Table 3 Skewness and excess kurtosis of education variables.
From: Asymmetric impacts of artificial intelligence on housing price valuation across education levels
Busan | Daegu | Daejeon | Gwangju | |
|---|---|---|---|---|
Panel A: Univ. grad. | ||||
Skewness | \({0.733}{(62.197^{\ddagger})}\) | \({-0.415}{({-38.787}^{\ddagger})}\) | \({0.616}{(36.225^{\ddagger})}\) | \({0.283}{(23.877^{\ddagger})}\) |
Excess kurtosis | \({0.692}(24.577^{\ddagger})\) | \({0.477}(18.841^{\ddagger})\) | \({1.277}{(25.602^{\ddagger})}\) | \({0.611}{(20.327^{\ddagger})}\) |
Panel B: Top school | ||||
Skewness | \({-0.276}{({-25.578}^{\ddagger})}\) | \({0.442}{(41.107^{\ddagger})}\) | \({0.347}{(21.495^{\ddagger})}\) | \({-0.331}{({-27.699}^{\ddagger})}\) |
Excess kurtosis | \({-1.011}({-114.128}^{\ddagger})\) | \({-0.909}{({-90.241}^{\ddagger})}\) | \({-1.000}{({-74.768}^{\ddagger})}\) | \({-07.88}{({-59.662}^{\ddagger})}\) |
