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From |
"Marc Michelsen" <marcmichelsen@t-online.de> |

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

Subject |
AW: st: Chi-squared test for independence of observed and expected frequencies |

Date |
Fri, 16 Jul 2010 10:22:40 +0200 |

Stas, Maarten, many thanks for your comments. The complete reference is: Dittmar, A., and A. Thakor. "Why do firms issue equity?" Journal of Finance 62 (2007), 1-54. You are totally right, the authors use this analysis only as an add-on / robustness test. The main body of the paper are multivariate analyses. Nevertheless, it would be quite helpful to determine the relative importance of the two explanatory variables (dimensions), i.e. prior stock return (divided into quartiles) and credit rating outlook (positive, negative, stable). Do you have any idea how the authors have tested the significance of each of the frequencies? I will have a look at your three proposed alternatives and see how fancy they are. Regards Marc -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Stas Kolenikov Gesendet: Donnerstag, 15. Juli 2010 23:52 An: statalist@hsphsun2.harvard.edu Betreff: Re: st: Chi-squared test for independence of observed and expected frequencies On Thu, Jul 15, 2010 at 10:33 AM, Marc Michelsen <marcmichelsen@t-online.de> wrote: > I am trying to copy the approach of Dittmar/Thakor (2007) "Why do firms > issue equity?" p. 27: The authors divide their sample of debt and equity > issuers into quartiles based on two explanatory variables, i.e. building a > matrix. Specifically, they examine the observed number of firms that fall > into one of the four categories and compare them to the expected > frequencies. After that, they apply a chi-squared test for independence to > determine if there are more or fewer firms than expected in each category. > Untabulated results show that each of these frequencies is significant. I agree with Maarten: that's a strange approach. Not that it is totally inappropriate... but it smells like 1960s when computations were essentially restricted to how much handwriting you can fit onto two sheets of paper. Propagating strange approaches does not do a good service to whatever discipline you are in (finance?). If those are continuous variables, you can use two-sample Kolmogorov-Smirnov tests to compare the distributions. I am pretty sure that bivariate versions of K-S tests exist, but they are not implemented in Stata. If the explanatory variables are categorical, you can compare the samples using -tabulate variable debt_vs_equity- as they are. If you want a fancier analysis, you can run -qreg- (or rather -sqreg-) over a set of quantiles, with debt/equity as the explanatory variables, to gauge whether the distributions of the continuous variables are the same for two types of firms. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: Fixed effects logit model***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**Re: st: Chi-squared test for independence of observed and expected frequencies***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: AW: st: Chi-squared test for independence of observed and expected frequencies***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: AW: st: Chi-squared test for independence of observed and expected frequencies***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: Chi-squared test for independence of observed and expected frequencies***From:*"Marc Michelsen" <marcmichelsen@t-online.de>

**Re: st: Chi-squared test for independence of observed and expected frequencies***From:*Stas Kolenikov <skolenik@gmail.com>

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