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Re: st: RE: RE: Using ivhettest to test for heterogeneity

From   Nick Cox <[email protected]>
To   [email protected]
Subject   Re: st: RE: RE: Using ivhettest to test for heterogeneity
Date   Fri, 2 Mar 2012 00:58:25 +0000

Again, I don't think the main issue with a predictor is whether it
causes heteroscedasticity!

But yes, as you are using plain -regress- you have the maximum
possible set of postestimation commands, including various diagnostic


On Thu, Mar 1, 2012 at 9:36 PM,  <[email protected]> wrote:
> Sorry, I did not want to puzzle anybody here by just giving priority to theoretical arguments. I think you are completely right that I should better have omitted some variables from a statistical viewpoint. I have relied on conducting standard heteroskedasticity tests in Stata because I did not anticipate that the problem might be a serious one. Certainly it would be a good idea to plot residuals vs fitted to detect outliers in the data. Would you also recommend to look at specific measures like Cook's distance to decide which predictors are likely to cause heteroskedasticity and may therefore be omitted?
> Andreas
> [email protected] wrote: -----
> To: "'[email protected]'" <[email protected]>
> From: Nick Cox
> Sent by: [email protected]
> Date: 03/01/2012 04:37PM
> Subject: RE: st: RE: RE: Using ivhettest to test for heterogeneity
> It's your problem, entirely and obviously, but I find it puzzling that like many people you seem more concerned with secondary assumptions about errors than with simplifying the model to omit predictors.
> Have you plotted residuals vs fitted?
> Nick
> [email protected]
> [email protected]
> These is a firm-specific regression of the cost of capital (COC) on different firm characteristics,
> including the potentially endogeneous fractional rank of disclosure quality (DRANK) and its linear
> interaction with the exogenous firm age (LBAGE), D_LBAGE. LNMV, LNBM LNLEV, CapInt, ROA, AssTr, LTG,
> LVOL and MAFE are different firm characteristics proxying for size, leverage, capital intensity,
> profitability, growth opportunities, stock volatility and estimation risk. There are indeed important
> theoretical reasons for keeping those variables in the regression, otherwise I would have dropped
> any insignificant predictors. Note that because of the endogeneity problem I have also run a sensitivity
> analysis using 2SLS treating both disclosure quality (DRANK) and the interaction effect (D_LBAGE) as
> endogenous. However, I decide to keep to the simpler and more efficient OLS estimation because
> the Hausman test statistic does not reject the null of exogeneity on the 5% level (even though my
> results likely suffer from small-sample bias and the Hausman test statistic may thus not be very
> conclusive).
> This is all what I can tell you about the background of these data. But if you still cannot make sense
> of my results, it won't be that bad because the test statistics of the different heterskedasticitiy
> test versions are all rejecting homoskedacitiy on the 10% level anyway.

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