Mengheng is a lecturer at Economics Discipline Group, UTS. Before working at UTS, he worked as a research analyst at the Economic Research and Policy Division of the Dutch central bank. Mengheng obtained his PhD in Econometrics from the Department of Econometrics, VU University Amsterdam and the Tinbergen Institute. Mengheng works on nonlinear/non-Gaussian state space models, Bayesian methods and high-dimensional time series models; on the empirical side, he focuses on econometric treatment of new Keynesian concepts, e.g. Phillips curve, inflation expectation, money in Taylor rule, estimation of natural rates (NRR, NAIRU) and, estimation of output gap.
- Member of the Econometric Society
- Member of the International Association for Applied Econometrics
- Member of the Society for Financial Econometrics
Can supervise: YES
- Empirical Macroeconomics
- Inflation Modelling
- Nonlinear and Non-Gaussian State Space Models
- Bayesian Econometrics
- Sequential Monte Carlo and Simulation Techniques
- Business Analytics
- Monetary Economics
- Quantitative Methods
Gorgi, P, Koopman, SJ & Li, M 2019, 'Forecasting economic time series using score-driven dynamic models with mixed-data sampling', International Journal of Forecasting.View/Download from: UTS OPUS or Publisher's site
We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables.
We verify in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our
proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of point and density forecasts.
© 2019 John Wiley & Sons Ltd This paper studies the evolution of long-run output and technical progress growth rates in the G-7 countries during the post-war period by considering the concept of the natural rate of growth. We use time-varying parameter models that incorporate both stochastic volatility and a Heckman-type two-step estimation procedure that deals with the possible endogeneity problem in the econometric models. Our results show a significant decline in long-run growth rates that is not associated with the detrimental effects of the Great Recession, and that the rate of growth of technical progress appears to be behind the slowdown in long-run GDP growth.
We propose a new forecasting procedure which particularly explores opportunities for improving the precision of medium and long-term forecasts of the Ni~no3.4 time series that is linked with the well-known El Ni~no phenomenon. This important climatic time series is subject to an intricate dynamic structure and is interrelated to other climatological variables. The procedure consists of three steps. First, a
univariate time series model is considered for producing prediction errors. Second, signal paths of the prediction errors are simulated via a dynamic factor model for the errors and explanatory variables. From these simulated errors, ensemble time series for Ni~no3.4 are constructed. Third, forecasts are generated from the ensemble time
series and their sample average is our nal forecast. As part of these dynamic factor simulations, we also obtain the forecast of the El Ni~no event which is a categorical variable. We present empirical evidence that our procedure can be superior in its forecasting performance when compared to other econometric forecasting methods.
Li, M & Mendieta-Munoz, I 2019, 'The multivariate simultaneous unobserved components model and identification via heteroskedasticity', UTS Business School Economics Working Paper Series - Paper No. 2019/08.
We propose a multivariate simultaneous unobserved components framework to determine the two-sided interactions between structural trend and cycle innovations. We relax the standard assumption in unobserved components models that trends are only driven by permanent shocks and cycles are only driven by transitory shocks by considering the possible spill over effects between structural innovations. The direction of spill over has a structural interpretation, whose identification is achieved via heteroskedasticity. We provide identifiability conditions and develop an efficient Bayesian MCMC procedure for estimation. Empirical implementations for both Okun’s law and the Phillips curve show evidence of significant spill overs between trend and cycle components.
Li, M & Hindrayanto, I 2018, 'Looking for the stars: Estimating the natural rate of interest', UTS Business School Economics Working Paper Series 2018 - Paper No. 51.
Li, M & Koopman, SJ 2018, 'Unobserved components with stochastic volatility in U.S. inflation: Estimation and signal extraction', Tinbergen Institute Discussion Papers TI 2018-027/III.
Li, M & Mendieta-Munoz, I 2018, 'Are long-run output growth rates falling? Evidence from time-varying parameter models', University of Utah, Department of Economics Working Paper Series: 2018 -02.
Li, M & Scharth, M 2018, 'Leverage, asymmetry and heavy tails in the high-dimensional factor stochastic volatility model', UTS Business School Economics Working Paper Series 2018 - Paper No. 49.
We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry and heavy tails across all systematic and
idiosyncratic components of the model. This framework accounts for well-documented features of univariate financial time series, while introducing a flexible dependence structure that incorporates tail dependence and asymmetries such
as stronger correlations following downturns. We develop an efficient Markov chain Monte Carlo (MCMC) algorithm for posterior simulation based on the particle Gibbs, ancestor sampling, and particle efficient importance sampling
methods. We build computationally efficient model selection into our estimation framework to obtain parsimonious specifications in practice. We validate the performance of our proposed estimation method via extensive simulation
studies for univariate and multivariate simulated datasets. An empirical study shows that the model outperforms other multivariate models in terms of value-at-risk evaluation and portfolio selection performance for a sample of US and