International transmission mechanisms and contagion in housing markets

This paper explores international transmission mechanism and its role in contagion effect in the housing markets across six major Asian cities. The analysis is based on the identification of house price diffusion effects through a global vector autoregressive (GVAR) model estimated using quarterly data for six major Asian cities (Hong Kong, Tokyo, Seoul, Singapore, Taipei and Bangkok) from 1991Q1 to 2011Q2. The empirical results indicate that the open economies heavily relying on international trade such as Singapore, Japan (Tokyo), Taiwan (Taipei) and Thailand (Bangkok) show positive correlations between the economy's openness and house prices, which is consistent with the Balassa–Samuelson hypothesis. Interestingly, some region-specific conditions also appear to play important roles as determinants of house price movements, which may be driven by restrictive housing policies and demand–supply imbalances such as Singapore and Bangkok. These results are reasonably robust across several model specifications. The findings bear significant implications for formulation of investment strategy and public policies.

An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error

Atmosphere only and ocean only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, non-linearity and observation density of the respective systems. Typical window lengths are 6-12 hours for the atmosphere and 2-10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single column model of the coupled atmosphere-ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.