Looking at the effects of the economic crisis on the past economic shocks, the movement of domestic and foreign economic variables has undergone a major change in the post - crisis period. As we have already seen, the various risks include financial r ...
Looking at the effects of the economic crisis on the past economic shocks, the movement of domestic and foreign economic variables has undergone a major change in the post - crisis period. As we have already seen, the various risks include financial risk, such as the Asian financial crisis in 1997 and the global crisis in 2008, and the most widespread and catastrophic disaster risks, such as the 2014 Mortar incident or the 2014 Mortar incident (disaster risk). First and foremost, the scope of risk is increasing and manifesting itself in various forms, so in order for the economy to grow sustainably and steadily, a function is needed to analyze and manage the impact of the risks.
It is very important in theory and policy to analyze how the responses of the industries in the economy to each other are different or similar to the economic shocks arising from various risks. In other words, it can be a basis for evaluating reality explanatory power of existing theory and suggesting new theory from the analytical point of view. From the policy point of view, Direction and range can be determined.
Acemoglu et al (2012) argues that similar com- pensation occurs in the inter-industry cycle of economic cycles, resulting from macroeconomic shocks and inter-industry interim transactions. However, the research on existing interindustry synchronicity is analyzed in the assumption that it does not change with time.
Therefore, this study attempts to analyze how the similarity of the responses of the industries to the shocks arising from various risks under uncertainty has changed over the last 30 years. It differs from the previous studies in the following points.
1) Analyze the effects of various defined risks on industry-to-industry synergies.
2) Compare and analyze macroeconomic common risk and oceanic industry's inherent risk harmonization.
3) Analyze how it changes with time.
For empirical analysis, this study includes the analysis of mixed frequency or incomplete data. That is, each of the variables has a mixed data structure that is observed by week, month, quarter, or year and has different observation periods. In particular, data on financial risk for weekly or quarterly data on production, such as industrial production, are based on weekly or daily high-frequency data.
Previous studies have shown that high-frequency data can be temporally aggregated into low-frequency as in Chow and Lin (1971), Ghysels and Valkanov (2006) As the study of Cuche and Hess (2000) and Liu and Hall (2001), interpolations of the low-order period data are made into higher order cycles, and then the form is dependent on the single-frequency based multivariate time series model. do. However, this results in loss of information and deterioration of forecasting ability, and it can yield the estimated estimation result. Recently, Ferraro et al (2015) empirical results show different results according to the period such as monthly or quarterly.
In this context, Seong et al. (2012) suggested that all multivariate time series are generated at the highest frequency, and proposed state space model and estimation method of VECM for mixed-root cointegration analysis. Using this, we consider maximum likelihood estimation based on EM (expectation maximization, Dempster et al., 1977) and Kalman filter and estimate low-order time series data based on high-order cycle time Smoothing). They have greatly improved the efficiency of estimates and predictions by establishing a cointegration model without interpolating at higher-order cycles or integrating them at lower-order cycles.
Therefore, this study attempts to analyze the response from low frequency data on high frequency data originating from various risks, and it differs from the previous studies in the following points.
1) Using the method of Seong et al., We try to perform cointegration analysis in the multi-dimensional mixed-lagged time series composed of monthly interindustry production index and weekly or daily financial and real-related risks.
2) In particular, high-frequency estimation and forecasting through this study is considered to be a meaningful study because of the high interest in macroeconomic statistics, which are breaking news after the global financial crisis.