Foreign direct investment and entrepreneurship: Does the role of institutions matter?

Development economics, international business, and entrepreneurship literature suggest that foreign direct investment (FDI) has significant positive spillover effects for entrepreneurial activities of host economies. However, the findings of past research are mixed, and they do not always confirm this suggestion. We argue that the reason for conflicting findings may be because of an incomplete understanding of the factors that influence the FDI-entrepreneurship nexus in different contexts. Previous studies have carried out only limited exploration of the contingencies in the FDI and domestic entrepreneurship relationship that may depend on the host country’s institutional capacity. We argue that not all countries can reap the rewards from FDI equally. Rather, we hypothesize that countries need to have a sufficient degree of institutional capacity relevant to specific conditions and appropriate threshold levels to successfully capture the positive spillover effects of FDI on domestic entrepreneurship. Utilizing panel data from 2006 to 2016 for 97 emerging markets, developing and developed countries (at different income levels), and a System Generalized Method of Moments (SGMM) estimator that controls for instrument proliferation in dealing with endogeneity problems, we test this hypothesis. We find that FDI has a negative (crowding-out) effect on domestic entrepreneurship at below-threshold levels of institutional capacity, and a positive (crowding-in) effect at above-threshold levels of institutional capacity. The crowding-out effect diminishes as the institutional capacity changes or improves to meet mutating economic environment conditions. Our findings are robust across a wide range of aggregate and disaggregate measures of different types of institutions and alternative empirical strategies.     

近郊乡村规划建设探讨

随着中国城市化进程的加快,乡村规划也逐步开展起来,特别是近郊的乡村规划建设显得尤为活跃,本文结合北京郊区乡村规划规划建设进行探讨。     

机场场面飞机滑行调度优化问题的MILP模型及算法

本文考虑了顺序约束、安全约束、起始与终止时间约束和速度约束四种约束,建立了以总滑行时间最小为目标函数的机场场面飞机滑行调度优化的MILP模型。该模型求解分解为两步,先用遗传算法求解各航班经过交叉点的顺序,然后再求解各航班到达各节点的时间。最后结合国内某枢纽机场的航班信息,对模型进行仿真实验,结果表明本文提出的MILP模型有效解决了滑行冲突。     

BAYESIAN STATE SPACE MODELS IN MACROECONOMETRICS

State space models play an important role in macroeconometric analysis and the Bayesian approach has been shown to have many advantages. This paper outlines recent developments in state space modelling applied to macroeconomics using Bayesian methods. We outline the directions of recent research, specifically the problems being addressed and the solutions proposed. After presenting a general form for the linear Gaussian model, we discuss the interpretations and virtues of alternative estimation routines and their outputs. This discussion includes the Kalman filter and smoother, and precision-based algorithms. As the advantages of using large models have become better understood, a focus has developed on dimension reduction and computational advances to cope with high-dimensional parameter spaces. We give an overview of a number of recent advances in these directions. Many models suggested by economic theory are either non-linear or non-Gaussian, or both. We discuss work on the particle filtering approach to such models as well as other techniques that use various approximations – to either the time state and measurement equations or to the full posterior for the states – to obtain draws.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Non-Gaussian models for CoVaR estimation

In this paper we show how to obtain estimates of CoVaR based on models that take into consideration some stylized facts about multivariate financial time series of equity log returns: heavy tails, negative skew, asymmetric dependence, and volatility clustering. While the volatility clustering effect is captured by AR-GARCH dynamics of the Glosten-Jagannathan-Runkle (GJR) type, the other stylized facts are explained by non-Gaussian multivariate models and copula functions. We compare the different models in the period from January 2007 to March 2020. Our empirical study conducted on a sample of listed banks in the euro area confirms that, in measuring CoVaR, it is important to capture the time-varying dynamics of the volatility. Additionally, a correct assessment of the heaviness of the tails and of the dependence structure is needed in the evaluation of this systemic risk measure.     

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.     

创新政策与科研结构双重影响下高校科技创新绩效研究

我国高校科技创新政策与科研结构不协调问题影响了高校创新绩效。基于2004—2019年中国内地27个省份高校面板数据，运用路径模型、分位数回归模型和双向固定效应模型，探究创新政策与科研结构对高校科技创新绩效的影响。结果表明，高校创新政策与科研结构对创新绩效具有正向影响。结合科研产出与成果转化两个阶段，创新政策能够显著促进科研产出绩效提升，但对科技成果转化绩效的影响表现出一定的非线性特征。其中，创新政策对科技成果转化绩效具有负向影响，而科研产出绩效在两者之间发挥中介作用，说明存在一定程度的科技成果隐性转化。科研结构对创新绩效的推动作用存在分异，集中作用于科研产出阶段，说明教学与科研之间的一致性和竞争性依然存在。据此，提出应进一步发挥创新政策对科技成果转化目标的正向引导作用，在政策层面关注科技成果隐性转化，强化科研方向转移与创新要素配置的作用。