产业政策对商业模式创新的影响效应测评研究——以光伏和人工智能上市公司为例

产业政策是政府引导产业发展、调整产业结构失衡的重要工具，影响着企业商业模式创新水平。基于中国大陆A股82家光伏上市公司、52家人工智能上市公司数据，采用清晰集QCA方法，从选择水平、激励水平和介入水平3个维度探索产业政策与其它因素共同引致高/低层次商业模式创新的组合方式。结果表明，产业政策不同属性水平的作用路径不同；高层次和低层次商业模式创新路径并非简单的对称关系，也不是单一条件就能起决定性作用的，还需要依赖不同条件之间的组合；产业政策不同属性对商业模式创新的影响效应存在差异。最后，通过设定商业模式创新评价标准，为优化商业模式创新路径提供可行思路。     

Determining analogies based on the integration of multiple information sources

Forecasting approaches that exploit analogies require the grouping of analogous time series as the first modeling step; however, there has been limited research regarding the suitability of different segmentation approaches. We argue that an appropriate analytical segmentation stage should integrate and trade off different available information sources. In particular, it should consider the actual time series patterns, in addition to the variables that characterize the drivers behind the patterns observed. The simultaneous consideration of both information sources, without prior assumptions regarding the relative importance of each, leads to a multicriteria formulation of the segmentation stage. Here, we demonstrate the impact of such an adjustment to segmentation on the final forecasting accuracy of the cross-sectional multi-state Kalman filter. In particular, we study the relative merits of single and multicriteria segmentation stages for a simulated data set with a range of noise levels. We find that a multicriteria approach consistently achieves a more reliable recovery of the original clusters, and this feeds forward to an improved forecasting accuracy across short forecasting horizons. We then use a US data set on income tax liabilities to verify that this result generalizes to a real-world setting.     

如何共舞?服务商业模式创新与技术创新对企业绩效的交互影响

基于权变视角与企业网络视角，剖析效率型、新颖型服务商业模式创新与利用式、探索式技术创新匹配对制造企业绩效的影响效应，并识别网络中心性的中介作用。研究发现：①新颖型服务商业模式创新与利用式技术创新组合匹配能够积极提升企业绩效，效率型、新颖型服务商业模式创新与探索式技术创新组合匹配对企业绩效具有抑制作用；②效率型、新颖型服务商业模式创新，利用式、探索式技术创新均能显著提升企业网络中心性和企业绩效；③网络中心性在服务商业模式创新对企业绩效的影响中发挥完全中介作用。     

Using Artificial Neural Networks to forecast Exchange Rate,including VAR‐VECM residual analysis and prediction linear combination

The Euro US Dollar rate is one of the most important exchange rates in the world, making the analysis of its behavior fundamental for the global economy and for different decision‐makers at both the public and private level. Furthermore, given the market efficiency of the EUR/USD exchange rate, being able to predict the rate's future short‐term variation represents a great challenge. This study proposes a new framework to improve the forecasting accuracy of EUR/USD exchange rate returns through the use of an Artificial Neural Network (ANN) together with a Vector Auto Regressive (VAR) model, Vector Error Corrective model (VECM), and post‐processing. The motivation lies in the integration of different approaches, which should improve the ability to forecast regarding each separate model. This is especially true given that Artificial Neural Networks are capable of capturing the short and long‐term non‐linear components of a time series, which VECM and VAR models are unable to do. Post‐processing seeks to combine the best forecasts to make one that is better than its components. Model predictive capacity is compared according to the Root Mean Square Error (RMSE) as a loss function and its significance is analyzed using the Model Confidence Set. The results obtained show that the proposed framework outperforms the benchmark models, decreasing the RMSE of the best econometric model by 32.5% and by 19.3% the best hybrid. Thus, it is determined that forecast post‐processing increases forecasting accuracy.     

Bank lending margins in the euro area: Funding conditions,fragmentation and ECB's policies

In this paper we study the determinants of lending margins paid by euro‐area corporates for their bank loans. Across two separate groups of countries (distressed and non‐distressed) we examine whether lending margins have been affected by structural changes in the banking sector, the credit and liquidity position of banks and the costs of funding in the corporate and sovereign bond markets. The role of ECB policies with respect to narrowing down the fragmentation in the bank lending channel is also investigated through a structural panel VAR model for the period 2003:1 to 2014:12.     

External Shocks and Business Cycle Fluctuations in Mongolia: Evidence from a Large Bayesian VAR

This paper examines macroeconomic effects of external shocks and their transmission mechanisms in one of the most commodity-abundant countries-Mongolia using a large Bayesian vector autoregression (BVAR) based on the approach proposed by Bańbura, Giannone, and Reichlin [(2010). Large Bayesian Vector Auto Regressions. Journal of Applied Econometrics, 25, 71–92]. Nine structural shocks (five external and four domestic shocks) are identified using a recursive ordering. Results show that external shocks are important sources of macroeconomic volatility in Mongolia. Commodity price shocks affect the economy through exchange rate and budget expenditure channels, while China’s growth and FDI shocks are primarily transmitted through the real sector and bank lending channels.     

金融稳定是否应纳入中国货币政策目标——基于混频IS-Phillips模型的实证分析

文章首先使用混频动态因子模型（MF-DFM），构建中国首个混频金融稳定指数（MF-FSI），接着把MF-FSI作为金融稳定的代理变量，使用文章新构建的混频IS-Phillips模型，比较分析纳入与不纳入金融稳定的中国货币政策损失函数差异，最后对货币政策目标选择和时效选择进行了敏感性分析，实证分析表明：（1）中国混频金融稳定指数是金融稳定的一个实时性有效测度指标；（2）中国货币政策目标应纳入金融稳定，以减少货币政策福利损失；（3）无论中国货币政策目标偏好和预期偏好怎样变化，央行都应给予金融稳定固定且不可忽略的关注度，但物价稳定和经济增长仍为主要货币政策目标。     

International propagation of shocks: A dynamic factor model using survey forecasts

This paper studies the pathways for the propagation of shocks across the G7 and major Asia-Pacific countries using multi-horizon forecasts of real GDP growth from 1995 to 2017. We show that if the forecasts are efficient in the long run, the results obtained using these forecasts are comparable to those obtained from the actual outturns. We measure global business cycle connectedness and study the impacts of both country-specific shocks and common international shocks using a panel factor structural VAR model. Our results suggest that there is a strong convergence of business cycles within the group of industrialized countries, as well as within the group of developing economies during non-recessionary periods. In particular, we find an increased decoupling between the industrialized and developing economies after the 2008 recession. However, the direction of shock spillovers during recessions and other crisis periods varies, depending on the nature and origin of the episode.     

Dynamic portfolio optimization across hidden market regimes

Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational advantages to using MPC when estimates of future returns are updated every time a new observation becomes available, since the optimal control actions are reconsidered anyway. MPC outperforms a static decision rule for changing the allocation and realizes both a higher return and a significantly lower risk than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces the number of trades is found to increase the robustness of the approach.     

Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives

Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to the selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality (“partially-egalitarian LASSO”). Ex post analysis reveals that the optimal solution has a very simple form: the vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures that are motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require the choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts; indeed, they perform approximately as well as the ex post best forecaster.