论混合市场及其最优均衡

本从制度经济学角度，对市场的经济内涵作了创新性的分析，指出市场是“公”权市场和“私”权市场的混合体，在此基础上分析传统市场的形态和现代混合市场的发展方向，最后对混合市场的最优均衡作探索性的分析。     

Public preferences for post 2020 agri-environmental policy in the Czech Republic: A choice experiment approach

In November 2017 European Union commission presented a communication report summarizing the reform proposal of the post 2020 Common Agriculture Policy (CAP). The reform aims to address the environmental degradation associated with agricultural production as well as change in the structure of CAP payments. To this end, the Ministry of Agriculture in Czech Republic is preparing to set its priorities towards CAP’s reform. In this study we applied a choice experiment to investigate the public preferences for a set of environmental goods and services delivered by agri-environment-climatic voluntary measures (AECMs). A mixed logit model is employed to elicit preferences and explore their heterogeneity. We find that respondents oppose strongly funding removal. Among environmental attributes, water and food quality are the ones with the highest implicit marginal willingness-to-pay values. Preferences for no funding option are heterogeneous with socio-demographic and attitudinal variables explaining some sources of this heterogeneity. A continuation of national funding for the AECMs is expected to lead to a better state of environment with an anticipated positive welfare change of 669–932 mil EUR as opposed to funding removal. The change reflects the estimated welfare change resulting from moving from a low to a medium or to a high preservation state of agri-environmental attributes. We also project the budget change for AECMs considering the level of national funding and given the transfer share between Pillar I and II. Based on our results, we suggest that national funding can be informed by the welfare change scenarios and transfer shares are projected accordingly.     

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of low-frequency time series variables through the use of timely information from high-frequency variables. We verify the in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation and GDP growth. In particular, we forecast monthly headline inflation using daily oil prices and quarterly GDP growth using a measure of financial risk. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of both point and density forecasts.     

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。     

Would you really recommend it? Antecedents of word-of-mouth in medical tourism

Medical tourism (MT) is a valuable component of many national service economies. Understanding the marketing and recruitment of MT patients is therefore an important consideration for MT providers. Research shows that word of mouth (WoM) remains the most important acquisition channel in this sector. Yet, there is only a limited understanding of antecedent factors behind a patient referral. We develop a framework for WoM, finding support for tourism factors, service quality and perceived value as key antecedents on WoM referrals. We further extend the MT literature by integrating a novel perspective on value creation that surfaces the experiences of frontline service providers for the first time. This paper incorporates two complementary studies, one with a focus on value creation, the other on perceived value. By combing the two in a mixed-methods approach, we emphasise the role of service delivery on WoM. Several implications can be drawn from the study's findings.     

Traveling with pets: Constraints,negotiation, and learned helplessness

Pets have become increasingly important companions for modern families in daily life and during vacations. Yet traveling with pets involves various uncertainties and constraints that can inhibit tourists' participation. Drawing upon the constraint-negotiation model, this two-step, mixed-methods study examines how these constraints may influence pet owners' travel intentions and how such barriers can be overcome. A qualitative investigation was first conducted based on online user-generated content from 71 Chinese bloggers. Thematic analysis informed the development of an extended constraint-negotiation framework featuring learned helplessness. The extended model was then tested using survey data from 615 Chinese pet owners. Findings suggest that while perceived constraints can inhibit traveling with pets through increased learned helplessness, negotiation can reduce helplessness and encourage pet owners’ tourism participation. Implications and limitations are also discussed.     

Mixed data sampling expectile regression with applications to measuring financial risk

To avoid information loss or measurement error in traditional methods dealing with mixed frequency data, we develop a novel mixed data sampling expectile regression (MIDAS-ER) model to measure financial risk. We construct the MIDAS-ER model by introducing a MIDAS structure into expectile regressions. This enables us to perform an expectile regression on raw mixed frequency data directly. We apply the proposed MIDAS-ER model to estimate two popular financial risk measures, namely, Value at Risk and Expected Shortfall, with both simulated data and four stock indices, and compare the model's performance with those of several popular models. The outstanding performance of our model demonstrates that high-frequency information helps to improve the accuracy of risk measurement. In addition, the numerical results also imply that our model can be a significant tool for risk-averse investors to control risk losses and for financial institutions to implement robust risk management.     

The visual impact of agricultural sheds on rural landscapes: The willingness to pay for mitigation solutions and treatment effects

New rural and industrial sheds cause significant transformations determining a negative change in landscape perception. In this paper the willingness to pay (WTP) of residents to mitigate the visual impact of sheds in the rural areas of the UNESCO World Heritage Site “Vineyard Landscape of Piedmont: Langhe-Roero and Monferrato” is estimated through a Choice Experiment (CE). The CE survey was carried out through face-to-face interviews involving 400 residents. The set of mitigation attributes was selected by means of a landscape analysis and presented to respondents using a set of images (close and distant views). Conditional Logit (CLogit) and Mixed Logit (MXL) models both in preference and in WTP space were estimated. From the empirical point of view, the study shows that sheds are generally perceived as negative landscape elements, and that residents are willing to pay for mitigation solutions, such as tree lines and formal hedgerows. The results are relevant for policy makers since they suggest how agricultural sheds are perceived and which mitigation strategies are preferred by local residents. From the methodological point of view, we estimate and test different models for assessing the effect of the visual treatment (close vs distant view). We show the theoretical equivalence of a fully interacted model (i.e., estimated on the whole sample and including interaction terms with the treatment), with split models of the choice for separate subsamples submitted or not to the treatment. We find that in estimation the equivalence holds in the results of Conditional Logit models, but not in Mixed Logit models. The reasons and implications are discussed.     

Structural analysis with mixed-frequency data: A model of US capital flows

We develop a new structural Vector Autoregressive (SVAR) model for analysis with mixed-frequency data. The MIDAS-SVAR model allows to identify structural dynamic links exploiting the information contained in variables sampled at different frequencies. It also provides a general framework to test homogeneous frequency-based representations versus mixed-frequency data models. A set of Monte Carlo experiments suggests that the test performs well both in terms of size and power. The MIDAS-SVAR is then used to study how monetary policy and financial uncertainty impact on the dynamics of gross capital inflows to the US. While no relation is found when using standard quarterly data, mixed frequency analysis exploiting the variability present in the series within the quarter shows that the effect of an interest rate shock is greater the longer the time lag between the month of the shock and the end of the quarter.     

FORTRAN语言及汇编语言在坐标测量中的混合编程技术

本文论述了Fortran在坐标测量数据采集系统以及图形输出系统中与汇编语言的程序连接及调用方法,讨论了利用Fortran语言进行测量样本点的拟合、画线以及图形输出问题,给出了编程方法及实例。