Since the first pair of BeiDou satellites was deployed in 2000, China has made continuous efforts to establish its own independent BeiDou Navigation Satellite System (BDS) to provide the regional radio determination satellite service as well as regional and global radio navigation satellite services, which rely on the high quality of orbit and clock products. This article summarizes the achievements in the precise orbit determination (POD) of BDS satellites in the past decade with the focus on observation and orbit dynamic models. First, the disclosed metadata of BDS satellites is presented and the contribution to BDS POD is addressed. The complete optical properties of the satellite bus as well as solar panels are derived based on the absorbed parameters as well the material properties. Secondly, the status and tracking capabilities of the L-band data from accessible ground networks are presented, while some low earth orbiter satellites with onboard BDS tracking capability are listed. The topological structure and measurement scheme of BDS Inter-Satellite-Link (ISL) data are described. After highlighting the progress on observation models as well as orbit perturbations for BDS, e.g., phase center corrections, satellite attitude, and solar radiation pressure, different POD strategies used for BDS are summarized. In addition, the urgent requirement for error modeling of the ISL data is emphasized based on the analysis of the observation noises, and the incompatible characteristics of orbit and clock derived with L-band and ISL data are illuminated and discussed. The further researches on the improvement of phase center calibration and orbit dynamic models, the refinement of ISL observation models, and the potential contribution of BDS to the estimation of geodetic parameters based on L-band or ISL data are identified. With this, it is promising that BDS can achieve better performance and provides vital contributions to the geodesy and navigation.

Targeting the 21 port provinces of China, this paper constructs a panel vector autoregressive (PVAR) model to explore the dynamic relationship between China's green economy and inflation, and to verify whether China's green economy has a negative effect on inflation. Both the global Malmquist-Luenberger (GML) index and the Malmquist-Luenberger (ML) index, which represent the production efficiency under pollution, were adopted to measure China's green economy. It is assumed that the green economy and inflation interact with each other through the output gap, which is associated with pollution, the side-effect of production. The calculated results were tested by impulse response analysis. The final conclusion goes that China's green economy has a weak but long-lasting negative effect on inflation. In other words, China's green economy can alleviate the inflation. Thus, China should speed up pollution control and green economy development to control inflation.

No abstract

To examine whether including economic data on other countries could improve the forecast of U.S. GDP growth, we construct a large data set of 77 countries representing over 90 percent of global GDP. Our benchmark model is a dynamic factor model using U.S. data only, which we extend to include data from other countries. We show that using crosscountry data produces more accurate forecasts during the global financial crisis period. Based on the vintage data on August 6, 2020, our benchmark model forecasts U.S. real GDP growth in 2020Q3 to be-6.9% (year-over-year rate) or 14.9% (quarter-over-quarter annualized rate), whereas the forecast is revised upward to-6.1% (yoy) or 19.1% (qoq) when crosscountry data are used. These examples suggest that U.S. data may fail to capture the spillover effects of other countries in downturns. However, we show that foreign variables are much less useful in normal times.

This paper focused on the sustainability of the impact of the Russia-Ukraine conflict on the military spending of NATO allies. As Russia launched a "special military operation" in Ukraine in February 2022, NATO allies announced their intention to significantly increase military spending in response to the "threat" from Russia. However, under the stimulation of the Russia-Ukraine conflict, it is uncertain whether the NATO defense budget increase spree can be sustained. Based on this, this paper analyzed the sustainability of NATO's military spending increase in the context of the Russia-Ukraine conflict by building a vector autoregressive model (VAR). Through the impulse response analysis, this paper concluded that the Russia-Ukraine conflict shock stimulates NATO allies to increase military spending persistently only for about three years.