Facing with increasing competition, many ports are taking innovative approaches to improve productivity and profits. Among a variety of methods, introducing external terminal operators(ETOs) while the port acts as a landlord to collect "rents" from those operators for conducting terminal activities inside the port is regarded as an effective way and is commonly observed in practices.This paper analyzes the decisions of two competing ports about whether to introduce their respective ETOs using a three-stage non-cooperative game model when there already exists a port's self-operation terminal operator(STO). At the first stage, the two ports simultaneously decide whether to introduce an ETO. If a port decided to introduce an ETO, at the second stage, it will further decide the unit fee to charge from the ETO. At the third stage, the two ports and the introduced ETO(s) simultaneously decide their respective profit-maximizing output levels. The findings indicate the equilibrium is both ports will introduce their respective ETOs, but when the ports and ETOs are substitutable in the sense of having the same level of productivity, the equilibrium solution is only one port should introduce an ETO. And simulation results show that even if one of ETOs is inefficient this still hold true. 相似文献
Since 2010, Chinese government has introduced a series of administrative policies to limit speculation in the housing market to stabilize price fluctuations and keep the housing market in a healthy state of development. In order to investigate whether administrative policy can play its due role, this paper constructs a comprehensive bottom-up housing market heterogeneous households multiagent model(HHMAM) to undertake research on the differentiated effect of administrative policy in different cities. The empirical studies find that: 1) Administrative policy that increases interest rates will cause housing prices to continue to decline in the long term, but they will resume a rising trend after reaching the lowest point; 2) If the government cancels a property-purchasing limitation, housing prices will continue to rise; and 3) investors tend to invest in 1~(st)-tier cities due to the high demand and greater likelihood of appreciation in these cities. 相似文献
Linear regression models for interval-valued data have been widely studied. Most literatures are to split an interval into two real numbers, i.e., the left- and right-endpoints or the center and radius of this interval, and fit two separate real-valued or two dimension linear regression models. This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix, based on a generalized linear model for interval-valued data. A three step estimation method is proposed. Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.
Financial distress prediction (FDP) has been widely considered as a promising approach to reducing financial losses. While financial information comprises the traditional factors involved in FDP, nonfinancial factors have also been examined in recent studies. In light of this, the purpose of this study is to explore the integrated factors and multiple models that can improve the predictive performance of FDP models. This study proposes an FDP framework to reveal the financial distress features of listed Chinese companies, incorporating financial, management, and textual factors, and evaluating the prediction performance of multiple models in different time spans. To develop this framework, this study employs the wrapper-based feature selection method to extract valuable features, and then constructs multiple single classifiers, ensemble classifiers, and deep learning models in order to predict financial distress. The experiment results indicate that management and textual factors can supplement traditional financial factors in FDP, especially textual ones. This study also discovers that integrated factors collected 4 years prior to the predicted benchmark year enable a more accurate prediction, and the ensemble classifiers and deep learning models developed can achieve satisfactory FDP performance. This study makes a novel contribution as it expands the predictive factors of financial distress and provides new findings that can have important implications for providing early warning signals of financial risk. 相似文献