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Empirical Study

Chapter
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Part of the ZEW Economic Studies book series (ZEW, volume 35)

Keywords

Propensity Score Cash Flow Stock Price Free Cash Flow Industry Structure 
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References

  1. 175.
    For a general overview of similarities and differences between IFRS and US-GAAP, see PriceWaterhouseCoopers (2004).Google Scholar
  2. 180.
    See Czarnitzki and Kraft (2004). See Appendix 7.4 for some descriptive statistics of the sample by creditworthiness of companies.Google Scholar
  3. 181.
    See_section 3.2.1.3.Google Scholar
  4. 184.
    This proceeding is similar to the proceeding of Schröder (2003: 26).Google Scholar
  5. 186.
    The proceeding is similar to the methodology in Burgstahler and Dichev (1997).Google Scholar
  6. 191.
    An overview is provided by Heckman et al. (1999).Google Scholar
  7. 192.
    In most research a binary treatment is applied, i.e. the treatment variable can have two states. This is also done in the present case. However, in some research this approach has been extended to multiple state matching, see e.g. Gerfin and Lechner (2002), Czarnitzki et al. (2004).Google Scholar
  8. 193.
    The idea of determining the propensity scores via probit regressions goes back to Maddala (1983).Google Scholar
  9. 195.
    The matching is done with the assistance of the psmatch2 procedure by Leuven and Sianesi (2003).Google Scholar
  10. 197.
    See DeFusco et al. (2001: 459). Sometimes this multicollinearity level is even set at 0.7. However, it is not possible to rule out the existence of multicollinearity only based on pairwise correlations because there might be linear combinations of independent variables that are highly correlateGoogle Scholar
  11. 198.
    See Cook and Weisberg (1983). The results of the Cook-Weisberg test are shown in the respective tables of the regressions.Google Scholar
  12. 199.
    See Whelan and McNamara (2004: 10). The sample is characterised by a large number of pool members (cross section) relative to the total number of observations.Google Scholar
  13. 200.
    See Figure 33 on page 164 for an illustration of the changes in the macroeconomic and capital market environment during the sample period; some other studies run annual estimations to account for such a timely variation, see e.g. Brief and Zarowin (1999); Barth et al. (1998).Google Scholar
  14. 202.
    Consistently with a similar study of Burgstahler and Dichev (1997), the domain of ROE is divided into three parts with an equal number of observations.Google Scholar
  15. 203.
    Jan and Ou (1995) and Collins et al. (1999) also found that — when price is regressed on earnings — the coefficient on earnings is reliably negative for loss firms. While the first ones call it a “bewildering phenomenon” the second ones suggest that it is the omission of book value which induces the negative bias. However, in the present analysis book value is not omitted and the sign is still negative.Google Scholar
  16. 204.
    This is largely consistent with the findings of Barth et al. (1998).Google Scholar
  17. 205.
    A possible explanation for this is that companies with high capital intensity typically operate in low competition industries, see White et al. (1997: 189–190).Google Scholar
  18. 212.
    For more details about this approach, see Cochrane and Rubin (1973), Rosenbaum and Rubin (1985). The common support restriction does not change under this approach. Thus, the number of companies that are off-support remains at 3. The matching protocol is very similar to the protocol that is presented in figure 32.Google Scholar
  19. 214.
    For this analysis, industries are defined as groups of certain NACE classes. The classification is largely identical with that of Sofka and Schmidt (2004). For a list of all industries and NACE classes included in this study, see Appendix 7.3.Google Scholar

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© Physica-Verlag Heidelberg 2006

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