인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
- 저자정보
피인용 0
초록·키워드
Social agents are faced with decisions that are intrinsically discrete. There are several situations in which the variable that needs to be explained takes only two different values. Spatial dependent choices frequently arise various modeling scenarios, including situations involving analysis of regional voting behavior, decisions by states or cities to change tax rates relative to neighboring jurisdictions, etc. Despite the huge availability of software to estimate cross-sectional spatial models, there are only few functions to estimate models dealing with spatial categorical dependent variable. In this study, we apply generalized method of moment (GMM) estimators to a case study analyzing the National Consortium on Violence Research (NCOVR) dataset, which includes data for 3,085 US counties. For this analysis, we first construct the binary dependent variable, CRIME_HIGH. This variable is derived from the 1980 homicide rate (HR80) and takes a value of ‛1' if a county's rate is above the dataset's third quartile, and ‛0' otherwise. Using the spatial Durbin binary model (=spatial autoregressive binary dependent model, SARB) with probit and logit link, we examine the impact of two key socio-economic independent variables: PS<sub>80</sub> measures the population structure in 1980 and UE<sub>80</sub> is the unemployment rate in 1980. In particular, we consider two different neighbourhood matrices, using the k-nearest neighbors (knn), with k=5 or 10. This approach allows us to estimate both the direct effects of these variables and their spatial spillover effects on the probability of a county experiencing a high homicide rate.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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