Modelling the housing market in OECD countries

Recent episodes of housing bubbles, which occurred in several economies after the burst of the United States housing market, suggest studying the evolution of housing prices from a global perspective. We utilise a theoretical model for the purposes of this contribution, which identifies the main drivers of housing price appreciation, such as, for example, income, residential investment, financial elements, fiscal policy and demographics. In a second stage of our analysis, we test our theoretical hypothesis by means of a sample of 18 OECD countries from 1970 to 2011. We employ the vector error correction econometric technique in terms of our empirical analysis, which permits us to model the long-run equilibrium relationship and the short-run dynamics, which also helps to account for endogeneity and reverse causality problems.     

The effect of exchange rate changes on trade balances in the short and long run

Using generalized impulse response functions, this study tests for the trade J‐curve for three transitional central European countries – the Czech Republic, Hungary, and Poland – in their bilateral trade with respect to Germany. Our findings suggest that for each country there are some characteristics associated with a J‐curve effect: after a (real or nominal) depreciation the export‐to‐import ratio briefly drops to below its initial value within a few months and then rises to a long run equilibrium value higher than the initial one.     

对外直接投资影响我国创新能力的机制与实证研究

利用2003-2015年我国内地30个省份的省际面板数据,在概述对外直接投资影响一国创新能力作用机制的基础上,借鉴Lichtenberg和Pottelsberghe提出的国际研发溢出测算方法,从技术差距视角出发,运用面板门槛模型实证检验二者间的关系。结果显示：对外直接投资可以通过R&D成果反馈效应、并购整合效应以及产业前后向关联效应提升一国创新能力;在不同的门槛取值区间内,OFDI逆向技术溢出对创新能力提升的作用呈显著倒U型特征,只有在最优技术差距区间内才能发挥其最大效应。最后,在作用机制概述和实证研究结论基础上,从政府和企业两个层面提出政策建议。     

Experimental evidence on the ‘insidious’ illiquidity risk

This paper introduces an experiment aiming to investigate the contribution of illiquidity risk to the total risk of a collective investment project. If implemented, the project succeeds with a known probability. Yet the project fails if the quota of investors is not reached in the first place. Hence strategic uncertainty compounds its effect with the “intrinsic risk” of the project. Results confirm the insidious nature of illiquidity: as long as a first collective default does not occur, investors accept high intrinsic risk projects. After a first default, they become extremely prudent and come back to market only gradually. After several defaults, private agents manage to coordinate on a relatively low intrinsic risk above which they refuse to participate in the project. Macroeconomic policy implications follow.     

The price relationship between organic and non-organic vegetables in the U.S.: evidence from Nielsen scanner data

This study investigates price relationships between organic and conventional carrots, tomatoes, and lettuce in the U.S. utilizing Nielsen scanner data from 2006–2015. We employ a threshold vector error correction model (TVECM), threshold vector autoregressive model (TVAR), and threshold cointegration test to test whether market integration exists between organic and conventional vegetables as well as the existence of asymmetric price transmission. The results find positive long-run relationships between organic and conventional prices of carrots and tomatoes and show the existence of asymmetric price transmission in price pairs of lettuce and tomatoes. Our findings suggest that the price relationship between organic and conventional vegetables varies by characteristics, such as shelf life, volatility in the price premium, and substitutability.     

Is there a role for uncertainty in forecasting output growth in OECD countries? Evidence from a time-varying parameter-panel vector autoregressive model

This paper uses a time-varying parameter-panel vector autoregressive (TVP-PVAR) model to analyze the role played by domestic and US news-based measures of uncertainty in forecasting the growth of industrial production of 12 Organisation for Economic Co-operation and Development (OECD) countries. Based on a monthly out-of-sample period of 2009:06 to 2017:05, given an in-sample of 2003:03 to 2009:05, there are only 46% of cases where domestic uncertainty can improve the forecast of output growth relative to a baseline monetary TVP-PVAR model, which includes inflation, interest rate and nominal exchange rate growth, besides output growth. Moreover, including US uncertainty does not necessarily improve the forecasting performance of output growth from the TVP-PVAR model which includes only the domestic uncertainty along with the baseline variables. So, in general, while uncertainty is important in predicting the future path of output growth in the 12 advanced economies considered, a forecaster can do better in majority of the instances by just considering the information from standard macroeconomic variables.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Modeling asymmetric price transmission in the European food market

In this study, we employ a multivariate panel error correction model (PVECM) to investigate asymmetric price transmission among the farm, processor, and retail segments of the European food supply chain for the 2005–2016 period. The results indicate that, in both the long- and short-run, retail prices respond more strongly to processor price increases than decreases and the same occurs for processor prices due to farm price changes. Thus, the findings demonstrate the presence of positive asymmetric price transmission in the European food supply chain. Finally, the results of the present study indicate that the food price pass-through varies greatly across product category and across countries, and that the pass-through to producer prices is greater than that to consumer prices.     

中国能源需求向量自回归模型的建立与分析

随着我国经济的快速发展,对能源需求日益增加,在未来一段时期内,如何科学地预测我国能源需求量,对于保证经济的可持续发展、小康社会目标的实现与和谐社会的构建具有重要的现实意义。向量自回归模型是基于数据的统计性质建立模型,把系统中每一个内生变量作为系统中所有内生变量的滞后值的函数来构造模型,是处理多个相关经济指标的分析和预测最容易操作的模型之一,常用于预测相互联系的时间序列系统。能源需求量是由煤炭、石油、天然气等一次能源的消费量组成,他们之间存在着密切的联系,基于此,应用VAR模型对我国中长期的能源需求总量、煤炭、石油、天然气的消费量进行预测,为科学地制定能源发展战略提供理论指导。     

基于主成分分析和支持向量机的个人信用评估

本文针对信用评估指标维数较高的问题,运用主成分分析与支持向量机理论建立了一个新的个人信用评估预测模型。为反映该模型在信用评估分类方面的优越性,又分别建立了基于神经网络、K近邻判别分析等多种理论的信用评估模型,并用同一组数据对不同的模型分别进行训练,然后比较其预测分类正确率。实验结果表明,基于主成分分析与支持向量机理论的个人信用评估模型具有较优的预测分类正确率。