주택하위시장 구분방법과 정책적 시사점

다국어 초록 (Multilingual Abstract)

  When it usually comes to the heteroskedasticity, it will cause the estimated variance of the coefficient to influence both direction, upward and downward. In other words, the estimated of variance of the coefficient can be overestimated or underestimated depends on the nature of causing factors in the model. If we persist in using the usual testing procedures despite heteroskedasticity, whatever conclusions we draw or inferences we make be very misleading. As a result, we can no longer rely on the conventionally computed confidence intervals and the conventionally employed t and F tests. Put it another way, if the proper method is not applied without knowing the exact causing nature of heteroskedasticity, its relevant housing policy can be dubious to achieve the planned goals. The contribution of this paper will be to the concrete procedure of the proper estimation when the heteroskedasticity does exist in the data; i.e., the estimated variance of the dependent variable can be improved by giving consideration to the issues of homogeneity and/or heteroskedasticity across research areas. The paper shows that by testing for heteroskedasticity, and by using robust methods in the presence of with and without heteroskedasticity, more efficient statistical inferences, including policy implications, are provided.

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