The emphasis of this study is on the practice of the Pooled Mean Group (PMG) estimators to investigate the magnitude of macroeconomic performances: Real Gross Domestic Product (RGDP), Foreign Exchange Rate (EX)...The emphasis of this study is on the practice of the Pooled Mean Group (PMG) estimators to investigate the magnitude of macroeconomic performances: Real Gross Domestic Product (RGDP), Foreign Exchange Rate (EX), and Deposit Interest Rate (DINT) affecting on the rate of financial sector returns in Southeast Asian Stock Markets including Stock Exchange Of Thailand (SET) index (Thailand), the Kuala Lumpur Composite Index (KLSE) index (Malaysia), Financial Times Share Index (FTSI) (Singapore), Philippine Stock Exchange (PSE), and the Jakarta Composite Index (JKSE) (Indonesia). The Panel Autoregressive Distributed Lag (Panel ARDL) is applied to model the relations. The study applies the Levin, Lin, and Chu (LLC) test (2002) and Im, Pesaran, and Shin (IPS) test (2003) to investigates a set of time series data to examine whether the determinants and the rate of financial sector returns contain a unit root, the next step is investigated the cointegration and causality relationship of the determinants of financial sector influencing on long-run rate of returns of financial sector in Southeast Asian Stock Markets.展开更多
MDSA (macro demand spatial approach) is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastruc...MDSA (macro demand spatial approach) is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastructure development in a region. In the model, MDSA combined with PCA (principal component analysis) and QA (qualitative analysis) to determine main development area in region and the variables that affecting electricity demand in there. Main development area is an area with industrial domination as a driver of economic growth. The electricity demand driver variables are different for type of electricity consumer. However, they will be equal for main development areas. The variables which have no significant effect can be reduced by using PCA. The generated models tested to assess whether it still at the range of confidence level of electricity demand forecasting. At the case study, generated model for main development areas at South Sumatra Subsystem as a part of Sumatra Interconnection System is still in the range of confidence level. Thus, MDSA can be proposed as alternative approach in transmission planning that considering location.展开更多
文摘The emphasis of this study is on the practice of the Pooled Mean Group (PMG) estimators to investigate the magnitude of macroeconomic performances: Real Gross Domestic Product (RGDP), Foreign Exchange Rate (EX), and Deposit Interest Rate (DINT) affecting on the rate of financial sector returns in Southeast Asian Stock Markets including Stock Exchange Of Thailand (SET) index (Thailand), the Kuala Lumpur Composite Index (KLSE) index (Malaysia), Financial Times Share Index (FTSI) (Singapore), Philippine Stock Exchange (PSE), and the Jakarta Composite Index (JKSE) (Indonesia). The Panel Autoregressive Distributed Lag (Panel ARDL) is applied to model the relations. The study applies the Levin, Lin, and Chu (LLC) test (2002) and Im, Pesaran, and Shin (IPS) test (2003) to investigates a set of time series data to examine whether the determinants and the rate of financial sector returns contain a unit root, the next step is investigated the cointegration and causality relationship of the determinants of financial sector influencing on long-run rate of returns of financial sector in Southeast Asian Stock Markets.
文摘MDSA (macro demand spatial approach) is an approach introduced in long time electricity demand forecasting considering location. It will be used at transmission planning and policy decision on electricity infrastructure development in a region. In the model, MDSA combined with PCA (principal component analysis) and QA (qualitative analysis) to determine main development area in region and the variables that affecting electricity demand in there. Main development area is an area with industrial domination as a driver of economic growth. The electricity demand driver variables are different for type of electricity consumer. However, they will be equal for main development areas. The variables which have no significant effect can be reduced by using PCA. The generated models tested to assess whether it still at the range of confidence level of electricity demand forecasting. At the case study, generated model for main development areas at South Sumatra Subsystem as a part of Sumatra Interconnection System is still in the range of confidence level. Thus, MDSA can be proposed as alternative approach in transmission planning that considering location.