x**0
发帖数: 9 | 1 有个问题，可能比较基础，不过自己还不是很清楚。希望了解的TX解释一下。
Tobit 与 censored regression model 的区别。
以前觉得前者是后者的一个特例。可是看了两个不同版本的解释，似乎互相矛盾。
wooldridge的教科书里
"While the terms “Tobit” and “censored regression” have often been used
interchangeably in econometrics, in practice there is a very important
difference.The Tobit model is applied to outcome variables that are roughly continuous
over positive values but have a positive probability of equaling zero. We saw an
example of this in the case of married women’s labor supply.. | x**0
发帖数: 9 | | f********8
发帖数: 5601 | 3 The difference is actually Censoring versus Truncation. Censoring means
corner solutions, the corresponding latent varialbes are negative.
Truncation simply means missing data, no observations are available, so
people put 0 when they collect data. Tobit is suitable for censoring, not
truncation. | f*******r
发帖数: 257 | 4 I think you can apply tobit in your case. However, tobit is very sensitive
to the normality assumption. If you have a majority of zeros in your data
set, it's very likely to reject the normality assumption. There are
different ways to handle this situation. Tobit is one of them. It's been
criticized because of the parametric assumption. Hurdle model can be
considered. Most recent development involves more complicated models to
handle that, such as SMM (simulated method of moments).
used
【在 x**0 的大作中提到】
: 有个问题，可能比较基础，不过自己还不是很清楚。希望了解的TX解释一下。
: Tobit 与 censored regression model 的区别。
: 以前觉得前者是后者的一个特例。可是看了两个不同版本的解释，似乎互相矛盾。
: wooldridge的教科书里
: "While the terms “Tobit” and “censored regression” have often been used
: interchangeably in econometrics, in practice there is a very important
: difference.The Tobit model is applied to outcome variables that are roughly continuous
: over positive values but have a positive probability of equaling zero. We saw an
: example of this in the case of married women’s labor supply..
| x**0
发帖数: 9 | 5 Thanks very much. it is helpful. |
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