• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.61
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• pp.165-190
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• 2014

The purpose of this study is to measure the trade insurance's macroeconomic effects by analyzing the causality between major economic variables(GDP per capita, market interest rate, inflation, unemployment rate, exchange rate) and trade insurance variable. I conducted empirical analyses using First-difference GMM(Generalized Method of Moments), System GMM and Panel-VAR Model, with panel data from 11 countries(Korea, United States, Japan, BRICs, Indonesia, Singapore, Hong Kong, Vietnam) between 1992 and 2011. There are several important findings. Above all, Trade insurance is positively and significantly related to GDP. This results show that trade insurance serves to increase economic growth. In other words, trade insurance leads to economic growth by helping increase GDP per capita. Especially, trade insurance negatively related to unemployment rate, it is for sure that trade insurance contribute to decrease unemployment rate. And trade insurance helps control of inflation. It is also confirmed that trade insurance contributes to price stability, which in turn serves to stabilize the overall economy. And this research finds as uncertainty in the market increases, seen it as increase of exchange rate, increasing trade insurance supply is stabilize the exchange rate.

• The Journal of the Acoustical Society of Korea
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• v.27 no.3
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• pp.119-128
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• 2008

This paper analyzes the relationship between two representative statistical methods for bandwidth extension (BWE): Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM) ones, and compares their performances. The HMM method is a memory-based system which was developed to take advantage of the inter-frame dependency of speech signals. Therefore, it could be expected to estimate better the transitional information of the original spectra from frame to frame. To verify it, a dynamic measure that is an approximation of the 1st-order derivative of spectral function over time was introduced in addition to a static measure. The comparison result shows that the two methods are similar in the static measure, while, in the dynamic measure, the HMM method outperforms explicitly the GMM one. Moreover, this difference increases in proportion to the number of states of HMM model. This indicates that the HMM method would be more appropriate at least for the 'blind BWE' problem. On the other hand, nevertheless, the GMM method could be treated as a preferable alternative of the HMM one in some applications where the static performance and algorithm complexity are critical.