• Journal of Korea Port Economic Association
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• v.30 no.2
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• pp.77-92
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• 2014

The focus of this study is to analyse dynamic relationship among BDI(Baltic Dry-bulk Index, hereafter BDI), forex market and industrial production using monthly data from 2003-2013. Specifically, we have focused on the investigations how monetary and real variable affect shipping industry during recession period. To compare performance between general VAR and Bayesian VAR we first examine DAG(Directed Acyclic Graph) to clarify causality among the variables and then employ MSFE(mean squared forecast error). The overall estimated results from impulse-response analysis imply that BDI has been strongly affected by other shock, such as forex market and industrial production in Bayesian VAR. In particular, Bayesian VAR show better performance than general VAR in forecasting.

• East Asian Economic Review
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• v.22 no.2
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• pp.141-176
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• 2018

The paper analyzes and compares the effects of domestic monetary policy using DSGE, DSGE-VAR, and VAR based on a two-country open economy model of Korea and the U.S. According to impulse response analysis, a domestic interest rate hike raises won value in the case of DSGE and DSGE-VAR models, while in the case of the unrestricted VAR model, it lowers won value. In the marginal data density standard, DSGE-VAR (${\mu}=1$) is superior to DSGE or Bayesian VAR over the sample period. Conversely, in the in-sample RMSE criterion, especially for the won/dollar exchange rate, VARs are superior to DSGE or DSGE-VAR. It is necessary to study further if these differences are caused by model misspecification or omitted variable bias.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.560-565
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• 2013

Population genetic structure and diversity of Ulmus davidiana var. japonica in South Korea were studied using ISSR markers. A total of 45 polymorphic ISSR amplicons were cropped from 7 ISSR primers and 171 individuals of 7 populations. The average of effective alleles and the proportion of polymorphic loci were 1.5 and 89% respectively. The Shannon's diversity index (I) was 0.435 and the expected heterozygosity from the frequentist's method ($H_e$) and the Bayesian inference (hs) were 0.289 and 0.323 respectively. From AMOVA, 4.2% of total genetic variation in the elm populations was explained with the difference among populations (${\Phi}_{ST}=0.042$) and the other 95.8% was distributed within populations. The ${\theta}^{II}$ value by Bayesian method which was comparable to the FST was 0.043. So the level of genetic diversity in the elm populations was similar to that in Genus Ulmus and the level of genetic differentiation was lower than that of others. No population showed a significant difference in the population-specific fixation indices (average of $PS-F_{IS}=0.822$) or the population-specific genetic differentiations (average of $PS-F_{ST}=0.101$). Seven populations were allocated into 3 groups in the UPGMA and the PCA, but the grouping patterns were different. Also, we could not confirm any geographic trend from Bayesian clustering.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1137-1147
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• 2022

River and groundwater stages are the main elements in the hydrologic cycle. They are spatially correlated and can be used to evaluate hydrological and agricultural drought. Stochastic simulation is often performed independently on hydrological variables that are spatiotemporally correlated. In this setting, interdependency across mutual variables may not be maintained. This study proposes the Bayesian vector autoregression model (VAR) to capture the interdependency between multiple variables over time. VAR models systematically consider the lagged stages of each variable and the lagged values of the other variables. Further, an autoregressive model (AR) was built and compared with the VAR model. It was confirmed that the VAR model was more effective in reproducing observed interdependency (or cross-correlation) between river and ground stages, while the AR generally underestimated that of the observed.

• East Asian Economic Review
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• v.28 no.1
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• pp.95-140
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• 2024

This study empirically investigates the international transmission of China's uncertainty shocks. It estimates a time-varying parameter Bayesian global structural vector autoregressive model (TVP-BGVAR) using time series data for 33 countries to evaluate heterogeneous international linkage across countries and time. Uncertainty shocks are identified via sign restrictions. The empirical results reveal that an increase in uncertainty in China negatively affects the global economy, but those effects significantly vary over time. The effects of China's uncertainty shocks on the global economy have been significantly altered by China's WTO accession, the global financial crisis, and the recent US-China trade conflict. Furthermore, the effects of China's uncertainty shocks, typically on inflation, differ significantly across countries. Moreover, Trade openness appears crucial in explaining heterogeneous GDP responses across countries, whereas the international dimension of monetary policy appears to be important in explaining heterogeneous inflation responses across countries.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.2
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• pp.37-47
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• 2022

This study uses monthly data from January 2009 to December 2020 to examine the effectiveness of foreign currency intervention and its influence on monetary policy in Vietnam using a Hierarchical Bayesian VAR model. The findings suggest that foreign exchange intervention has little influence on the exchange rate level or exports, but it can significantly minimize exchange rate volatility. As a result, we can demonstrate that the claim that Vietnam is a currency manipulator is false. As well, the forecast error variance decomposition results reveal that interest rate differentials mainly determine the exchange rate level instead of foreign exchange intervention. Moreover, the findings suggest that foreign exchange intervention is not effectively sterilized in Vietnam. Inflation is caused by an increase in international reserves, which leads to an expansion of the money supply and a decrease in interest rates. Although the impact of foreign exchange intervention grows in tandem with the growth of international reserves, if the sterilizing capacity does not improve, rising foreign exchange intervention will instead result in inflation. Finally, we use a rolling window approach to examine the time-varying effect of foreign exchange intervention.

• East Asian Economic Review
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• v.22 no.3
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• pp.371-400
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• 2018

Although asset price is an important factor in determining changes in external balances, no studies have investigated it from the Chinese perspective. In this study, I empirically examine the underlying driving forces of China's trade balances, particularly the role of asset price and the real exchange rate. To this end, I estimate a sign-restricted structural vector autoregressive model with quarterly time series data for China, using the Bayesian method. The results show that changes in asset price affect China's trade balances through private consumption and investment. Also, an appreciation of the real exchange rate tends to deteriorate trade balances in China. Furthermore, forecast error variance decomposition results indicate that changes in asset price (stock price and housing price) explain about 20% variability of trade balances, while changes in the real exchange rate can explain about 10%.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.181-193
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• 2022

This paper seeks to investigate major macroeconomic factors and bond yield interactions in Thai bond markets, with the goal of forecasting future bond yields. This study examines the best predictive yields for future bond yields at different maturities of 1-, 3-, 5-, 7-, and 10-years using time series data of economic indicators covering the period from 1998 to 2020. The empirical findings support the hypothesis that macroeconomic factors influence bond yield fluctuations. In terms of forecasting future bond yields, static predictions reveal that in most cases, the BVAR model offers the best predictivity of bond rates at various maturities. Furthermore, the BVAR model has the best performance in dynamic rolling-window, forecasting bond yields with various maturities for 2-, 4-, and 8-quarters. The findings of this study imply that the BVAR model forecasts future yields more accurately and consistently than other competitive models. Our research could help policymakers and investors predict bond yield changes, which could be important in macroeconomic policy development.

• Korea Trade Review
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• v.44 no.2
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• pp.127-141
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• 2019

In this study, the relationship between Baltic Dry Index(BDI) and maritime trade volume in the dry cargo market was verified using the vector autoregressive (VAR) model. Data was analyzed from 1992 to 2018 for iron ore, steam coal, coking coal, grain, and minor bulks of maritime trade volume and BDI. Granger causality analysis showed that the BDI affects the trade volume of coking coal and minor bulks but the trade volume of iron ore, steam coal and grain do not correlate with the BDI freight index. Impulse response analysis showed that the shock of BDI had the greatest impact on coking coal at the two years lag and the impact was negligible at the ten years lag. In addition, the shock of BDI on minor cargoes was strongest at the three years lag, and were negligible at the ten years lag. This study examined the relationship between maritime trade volume and BDI in the dry bulk shipping market in which uncertainty is high. As a result of this study, there is an economic aspect of sustainability that has helped the risk management of shipping companies. In addition, it is significant from an academic point of view that the long-term relationship between the two time series was analyzed through the causality test between variables. However, it is necessary to develop a forecasting model that will help decision makers in maritime markets using more sophisticated methods such as the Bayesian VAR model.

• The Korean Journal of Applied Statistics
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• v.25 no.4
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• pp.553-561
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• 2012

Various statistical models to Estimate GDP (measured as a nation's economic situation) have been developed. In this paper an autoregressive distributed lag model, factor model, and a Bayesian VAR model estimate quarterly GDP as a single model; the combined estimates were evaluated to compare a single model. Subsequently, we suggest that some combined models are better than a single model to estimate quarterly GDP.