Nowcasting GDP using dynamic factor model: A Bayesian approach

Real-time nowcasting is an assessment of current economic conditions from timely released economic series (such as monthly macroeconomic data) before the direct measure (such as quarterly GDP figure) is disseminated. Dynamic factor models (DFMs) are widely used in econometrics to bridge series with different frequencies and achieve a reduction in dimensionality. However, most of the research using DFMs often assumes the number of factors is known. In this dissertation, we first develop a Bayesian approach to provide a way to deal with unbalanced feature of the data set and to estimate latent common factors when the number of factors is assumed to be fixed and known. Then we extend our method such that it can identify the unknown number of factors and estimate the latent dynamic factors of DFMs accurately in a real-time nowcasting framework. The proposed method can deal with the unbalanced data, which is typical of a real-time nowcasting analysis. We demonstrate the validity of our approach through simulation studies and explore the applicability of our approach through empirical studies in nowcasting China\u27s GDP or US GDP using monthly data series of several categories in each country\u27s market respectively. The simulation studies and empirical studies indicate that our Bayesian approach is a viable option to conduct real-time nowcasting for China\u27s and US\u27s quarterly GDP

Equation of State Dependence of Nonlinear Mode-tide Coupling in Coalescing Binary Neutron Stars

Recently, an instability due to the nonlinear coupling of p-modes to g-modes in tidally deformed neutron stars in coalescing binaries has been studied in some detail. The result is significant because it could influence the inspiral and leave an imprint on the gravitational wave signal that depends on the neutron star equation of state (EOS). Because of its potential importance, the details of the instability should be further elucidated and its sensitivity to the EOS should be investigated. To this end, we carry out a numerical analysis with six representative EOSs for both static and non-static tides. We confirm that the absence of the p-g instability under static tides, as well as its return under non-static tides, is generic across EOSs, and further reveal a new contribution to it that becomes important for moderately high-order p-g pairs (previous studies concentrated on very high order modes), whose associated coupling strength can vary by factors of ~10-100 depending on the EOS. We find that, for stars with stiffer EOSs and smaller buoyancy frequencies, the instability onsets earlier in the inspiral and the unstable modes grow faster. These results suggest that the instability's impact on the gravitational wave signal might be sensitive to the neutron star EOS. To fully assess this prospect, future studies will need to investigate its saturation as a function of the EOS and the binary parameters.Comment: 21 pages, 14 figure

Historical Occurrence of Algal Blooms in the Northern Beibu Gulf of China and Implications for Future Trends

Large-scale harmful algal blooms (HABs) occur in the coastal waters of the northern Beibu Gulf, China, and have deleterious effects on the marine ecosystem. The frequency, duration, and extent of HAB events in this region have increased over the last 30 years. However, the underlying causes of HABs and their likely future trends are unclear. To investigate, we evaluated historical data for temporal trends of HABs in the Beibu Gulf, and association with environmental factors as possible drivers. The results confirmed that HAB events had increased in frequency, from 6 reported events during the period 1985–2000, to 13 during 2001–2010, and 20 during 2011–2017. We also found that the geographic scale of algal blooms had increased from tens of km2 to hundreds of km2. There were temporal changes in HAB trigger species: prior to 2000, the cyanobacteria Microcystis aeruginosa was the dominant species, while during the period 2001–2010, blooms of cyanobacteria, dinoflagellates, and diatoms co-occurred, and during 2011–2017, the haptophyte Phaeocystis globosa became the dominant algal bloom species. Principal component analysis and variation partitioning analysis indicated that nutrient discharge, industrial development, and human activities were the key drivers of HAB events, and redundancy analysis showed that variation in the algal community tended to be driven by nutrient structure. Other factors, such as shipping activities and mariculture, also contributed to HAB events and algal succession, especially to P. globosa blooms. We speculated that the increasing severity of algal blooms in the northern Beibu Gulf reflects a more complex aquatic environment and highlights the damaging effects of anthropogenic inputs, urbanization development, and an expanding industrial marine-economy on the marine ecosystem. This research provides more insight into the increase of HABs and will aid their management in the Beibu Gulf