1. Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model

(With M. Scharth) PaperDataMain codeConference SlidesPoster

2. Unobserved components and stochastic volatility in U.S. Inflation: Estimation and signal extraction

(With S.J. Koopman, submitted) PaperDataMain codeConference slides

3. Long term forecasting of El Niño events via dynamic factor simulations

(With S.J. Koopman, R. Lit, and D. Petrova, submitted) PaperMain codeConference slides; Data can be requested

4. Forecasting economic time series using score-driven models with mixed-data sampling

(With P. Gorgi, and S.J. Koopman, submitted) PaperConference slides

5. Are long-run output growth rates falling? Evidence from time-varying parameter models

(With I. Mendieta-Muñoz)  University of Utah, Department of Economics Working Paper Series. Working Paper No: 2017-03. PaperPoster

6. Look for the stars: Estimating the natural rate of interest

(With I. Hindrayanto) Paper

7. Unobserved components time-varying vector autoregressions

8. Global evidence on the changing dynamics of inflation expectation formation

(With G. Galati)


On the methodological side, my research employs either sequential Monte Carlo method (Bayesian) or simulated maximum likelihood (frequentist), usually with the following keywords: importance sampling, particle filter, particle Gibbs, Kalman filter, simulation smoothing. On the empirical side, macroeconomic and financial modelling is the central theme. For instance, central banks need to estimate the natural rate of output growth and real interest rate for making monetary policy and construct coincidental indicator for understanding the stance of the economy. Or Investors would like to balance their expected gain against hidden time-varying volatility, managing a portfolio that takes many positions. I firmly uphold academic integrity and professionalism when conducting replicable research.