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From |
"Zou Hong" <hzou@ln.edu.hk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
Re: st: truncreg problem and the reasons |

Date |
Tue, 24 Jul 2007 09:42:34 +0800 |

Dear Austin,

Many thanks for your prompt and very kind advice and suggestion. I need to read and think about your comments and may need to come back to you later.

Regards

Joe

----- Original Message ----- From: "Austin Nichols" <austinnichols@gmail.com>

To: <statalist@hsphsun2.harvard.edu>

Sent: Tuesday, July 24, 2007 5:20 AM

Subject: Re: st: truncreg problem and the reasons

Zou Hong <hzou@ln.edu.hk>: Your problem seems ideal for -poisson- (or -glm- or -zip-); see http://www.statacorp.com/statalist/archive/2007-04/msg00549.html and related posts for some relevant discussion. Suppose "ins" is insurance and "asset" is value of insurable assets--then you should . g lna=ln(asset) . poisson ins lna size1 tang, r to get an estimate of the elasticity of insurance with respect to assets. Your current model *assumes* the elasticity is one, i.e. the model with the coef on lna constrained to one is equivalent to . g ratio=ins/asset . poisson ratio size1 tang, r but you do not know that the coef is equal to one, right? As an example, consider: webuse nlswork, clear keep if year>78 bys id (year): drop if _n<_N gen total=wks_work*hours gen wage=exp(ln_w) gen earn=wage*total gen lntot=ln(total) poisson earn lntot grade tenu, r test lntot=1 poisson wage grade tenu, r Note that just because you can't reject the null hypothesis that the coef on the "denominator" variable is one, doesn't mean you should blindly accept the null. (There is a positive association between higher hours and higher wages in the example, and there likely is a similar pattern in your insurance and assets data.) It just makes sense to estimate the parameter IMHO. On 7/22/07, Zou Hong <hzou@ln.edu.hk> wrote:Dear lister,

I am investigating the insurance consumption issue using Cragg's (1971)

model, which is a first-stage probit plus a second-stage truncated model at

zero (using only firms buying insurance). My sample is quite big around

60,000 observations rougly with 50% of firms buying insurance.

I then find "ins1", the dependent variable defined as insurance

expense/total assets, is higly skewed with a high kurtosis (see descriptive

statistics below). I suspect this is the source of the problem. To mitigate

the skewness, I create a variable "lnins1" (= ln (1+ins1*1000)) that is

truncated at 0. I multiply ins1 by 1000 since ins1 is a very small ratio

variable. I then reestimated the truncated variable and the model did

converge (see below).

I wonder whether my above transformation makes a sense. I think it does preserve the interpretation of the direction of independent variables on "ins1". Any and suggestions comments are welcome Joe* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

* * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: What is wrong with this syntax?***From:*Kyle Hood <kyle.hood@yale.edu>

**Re: st: What is wrong with this syntax?***From:*Richard Williams <Richard.A.Williams.5@ND.edu>

**st: truncreg problem and the reasons***From:*"Zou Hong" <hzou@ln.edu.hk>

**Re: st: truncreg problem and the reasons***From:*"Austin Nichols" <austinnichols@gmail.com>

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