91Ö±²¥

Indeewara’s research interests include model fitting, estimation, inference, and forecasting in non-linear time series models, with special emphasis on statistical analysis of financial data in nonstandard and massive data setups. The concepts and tools used for weak convergence of stochastic processes in metric spaces, bootstrap methods, and goodness-of-fit tests play important roles in most of his research. He also has growing interests in developing and applying bootstrap based methods for inference in high-dimensional statistics/data analysis.

His work has been published in leading peer-reviewed international journals, including the Journal of Econometrics, the Journal of Computational and Graphical Statistics and Econometric Theory.

Indeewara is interested in supervising PhD students working in Econometrics (Theoretical or Applied) and Statistics. Specifically, he is interested in the following areas:

• Developing new methods for model fitting, estimation, inference and forecasting in non-linear Econometric/Time-Series models, including ARCH/GARCH type models, Multiplicative Error models, and Panel Data models.
• Bootstrap and resampling methods in Econometrics and Statistics; in particular, he is interested on nonstandard and massive data setups.