TY - JOUR
T1 - An approach to causal modeling in fuzzy environment and its application
AU - Oh, Sang Bong
AU - Kim, Woncheol
AU - Jae Kyu Lee, Kyu Lee
PY - 1990/3/9
Y1 - 1990/3/9
N2 - This paper suggests an approach to building a causal model in fuzzy environment. The fuzzy vector autoregressive (FVAR) model is defined and a causality definition in the fuzzy environment is provided. Three types of causality, namely, nonfuzzy non-causality, nonfuzzy causality, and fuzzy causality are defined based on the fuzzy causality relation introduced in this paper. Necessary and sufficient conditions for the three types of causality are also provided when fuzzy parameters have triangular membership functions. A procedure to implement the fuzzy causal modeling is also suggested. We applied the procedure to real data and compared it with other conventional empirical studies.
AB - This paper suggests an approach to building a causal model in fuzzy environment. The fuzzy vector autoregressive (FVAR) model is defined and a causality definition in the fuzzy environment is provided. Three types of causality, namely, nonfuzzy non-causality, nonfuzzy causality, and fuzzy causality are defined based on the fuzzy causality relation introduced in this paper. Necessary and sufficient conditions for the three types of causality are also provided when fuzzy parameters have triangular membership functions. A procedure to implement the fuzzy causal modeling is also suggested. We applied the procedure to real data and compared it with other conventional empirical studies.
KW - economics
KW - Fuzzy causality analysis
KW - fuzzy causality relation
KW - fuzzy linear regression
KW - fuzzy vector autoregressive (FVAR) model
UR - https://www.scopus.com/pages/publications/38249019119
U2 - 10.1016/0165-0114(90)90017-Z
DO - 10.1016/0165-0114(90)90017-Z
M3 - 文章
AN - SCOPUS:38249019119
SN - 0165-0114
VL - 35
SP - 43
EP - 55
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
IS - 1
ER -