Basic Report

In this study, we surveyed the current status of regional statistics related to the regional economy and analyzed spatial-temporal characteristics of the regional economy using spatial statistics techniques. In addition, by estimating the extended Bayesian VAR model, we suggested that the local economy's response to exogenous shocks can vary by province and at different times.
   In this study, we examined available statistics related to the regional economy and employed spatial statistics techniques to analyze spatial and temporal characteristics of the regional economy. In addition, by estimating the extended Bayesian VAR model, it was found that the local economy's response to exogenous shocks differs by city/province or time point.
   First, to better understand the current status of the regional economy, scattered regional statistics need to be scrutinized and unified to enable accurate and objective analysis.
   Although there may be differences in the list of statistics used depending on the unique characteristics of each city and province, a unified indicator and analysis framework should be presented and used to identify regional economic trends nationwide and compare and analyze their relative positions.
   The statistics and utilization status by city and province presented in this study can be used to compare economic trends by region.
   Second, we aim to understand the characteristics of the mid-to-long-term spatial-temporal distribution targeting geographical space. By employing hot and cold spot analysis on employment rate, unemployment rate, number of mining and manufacturing businesses, workers, per capita salary, and population movement, it was possible to classify regions that have been hot or cold spots for the last 11 years.
   Analysis results for each indicator were presented to classify regional types. It was also suggested to conduct additional analysis to understand the cause of the region classified as a hotspot.
   Third, it was suggested that shock responses to exogenous shocks according to the time could differ by region. We chose Gyeonggi-do and Jeollanam-do, where regional economic cycles can be contrasted, and empirically analyzed the response function to exogenous shocks that occurred during the economic recession of the last 20 years.
   Based on the analysis results, it was suggested that regional economic trends and the resulting policies and strategies employed in response to regional economic fluctuations may imply that different policy combinations are needed for each region and time point.
   Therefore, it is crucial that local governments closely observe the cycle of the local economy and propose policy combinations suitable for each time point to the state. To this end, securing sufficient capacity for analyzing economic trends in each province should be a priority.
   Finally, the factor expansion model introduced and analyzed in this study can be used for GRDP nowcasting through follow-up studies. Central banks of each country are already introducing GDP Nowcasting on a trial basis by utilizing economic variables that highly correlate with GDP. In the case of the factor-expanding VAR applied in this study, statistics by sector that were not considered in the previous analysis were included, and the information contained in the statistics in use can be reduced to a few factors. In future research, it will be possible to find GRDP time series, correlation, and variables with high predictability for each trial and use them for GRDP Nowcasting.