Forecast Of China Research Articles

An Analysis of the Trend of China’s Export Trade to the USA Based on R Language

After President Trump came to power, in order to change the “imbalance” between China and US trade, he launched a trade war with China, which led to increase uncertainty in China-US trade and increased export volatility. Based on R language environment, this paper compares the advantages and disadvantages of seasonal ARIMA (p, d, q) model and double-index ETS (A, N, A) model in short-term forecast of China’s total export value to the United States. Then, the double-index ETS (A, N, A) model is selected to predict the trend of China’s export trade to the United States in the months of 2009-2020. The forecast results show that China’s export to the United States has seasonal characteristics. The export fluctuation is smaller than that in 2018, but the total value of exports has decreased significantly. Finally, some suggestions are put forward.

Carbon trading volume and price forecasting in China using multiple machine learning models

Motivated by reducing carbon emissions, carbon trading market have been opened to promote environmental protection. Accurate carbon trading volume and price forecasts have far-reaching implications for environmental and energy policy formulation. As the country with the most massive carbon emissions in the world, China’s carbon price trends, carbon trading volume trends, and policy orientation are of great significance. As matters stand, there are few studies on carbon price and trading volume forecasts, and no research paper has covered carbon price and trading volume forecasts for all carbon trading markets in China. In order to provide a relatively complete reference for the carbon price and trading volume in China, this paper uses six machine learning models to predict the daily carbon price and trading volume of eight carbon markets in China, including Beijing, Shenzhen, Guangdong, Hubei, Shanghai, Fujian, Tianjin, Chongqing. The prediction models include extreme gradient boosting, random forest, kernel-based nonlinear extension of the Arps decline model optimized by grey wolf optimizer (GWO-KNEA), support vector machine optimized by particle swarm optimizer, support vector machine optimized by fruit fly optimizer and simulated annealing algorithm, and radial basis function neural network (RBFNN). Moreover, an advanced data denoising method, i.e., complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is used in the models to smooth the raw data. Because the eight carbon trading markets have different carbon price and trading volume distribution characteristics in time series, by comparing the prediction results, more suitable models for the specific carbon market can be obtained. The prediction results show that CEEMDAN-GWO-KNEA and CEEMDAN-RBFNN are more competitive in predicting carbon prices and trading volume in China because of their best performance in many datasets. The average accuracy of CEEMDAN-RBFNN and CEMDAN-GWO-KNEA in carbon price prediction is 98.40% and 97.89%, respectively. The predictive stability of each model in different application scenarios is also discussed. The results show that high prediction accuracy does not mean better prediction stability, but CEEMDAN-RBFNN and CEMDAN-GWO-KNEA can still guarantee high stability in most datasets.

THE FORECASTING OF POPULATION SUPER-AGING AND POLICY SIMULATION OF LONG-TERM CARE IN CHINA (2019-2070)

The purpose of this paper is to provide a policy simulation of Long-Term Care based on the aging population forecast for China, focusing on the super-aging and oldest-old segments of the population. As a developing country, it is a challenge for China to increase its wealth with the financial implications of an aging nation. The study identified two significant turning points for population development: 1) The multi-pilot program of long-term care policy reform must be executed between 2016 – 2020. This reform is time dependent because the multi-state longevity development period is from 2016 to 2030 and in the year 2030, the oldest-old population will be part of China’s society. 2) The year 2060 will be representative of a stable period in China, as the age of China’s population will no longer be accelerating.

Evaluating the Impact of Fossil Fuel Vehicle Exit on the Oil Demand in China

Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply, among other matters. The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts. This study fills this gap in forecasting economic growth and inflation in China, by using the rolling weighted least squares (WLS) with the practically feasible cross-validation (CV) procedure of Hong et al. (2018) to choose an optimal estimation window. We undertake an empirical analysis of monthly data on up to 30 candidate indicators (mainly asset prices) for a span of 17 years (2000–2017). It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows. The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases. One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms, policies, crises, and other factors. Furthermore, we find that, in most cases, asset prices are key variables for forecasting macroeconomic variables, especially output growth rate.

The impact of multi-species surface chemical observation assimilation on air quality forecasts in China

Abstract. An ensemble Kalman filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3, and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3, and CO concentrations during an extreme haze episode that occurred in early October 2014 over East Asia. Numerical experimental results indicate that ICs played key roles in PM2.5, PM10 and CO forecasts during the severe haze episode over the North China Plain. The 72 h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near-perfect verification forecasts results were achieved. However, with longer-range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72 h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for the first 3 h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72 h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

Roadmap for earthquake numerical forecasting in China—Reflection on the tenth anniversary of Wenchuan earthquake

Roadmap for earthquake numerical forecasting in China—Reflection on the tenth anniversary of Wenchuan earthquake

Forecast of China’s natural gas consumption using mathematical models

ABSTRACTBased on the statistical data, natural gas consumption of China from 1995 to 2011, Boltzmann model and a third-order polynomial curve model are employed to fit the historical data, which aim to explore the rule of the past variation tendency and find out the proper model for forecasting. Fitting results demonstrate that a combination model based on the Boltzmann model and a third-order polynomial curve model can show great fitting result and its residual sum of squares and mean absolute percentage error are remarkably less than those of any previous models. According to the combined model, the average annual growth rate of the consumption in the next 7 years is 16.27% from 2012 to 2018.

2015–16 floods and droughts in China, and its response to the strong El Niño

The features of hydro-climate anomalies in China in 2015–2016 were analyzed in great detail, together with possible responses to the super 2015–16 El Niño event. The 2015–16 El Niño is characterized as a “strong” event in terms of the duration, intensity, and coverage of warming sea surface temperature (SST) in the central and east-central equatorial Pacific in comparison to the 1982–83 and 1997–98 events. The floods and droughts frequency were incidence of floods and droughts per year, respectively. The results show several significant anomalies in China: 1) About 9%–173% of precipitation variance in 2015–16 can be attributed to this El Niño; 2) There was significant inconformity between hydro-climate anomalies and the occurrence of floods and droughts; 3) Flood frequency has increased, especially over Southeast China and the Yangtze River in the summer of 2016; 4) Drought frequency has also increased, especially over Northeast China in summer of 2015, Northwest China in spring of 2016, and most parts in winter of 2015. The response of China hydro-climate anomalies to the 2015–16 El Niño was significant via El Niño and warm Indian Ocean induced circulation anomalies, which were characterized by stronger and more westward-extending western Pacific subtropical high and anomalous water vapor transport. Knowledge of the response of hydro-climate extremes to El Niño can provide valuable information to improve flood and drought forecasting in China.

Directional Accuracy of Urban Consumers’ Inflation Forecasts in China

We evaluate the directional accuracy of inflation forecasts based on the survey data of urban savings account holders in China. By using a new market-timing test, we show that the urban consumers’ expectations of inflation are not a useful predictor of the overall consumer price index (CPI) and the urban household CPI (U-CPI) in China. However, after our in-depth analysis using the inflation rate of each category in the U-CPI basket, we find that the consumers’ forecasts are useful in predicting the movement of the residence component in the U-CPI basket since the third quarter of 2009.

Evaluation of Real‐Time Convection‐Permitting Precipitation Forecasts in China During the 2013–2014 Summer Season

AbstractForecasts at a 4 km convection‐permitting resolution over China during the summer season have been produced with the Weather Research and Forecasting model at Nanjing University since 2013. Precipitation forecasts from 2013 to 2014 are evaluated with dense rain gauge observations and compared with operational global model forecasts. Overall, the 4 km forecasts show very good agreement with observations over most parts of China, outperforming global forecasts in terms of spatial distribution, intensity, and diurnal variation. Quantitative evaluations with the Gilbert skill score further confirm the better performance of the 4 km forecasts over global forecasts for heavy precipitation, especially for the thresholds of 100 and 150 mm d−1. Besides bulk characteristics, the representations of some unique features of summer precipitation in China under the influence of the East Asian summer monsoon are further evaluated. These include the northward progression and southward retreat of the main rainband through the summer season, the diurnal variations of precipitation, and the meridional and zonal propagation of precipitation episodes associated with background synoptic flow and the embedded mesoscale convective systems. The 4 km forecast is able to faithfully reproduce most of the features while overprediction of afternoon convection near the southern China coast is found to be a main deficiency that requires further investigations.

Analysis and Forecast of China’s Grain Supply and Demand Quality Grade Structural Balance

Based on the analysis of China’s grain production and demand quality grade, this paper uses the non-equidistance GM (1,1) model and the inference algorithm to predict the structural balance of the supply and demand grades of China’s four major grain crops. The results show that the supply and demand for wheat, corn and rice can maintain a quantitative balance. While, it’s difficult to achieve a quality grade balance in wheat and rice and keep a supply and demand balance in terms of quantity and quality of soybean. Simultaneously, the supply of premium grade corn is greater than the demand. The analysis and prediction results can reflect the existing structural balance matter of grain supply and demand at different quality grades in China, and provide theoretical basis for governments to formulate relevant policies.

Research of Coal Resources Reserves Prediction Based on GM (1, 1) Model

Based on the forecast of China’s coal reserves, this paper uses the GM (1, 1) gray forecasting theory to establish the gray forecasting model of China’s coal reserves based on the data of China’s coal reserves from 2002 to 2009, and obtained the trend of coal resources reserves with the current economic and social development situation, and the residual test model is established, so the prediction model is more accurate. The results show that China’s coal reserves can ensure the use of production at least 300 years of use. And the results are similar to the mainstream forecast results, and that are in line with objective reality.

Research on the Impacts of Analyst Forecasts on Management Earnings Forecasts—Evidence from China

This paper investigates the effects of analyst forecasts on management earnings forecasts in Chinese forecasting environment. The empirical results show that: The pessimistic forecasts bias encourage listed firms disclose earnings forecasts more timely and accurate to correct the market expectation of earnings; managers will make more précised forecasts when analyst forecasts dispersions are high. The results imply that, analyst forecasts have great impacts on management earnings forecasts, which is not only restricted by government, but also affected by other market participants. This paper sheds new light on the research on the relationship between analyst forecasts and management earnings forecasts in China.

Conditional density forecast of China’s energy demand via QRNN model

ABSTRACTWe propose a new method for conditional density forecast of China’s energy demand through quantile regression neural network (QRNN). This method has at least two advantages. First, it is flexible to explore the true nonlinearity in the energy demand system via neural network structure. Second, it is able to describe the whole conditional distribution of energy demand via quantile regression. In the empirical study on China’s energy demand, QRNN outperforms several classical methods in terms of forecast accuracy both in-sample and out-of-sample. Considering China’s economic and social environment, we set a scenario for predictors and forecast the conditional density of China’s energy demand from 2015 to 2020. The empirical results show that the conditional density curve moves to right and its dispersion increases over time, which indicates that the energy demand in China will keep growing with an average annual rate of 9.672% and its uncertainty is enlarged with 42.210%.

Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast

Urban haze pollution is becoming increasingly serious, which is considered very harmful for humans by World Health Organization (WHO). Haze forecasts can be used to protect human health. In this paper, a Selective ENsemble based on an Extreme Learning Machine (ELM) and Improved Discrete Artificial Fish swarm algorithm (IDAFSEN) is proposed, which overcomes the drawback that a single ELM is unstable in terms of its classification. First, the initial pool of base ELMs is generated by using bootstrap sampling, which is then pre-pruned by calculating the pair-wise diversity measure of each base ELM. Second, partial-based ELMs among the initial pool after pre-pruning with higher precision and with greater diversity are selected by using an Improved Discrete Artificial Fish Swarm Algorithm (IDAFSA). Finally, the selected base ELMs are integrated through majority voting. The Experimental results on 16 datasets from the UCI Machine Learning Repository demonstrate that IDAFSEN can achieve better classification accuracy than other previously reported methods. After a performance evaluation of the proposed approach, this paper looks at how this can be used in haze forecasting in China to protect human health.

Improving PM2.5 Air Quality Model Forecasts in China Using a Bias-Correction Framework

Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well.

Do Business Relationships Affect the Accuracy of Analyst Earnings Forecasts? Evidence from China

AbstractSupporting the “conflicts of interest hypothesis,” we show that, in China, better‐informed analysts issue more optimistically biased forecasts and the reputation of financial analysts mitigates the bias. We contribute to the literature by showing that such an adverse information effect varies over types of investment banking relationships and a better developed local legal environment reduces forecast bias. Our results call for a better developed market mechanism to discipline analysts so as to issue independent and accurate earnings forecasts in China.

Analysis and Forecast of China's Total Economy and Its Structure from 2016–2035

Analysis and Forecast of China's Total Economy and Its Structure from 2016–2035

A dynamic computable general equilibrium simulation of China’s innovation-based economy under the new normal

Researching China’s innovative economic and financial innovation issues under the background of the New Normal, we need to carefully analyze the internal structure and interaction of China’s macroeconomics. The computable general equilibrium (CGE) model has outstanding advantages on predicting the external shock influences on economic system, but previous studies on forecast for China’s future economy mostly considered a high growth rate which is hard to comply with the New Normal scene. By constructing China’s macroeconomic dynamic CGE (DCGE) model and anticipating the economic impact of the New Normal, this paper finds that the New Normal has a certain extent inhibition on China’s macro-economy and innovation. However, after adding the research and development (R&D) subsidy policy, the negative impacts of the New Normal on macro-economy can be eliminated to realize the optimization of economic structure. In addition, after combining the financial innovation promoting policy and the KeQiang index through the simulation of macro-economy, we find that the quality of economic growth is improved. Finally, we provide the policy recommendations for the realization of an innovative economy under China’s New Normal.