1. Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model (Job Market Paper)
2. Unobserved components and stochastic volatility in U.S. Inflation: Estimation and signal extraction
3. Long term forecasting of El Niño events via dynamic factor simulations
4. Forecasting economic time series using score-driven models with mixed-data sampling
(With P. Gorgi, and S.J. Koopman) Conference 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. Paper
6. Are measures of natural rate of interest always inaccurate?
(With I. Hindrayanto)
7. Unobserved components time-varying vector autoregressions
8. Global evidence on the changing persistence of core inflation
(With G. Galati)
Mengheng’s research is mostly computation-intensive and employs either sequential Monte Carlo method (Bayesian) or simulated maximum likelihood (frequentist), depending on the objective function of a specific research question. In Mengheng’s work, all models, methodologies, and computer programs are motivated by real-life economic and financial problems. For instance, central banks need to forecast inflation for implementing monetary policies and understand their transmission channel. Or Investors would like to balance their expected gain against hidden time-varying volatility, managing a portfolio that takes many positions. Mengheng is proud to uphold academic integrity and professionalism when conducting research.