哎~楼主对虚拟交叉项的理解还需加油啊,多翻翻书吧,市面上通行的教材基本都有讲的。
随便摘一本吧: Lawrence C. Hamilton. Statistics with STATA Version 12.
Cengage Learning P185-186
Interaction Effects
The previous section described what are called “intercept dummy variables,” because their coefficients amount to shifts in a regression equation’s y intercept, comparing the 0 and 1 groups. Another use for dummy variables is to form interaction terms called “slope dummy variables” by multiplying a dummy times a measurement variable. In this section we stay with the Nations2.dta data, but consider some different variables: per capita carbon dioxide emissions (co2), percent of the population living in urban areas (urban), and the dummy variable reg4 defined as 1 for European countries and 0 for all others. We start out by labeling the values of reg4, and calculating a log version of co2 because of that variable’s severe positive skew.
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We form an interaction term or slope dummy variable named urb_reg4 by multiplying the dummy variable reg4 times the measurement variable urban. The resulting variable urb_reg4 equals urban for countries in Europe, and zero for all other countries.
简单地说,虚拟变量交叉其他变量,会产生一个影响斜率的新变量。类比虚拟变量本身会影响截距。比如图中是否为欧洲(urban)变量就影响了co2的斜率,即co2的系数。
还有许多可以参考的书,比如Wooldridge. Introductory Econometrics A Modern Approch第5版,清华大学出版社有影印版的。