英文摘要 |
This paper discusses the applications of genetic programming to the empirical study of the natural rates of vancancy in Taiwan's housing market. The genetic programming paradigm, a new approach developed in artificial intelligence, is an automatic model search process and is very promising in treating the issue of model selection. By using the model in Lin et. al. (1994) as a benchmark, we explore the advantages of this approach by demonstrating two things: firstly, how genetic programming can be used to investigate the robustness of a given model; secondly, how genetic programming can be used to detect the potential non-lineraity and structural stability in the model. Our findings are two-fold. First of all, using the data running from 1981 to 1988, we find that the 2SLS model considered in Lin et. at. is pretty robust at least in the sense of linearity. However, if we exclude 1981 and add 1989 to our data set, the model is not robust any more. Our further analyses suggest that business cycles in the housing market might affect our estimate of natural vancancy rates and should be taken into account in future studies. |