Capture the abrupt changes in Asian residential property markets.

In this paper, studies on the real estate markets mainly focused on the relationship between abrupt change points and corresponding political issues and economic collapse. Within the past statistical framework, change-point detection technique was widely considered based on large and long data sets. Few studies considered the situation where a limited size of time-series data sets is available in the real estate markets. To fill in this gap, the wavelet analysis with minimax threshold is introduced in this paper. By comparing Daubechies LA(8), wavelet analysis with minimax threshold is a versatile and powerful approach to the analysis of residential data as they are flexible in their function form and provide a robust computational method even with a small sample size. The detected change points reflect some significant political issues and economic collapses. It can be shown from the empirical result that a "diffusion relationship" happened from one location to another.

Examining structural changes in Asian offices market.

It is complicated to measure the effects of various economic events on office markets within a non-parameter modeling framework. In response to this issue, a non-parametric statistical method-wavelet analysis is introduced in this study. Based on this innovative technique, we not only could detect the abrupt change points with a comparatively small data sample, but also could evaluate the impact from the abrupt change points by reconstructing the wavelet coefficient/de-noising the raw data, which had never been considered in previous studies of office markets. Our empirical results suggest that the wavelet reconstruction method, to some extent, makes it easier for the detection of the existence of structural change points. More interestingly, our findings also indicate that free market economies (i.e. Hong Kong and Singapore) are mainly influenced by the effects of global events, whereas the actual (net) impact on socialist economies (i.e. Beijing and Shanghai), depends on both the openness of the economies, and the magnitude of counter domestic forces put in place.

Modeling respiratory illnesses with change point: A lesson from the SARS epidemic in Hong Kong.

It is generally agreed that respiratory disease is closely related to ambient air quality and weather conditions. Besides, hygiene related factors such as the public health measures by the government and possible personal awareness in the community can also affect the spread of infectious respiratory diseases. However, there is no quantitative support for this conclusion, because of lack of quality data. The severe acute respiratory syndrome (or SARS) outbreak in 2003 triggered strict public health measures and personal awareness in the prevention of infectious respiratory diseases, providing us an opportunity to quantify the impact of hygiene related factors in the spread of the disease. In this paper, we model the number of the respiratory illnesses by a semiparametric model which models the environmental and weather impacts using a multiple index model and the impact of other public health measures and possible personal awareness using a growth curve with jump. Using data from Hong Kong, we found that public health measures contributed to about 39% of reduction in the number of respiratory illnesses during the SARS period. However, the impact of hygienically related factors eventually fades as time passes. The results provide indirect quantitative support to the usefulness of governmental campaigns to arouse the awareness of the public in staying away from transmission of respiratory diseases during the full outbreak of the disease. The results also show the fast fading of alertness of Hong Kong people towards the epidemic. Furthermore, our model also offers a way to model the impacts of environmental factors on respiratory diseases, when the data contains the effect of human intervention, by introducing the change point and growth curve to remove such an effect.

Finding environmental factors for respiratory diseases by nonparametric variable selection.

It is well known that the exposure to ambient air pollution might cause serious respiratory illnesses and that the weather conditions may also contribute to the seriousness. However, quantifying the effects of pollution and the weather condition is a difficult task due to the nonlinear nature of these impacts. The problem is further complicated by the possibly cumulative effects of these impacts. In this paper, the nonparametric additive (NPA) models, which have the advantage of ease in interpretation and forecasting, are employed for modeling the effects of pollution and weather. All models are derived by the local linear method. The variables in the final selected NPA model are chosen by cross-validation method together with bootstrap test for the data of Hong Kong. For comparison the final selected linear regression (LR) model by the backward elimination method is also considered. It is found, interestingly, that the variables selected by nonparametric method and the usual backward elimination method for linear models are different. Furthermore, by comparing forecasted values obtained from the NPA and LR models and true values the final selected NPA model is shown to outperform the LR model.