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Extra info for Modelling the Riskiness in Country Risk Ratings: An Empirical Analysis of the Trends and Volatilities in Country Risk Ratings and Risk Returns
And P. Rivoli (1990), “Politics and perceived country creditworthiness in international banking”, Journal of Money, Credit and Banking, Vol. 22(3), pp. 357– 369. N. and H. Inoue (1985), “An appraisal of the early-warning indicators of sovereign loan default in country risk evaluation systems”, Management International Review, Vol. 25(1), pp. 45 – 56. Cantor, R. and F. Packer (1996), “Determinants and impact of sovereign credit ratings”, FRBNY Economic Policy Review, October, pp. 37 – 53. P. (1997), “Neural network approach for assessing country risk for foreign investment”, International Journal of Management, Vol.
Classiﬁcation of cross-section data by number of countries Number of Countries Frequency 18 20 27 29 30 35 45 49 52 70 71 88 93 143 892 Unstated Total 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 18 Notes: (1) One study used three data sets. (2) The sample with 892 observations refers to municipalities rather than countries. 6. Classiﬁcation of cross-section data by number of time series observations Number of Observations Frequency 1 2 3 4 5 7 8 10 11 20 23 10 4 1 2 5 1 1 1 1 2 1 Total 29 Note: More than one time series data set was used in some studies.
While logit, probit and Tobit models are used 40 times in total, the ML estimation method is used only 35 times. Moreover, while linear and log – linear models are used only seven times in total, OLS is used 16 times. Hence, there is a disparity between the model speciﬁcation examined and the appropriate method of estimation. Finally, diagnostic testing has generally been ignored in the country risk literature. Overall, in the absence of testing the validity of the underlying assumptions of the surveyed country risk models, the empirical results should generally be interpreted with both caution and scepticism.