Sparse estimation of dynamic principal components for forecasting high-dimensional time series

We present the sparse estimation of one-sided dynamic principal components (ODPCs) to forecast high-dimensional time series. The forecast can be made directly with the ODPCs or by using them as estimates of the factors in a generalized dynamic factor model. It is shown that a large reduction in the number of parameters estimated for the ODPCs can be achieved without affecting their forecasting performance.     

Limitations of portfolio diversification through fat tails of the return Distributions: Some empirical evidence

This study investigates the level of risk due to fat tails of the return distribution and the changes of tail fatness (TF) through portfolio diversification. TF is not eliminated through portfolio diversification, and, interestingly, the positive tail has declining fatness until a certain level is reached, while the negative tail has rising fatness. This indicates that fat tails are highly relevant to common factors on systematic risk and that the relevance of common factors is higher for the negative tail compared to the positive tail. In the portfolio diversification effect, the declining fatness of the positive tail further reduces risk, but the rising fatness of the negative tail does not contribute to this effect. The asymmetry between the fatness of the positive and negative tails in the return distribution corresponds to the asymmetry of the trade-off relationship between loss avoidance and profit sacrifice that is expected as a consequence of portfolio diversification. Investors use portfolio diversification to reduce their risk of suffering high losses, but following this strategy means sacrificing high-profit potential. Our study provides empirical confirmation for the practical limitation of portfolio diversification and explains why investors with diversified portfolios suffer high losses from market crashes. An examination of the Northeast Asian stock markets of China, Japan, Korea, and Taiwan show identical results.     

Random Effects Misspecification Can Have Severe Consequences for Random Effects Inference in Linear Mixed Models

There has been considerable and controversial research over the past two decades into how successfully random effects misspecification in mixed models (i.e. assuming normality for the random effects when the true distribution is non‐normal) can be diagnosed and what its impacts are on estimation and inference. However, much of this research has focused on fixed effects inference in generalised linear mixed models. In this article, motivated by the increasing number of applications of mixed models where interest is on the variance components, we study the effects of random effects misspecification on random effects inference in linear mixed models, for which there is considerably less literature. Our findings are surprising and contrary to general belief: for point estimation, maximum likelihood estimation of the variance components under misspecification is consistent, although in finite samples, both the bias and mean squared error can be substantial. For inference, we show through theory and simulation that under misspecification, standard likelihood ratio tests of truly non‐zero variance components can suffer from severely inflated type I errors, and confidence intervals for the variance components can exhibit considerable under coverage. Furthermore, neither of these problems vanish asymptotically with increasing the number of clusters or cluster size. These results have major implications for random effects inference, especially if the true random effects distribution is heavier tailed than the normal. Fortunately, simple graphical and goodness‐of‐fit measures of the random effects predictions appear to have reasonable power at detecting misspecification. We apply linear mixed models to a survey of more than 4 000 high school students within 100 schools and analyse how mathematics achievement scores vary with student attributes and across different schools. The application demonstrates the sensitivity of mixed model inference to the true but unknown random effects distribution.     

Non-linear exchange rate relationships: An automated model selection approach with indicator saturation

This paper examines whether the explanatory power of exchange rate models can be improved by allowing for cross-country asymmetries and non-linear effects of fundamentals. Both appear to be crucial. The samples include the USD versus pound and yen from 1982:10 to 2013:10, and automated model selection is conducted with indicator saturation. Several non-linear effects are significant at 1%. Further, many of the indicators present in the linear models are eliminated once allowing for non-linearities; suggesting some of the structural breaks found in previous work were an artifact of the misspecified linear functional form. These conclusions are robust to estimation using principal components.     

零部件拆装及测绘实践

针对工程制图的课程特点,简要介绍在建构主义教育理念的基础上,进行的零部件拆装实践教学。在教学内容上,给定真实的拆装与测绘环境,用逆向设计模式,画出相应的零部件装配图和零件图。教学手段上加强了数字化教学资源的建设。教学方法上,更注重于以项目为载体,理论教学与实践教学相结合。     

Financial components of FDI and choice of capital structure: an analysis for 30 OECD countries

Even though the choice of capital structure depends on the three different financial components of foreign direct investment (FDI), previous research has regarded FDI as unidimensional rather than multidimensional. This study addresses new findings in the FDI area and investigates the relevant determinants of capital structure in 30 OECD countries from 2006 to 2014 within the framework of a simultaneous equation model. Our primary findings reveal that each component has its own deterministic features driven by relevant policy variables and risks in the market. While an increase or decrease in equity capital shows the ability of the host country to attract new investments, the subsequent components are mostly used to adjust the equity capital investment exposure.     

Two-dimensional mapping of Asia Pacific destinations combining tourism and economic indicators

The present study aims to cluster five Asia Pacific destinations (Cambodia, Hong Kong, Indonesia, the Philippines, and Singapore) with respect to other countries according to the evolution of the main tourism and economic indicators over the period between 2000 and 2014. By assigning a numerical value to each country corresponding to its position, we summarize all the information into two components (“tourism expenditure and profitability of tourism activity” and “tourism development and economic growth”) using different multivariate techniques for dimensionality reduction. By means of perceptual maps, we find that the five Asia Pacific destinations can be clustered into three different groups: Hong Kong and Singapore, which are the most mature markets; Indonesia and the Philippines, with moderate growth rates in most variables; and Cambodia, with top positions in all variables, showing a huge potential in terms of growth and tourism development and the challenges derived therefrom.     

Identifying micro-destinations and providing statistical information: a pilot study in the Canary Islands

Municipalities and regions are often used as an object of tourism analysis at the subnational level. This occurs because administrative borders are used to implement tourism policies and collect statistical information. However, administrative boundaries may not always be suitable for studying tourism destinations at a local level. Sometimes, particularly in high-density tourism destinations, several differentiated tourism areas occupy a single municipality; tourism areas may, and often do, extend beyond municipal boundaries and tourism destinations may not occupy the whole of the municipal area. As such, a new level of analysis is often required: the micro-destination. There are few tourism concepts as imprecise as that of the destination. Therefore, the process of identifying and establishing the boundaries of a micro-destination is by no means a straightforward one. This paper presents six criteria for establishing the boundaries of this type of tourism area. Of these six, the two primary criteria used are: the concentration of tourism establishments; and tourism typologies and supply characteristics. These criteria have been applied as part of a pilot study in the Canary Islands. Statistical information for nine micro-destinations is generated using the geolocation of tourism establishments together with information obtained from both supply-side and demand-side surveys.     

Is it one break or ongoing permanent shocks that explains U.S. real GDP?

The relative importance of permanent versus cyclical shocks to GDP has been found to depend on the presence or absence of a single break in mean growth. We estimate unobserved components models conditional on a trend break having occurred in any specified quarter and use the Bayesian model averaging to combine the conditional estimates. We estimate a break occurred around 2006:1. Allowing for a break significantly reduces estimates of trend variance. However, enough spread remains in the posterior distribution to indicate that available data does not definitively settle the question of the relative importance of trend versus cycle.     

我国整车与零部件产业共聚的空间分布研究

本研究利用我国整车与零部件产业2002～2012年的数据,分析了中国整车和零部件产业协同共聚的空间布局及其空间演化趋势,以期为我国汽车、零部件产业和区域经济发展提供借鉴意义。研究发现：我国整车产业布局与零部件产业布局存在显著的空间相关性;2002年我国省域样本中,山东、安徽、北京与天津的整车与零部件产业空间共聚趋势明显;而在2012年的样本中,辽宁、北京、天津、河北、湖北、安徽、江苏的共聚趋势明显,这些省份逐步形成了2个整车与零部件空间上协同共聚的群落,一个是以北京为中心,天津、河北、辽宁环绕的共聚群;另外一个则是以安徽为中心,湖北、江苏环绕的共聚群。