from functional restrictions. Model A3 eases the functional restrictions even further such that all parameters in can vary by proximity orders and types of station; thus, has no room to intervene. In both extended models, a greater J is associated with more parameters, whereas the number of parameters does not go beyond six in Model A1. Table 11 describes estimation results for Model A2. The results of the model with = 4 or more J are not shown in the table because these maximum likelihood estimates fail to converge. We suspect that and some of are no longer statistically different from zero for = 4, preventing the j parameters from being identified. << >> insert Table 11, here 12 shows results for Model A3. We are also unable to estimate the models with = 4 or J more. By construction of this model, and cannot be identified if is zero. We suspect that is close enough to zero. << >> insert Table 12, here Next, the following two models are examined as extensions of Model B1: Model B2 Model B3 As with the previous cases, only in Model B2 are generalized from Model B1, and Model B3 ’s allows all parameters to vary by proximity orders and qualitative characteristics. We employ OLS for Model B3, which now turns to be a linear function. Table 13 shows the results for Model B2. In contrast to Model A2, we are able to obtain results for all from 1 to 9, because J the parameters are independent of one another in this model. We observe that and are close to zero, as with the results for Model B1. << >> insert 13, here In [13-5], = 0.22 means that the effect of the second closest station with a new line is 22% as significant as the effect of the closest station. It turns out that is the only weighting parameter with a significant sign. = 0.41 implies that the rent is higher by 4,100 yen ($41) for a housing unit with , and compared with the rent of a housing unit with , and . If we assess the rental difference by using the mean distance to the second closest station (0.93 mile) and , the rental gap will be approximately 1,300 yen ($13).
from functional restrictions. Model A3 eases the functional restrictions even further such that all parameters in can vary by proximity orders and types of station; thus, has no room to intervene. In both extended models, a greater J is associated with more parameters, whereas the number of parameters does not go beyond six in Model A1. Table 11 describes estimation results for Model A2. The results of the model with J = 4 or more are not shown in the table because these maximum likelihood estimates fail to converge. We suspect that and some of are no longer statistically different from zero for j = 4, preventing the parameters from being identified. << insert Table 11, here >> 12 shows results for Model A3. We are also unable to estimate the models with J = 4 or more. By construction of this model, and cannot be identified if is zero. We suspect that is close enough to zero. << insert Table 12, here >> Next, the following two models are examined as extensions of Model B1: Model B2 Model B3 As with the previous cases, only ’s in Model B2 are generalized from Model B1, and Model B3 allows all parameters to vary by proximity orders and qualitative characteristics. We employ OLS for Model B3, which now turns to be a linear function. Table 13 shows the results for Model B2. In contrast to Model A2, we are able to obtain results for all J from 1 to 9, because the parameters are independent of one another in this model. We observe that and are close to zero, as with the results for Model B1. << insert 13, here >> In [13-5], = 0.22 means that the effect of the second closest station with a new line is 22% as significant as the effect of the closest station. It turns out that is the only weighting parameter with a significant sign. = 0.41 implies that the rent is higher by 4,100 yen ($41) for a housing unit with , and compared with the rent of a housing unit with , and . If we assess the rental difference by using the mean distance to the second closest station (0.93 mile) and , the rental gap will be approximately 1,300 yen ($13).
20170502-econo-sadayuki-wp-draft22.docx