific Basin Finance Journal [P], vol 13, issue 2, Elsevier BV North-Holland, Amsterdam Netherlands, pp. 225-245.

Gao, J., King, M.L., 2004, Adaptive testing in continuous-time diffusion models, Econometric Theory, vol 20, issue 5, Cambridge University Press, UK, pp. 844-882.