지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 연구보고서
- 저자정보
- 발행연도
- 2011.10
- 수록면
- 1 - 116 (116page)
이용수
초록· 키워드
오류제보하기
This study is to activate economic statistics, to integrate the existing economic indicators and to develop the Leading Composite Indexes of business indicators in Gyeongnam, province.
That is, this study aims to develop Leading Composite Index; Leading CI using economic indicators in Gyeongnam and to use the index for business forecasting, so-called a diffusion index which helps us interpret composite indexes of the Business Cycle Indicators accurately. The Leading CI in Gyeongnam will be a follow-up of Coincident Composite Index which has been already developed in our study.
Also, this study tries to find some ways to use the Leading CI as a model for forecasting phase or trend of business condition in the near future.
In chapter 2 status of statistics compiled in Gyeongnam are analysed and found some related problems. In chapter 3, the way how Leading Composite Indexes of business indicators has been developed is covered. Chapter 4 is covering the result and the way how the Leading CI could be used.
Eight economic indicators are selected as the final economic indicators for developing Leading CI in Gyeongnam. The eight indicators are opening-toapplication ratio, inventory cycle indicator, building area, shipment index of intermediate materials, the amount of imports of capital goods, net barter terms of trade(nationwide), loans and discounts of deposit money bank, and composite stock price index.
Compared with Cycle Variation Value of Coincident Composite Index in Gyeongnam, that of Leading CI proceeds by 10 months compared on the same period from a year ago. This allows us to forecast the current economic trend and the economic trend in the near future.
Mean Absolute Percentage Error was used to find out how well the model using Leading CI can predict business conditions and trends.
I have suggested two business conditions forecast models in analysis; one is a regression model of which dependent variable is Coincident CI and independent variable is Leading CI, and the other is a regression model where Coincident CI Cycle Variation Value is a dependent variable and Leading CI year-to-year is an independent variable.
Since MAPE from the two models was low, it is suggested that the prediction is relatively accurate.
Pattern of Leading CI comparing to the same period from a years ago passes by trough in May, 2009 and continues through expansion phase until the first half year of 2010 and it reaches peak and the contraction phase continues until 2011. It is expected that there will be a tendency for business to drop for the first half of the next year for a while.
This study suggests a model where GRDP growth rate in Gyeongnam is dependent variable and Leading CI in Gyeongnam is independent variable. This model will boost economic growth rate. This model also can be used for predicting economic outlook for next year, or the first half year or the second half year of the next year.
That is, this study aims to develop Leading Composite Index; Leading CI using economic indicators in Gyeongnam and to use the index for business forecasting, so-called a diffusion index which helps us interpret composite indexes of the Business Cycle Indicators accurately. The Leading CI in Gyeongnam will be a follow-up of Coincident Composite Index which has been already developed in our study.
Also, this study tries to find some ways to use the Leading CI as a model for forecasting phase or trend of business condition in the near future.
In chapter 2 status of statistics compiled in Gyeongnam are analysed and found some related problems. In chapter 3, the way how Leading Composite Indexes of business indicators has been developed is covered. Chapter 4 is covering the result and the way how the Leading CI could be used.
Eight economic indicators are selected as the final economic indicators for developing Leading CI in Gyeongnam. The eight indicators are opening-toapplication ratio, inventory cycle indicator, building area, shipment index of intermediate materials, the amount of imports of capital goods, net barter terms of trade(nationwide), loans and discounts of deposit money bank, and composite stock price index.
Compared with Cycle Variation Value of Coincident Composite Index in Gyeongnam, that of Leading CI proceeds by 10 months compared on the same period from a year ago. This allows us to forecast the current economic trend and the economic trend in the near future.
Mean Absolute Percentage Error was used to find out how well the model using Leading CI can predict business conditions and trends.
I have suggested two business conditions forecast models in analysis; one is a regression model of which dependent variable is Coincident CI and independent variable is Leading CI, and the other is a regression model where Coincident CI Cycle Variation Value is a dependent variable and Leading CI year-to-year is an independent variable.
Since MAPE from the two models was low, it is suggested that the prediction is relatively accurate.
Pattern of Leading CI comparing to the same period from a years ago passes by trough in May, 2009 and continues through expansion phase until the first half year of 2010 and it reaches peak and the contraction phase continues until 2011. It is expected that there will be a tendency for business to drop for the first half of the next year for a while.
This study suggests a model where GRDP growth rate in Gyeongnam is dependent variable and Leading CI in Gyeongnam is independent variable. This model will boost economic growth rate. This model also can be used for predicting economic outlook for next year, or the first half year or the second half year of the next year.
목차
[표지]
[발간사]
[요약 및 정책제언]
[목차]
표목차
그림목차
[제1장 서론]
제1절 연구배경과 필요성
제2절 연구 목적과 체계
[제2장 지역통계현황]
제1절 통계작성현황
제2절 행정구역별 통계현황
제3절 경남지역통계현황
제4절 시사점
[제3장 경기선행종합지수 개발]
제1절 경기종합지수 종류 및 작성현황
제2절 기본 방향
제3절 경기선행종합지수 개발 방법
제4절 경남 경기선행종합지수 대표계열 선정
[제4장 경기선행종합지수 결과]
제1절 경남 경기선행종합지수 작성 결과
제2절 경남지역 경기변동 분석
제3절 선행지수를 활용한 경기예측
제4절 경기종합지수 활용 방안
[제5장 결론 및 정책제언]
제1절 결론
제2절 정책제언
[참고문헌]
[ABSTRACT]
[부록: 선행지수 구성지표]
참고문헌 (0)
참고문헌 신청이 논문의 저자 정보
이 논문과 함께 이용한 논문
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2014-322-000867889