Identifying the Predictors for Financial Crisis Using Gibbs Sampler

The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory and econometric methods. By using the economic theory, one can locate possible potential crisis predictors whereas appropriate econometric models can pinpoint effective ones. In this paper we suggest using the Stochastic Search Variable Selection (SSVS) developed by George and McCulloch (1993) to identify the crisis predictors. As is suggested by the name, SSVS stochastically searches for practically significant variables. Each variable coefficient is assumed to come from a mixture of two normal variates with respectively large and small variances. For the former case, this variable is considered as insignificant and should be excluded from the model whereas for the latter, this variable is significant and should be included in the model. SSVS is not affected by the ordering of the candidate variables and is particularly effective when the sample size is much smaller than the number of all possible models. By employing SSVS method, we conclude that annual growth rate of money supply, $M_2$, and the ratio of government debt to GDP are promising predictors for financial crisis. It is worth mentioning that the frequently mentioned factors, such as ratio of total foreign reserve to GDP and the ratio of current deficit to GDP are not selected by our analysis. Our empirical analysis implies that monetary and fiscal policy play a crucial role in exploring the Asian financial crisis.early warning

Identifying the Predictors for Financial Crisis Using Gibbs Sampler

The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory and econometric methods. By using the economic theory, one can locate possible potential crisis predictors whereas appropriate econometric models can pinpoint effective ones. In this paper we suggest using the Stochastic Search Variable Selection (SSVS) developed by George and McCulloch (1993) to identify the crisis predictors. As is suggested by the name, SSVS stochastically searches for practically significant variables. Each variable coefficient is assumed to come from a mixture of two normal variates with respectively large and small variances. For the former case, this variable is considered as insignificant and should be excluded from the model whereas for the latter, this variable is significant and should be included in the model. SSVS is not affected by the ordering of the candidate variables and is particularly effective when the sample size is much smaller than the number of all possible models. By employing SSVS method, we conclude that annual growth rate of money supply, $M_2$, and the ratio of government debt to GDP are promising predictors for financial crisis. It is worth mentioning that the frequently mentioned factors, such as ratio of total foreign reserve to GDP and the ratio of current deficit to GDP are not selected by our analysis. Our empirical analysis implies that monetary and fiscal policy play a crucial role in exploring the Asian financial crisis.Financial crisis, early warning

Identifying the Predictors for Financial Crisis Using Gibbs Sampler

The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory and econometric methods. By using the economic theory, one can locate possible potential crisis predictors whereas appropriate econometric models can pinpoint effective ones. In this paper we suggest using the Stochastic Search Variable Selection (SSVS) developed by George and McCulloch (1993) to identify the crisis predictors. As is suggested by the name, SSVS stochastically searches for practically significant variables. Each variable coefficient is assumed to come from a mixture of two normal variates with respectively large and small variances. For the former case, this variable is considered as insignificant and should be excluded from the model whereas for the latter, this variable is significant and should be included in the model. SSVS is not affected by the ordering of the candidate variables and is particularly effective when the sample size is much smaller than the number of all possible models. By employing SSVS method, we conclude that annual growth rate of money supply, $M_2$, and the ratio of government debt to GDP are promising predictors for financial crisis. It is worth mentioning that the frequently mentioned factors, such as ratio of total foreign reserve to GDP and the ratio of current deficit to GDP are not selected by our analysis. Our empirical analysis implies that monetary and fiscal policy play a crucial role in exploring the Asian financial crisis

Mechanisms of Visible Light Photocatalysis in N-Doped Anatase TiO2 with Oxygen Vacancies from GGA+U Calculations

We have systematically studied the photocatalytic mechanisms of nitrogen doping in anatase TiO2 using first-principles calculations based on density functional theory, employing Hubbard U (8.47 eV) on-site correction. The impurity formation energy, charge density, and electronic structure properties of TiO2 supercells containing substitutional nitrogen, interstitial nitrogen, or oxygen vacancies were evaluated to clarify the mechanisms under visible light. According to the formation energy, a substitutional N atom is better formed than an interstitial N atom, and the formation of an oxygen vacancy in N-doped TiO2 is easier than that in pure TiO2. The calculated results have shown that a significant band gap narrowing may only occur in heavy nitrogen doping. With light nitrogen doping, the photocatalysis under visible light relies on N-isolated impurity states. Oxygen vacancies existence in N-doped TiO2 can improve the photocatalysis in visible light because of a band gap narrowing and n-type donor states. These findings provide a reasonable explanation of the mechanisms of visible light photocatalysis in N-doped TiO2

Comparative study of spectral and morphological properties of blends of P3HT with PCBM and ICBA

[[abstract]]We report a comparative study on spectral and morphological properties of two blend systems for polymer solar cells: the donor material poly(3-hexylthiophene) (P3HT) in combination with the acceptor material of either [6,6]-phenyl-C61 butyric acid methyl ester (PCBM) or indene-C60 bisadduct (ICBA) that was reported to enhance efficiencies of polymer solar cells. Optical microscopy and grazing incidence X-ray scattering reveal the stronger tendency of PCBM to from larger and more ordered domains/grains than ICBA either in pure or blend films. Compared to PCBM, the presence of ICBA also substantially perturbs the organization and longer-range ordering of P3HT in increasing the ICBA ratio in blends. With larger and more ordered phase-separated domains, the P3HT/PCBM blend films exhibit significant optical scattering at higher PCBM ratios. Yet, such optical scattering is not significant for P3HT/ICBA blends (even with high ICBA ratios). Overall, results here suggest the reported higher efficiencies of P3HT/ICBA solar cells (vs. P3HT/PCBM cells) cannot be attributed to larger and/or more ordered phase-separated donor–acceptor domains and other characteristics play more important roles in this case.[[incitationindex]]SCI[[booktype]]電子版[[booktype]]紙

Liquid biopsy genotyping in lung cancer: ready for clinical utility?

Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy