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The authors would like to thank Tamim Bayoumi, Stijn Claessens, Lorenzo Forni, Philip Liu, Abdelhak Senhadji, Hui Tong, as well as seminar participants at the IMF for useful comments on an earlier version of the paper. Oriel Fernandes and Malin Hu provided excellent research assistance.
Among papers that use real-time data: Laubach (2009) analyzes the impact of fiscal policy on long term U.S. yields; Dell’Erba and Sola (2011) analyze the determinants of sovereign yields in OECD countries; Alper and Forni (2011) assess debt spillover in advanced and emerging economies; Sgherri and Zoli (2009) analyze determinants of spreads in the euro area.
Throughout the text, we will use the term “real-time” to refer to the forecast values of variables from different WEO vintages, as opposed to early releases of macro data that are subject to statistical revision. Contrary to actual data, “real-time” data can be considered a proxy for investors’ expectations. Under the assumption that asset prices incorporate news rapidly, the real time variable is expected to affect the sovereign spreads via the information content it provides. Since the sovereign spreads are sampled after the release of the forecast, one can expect that their value incorporates this information, without the fundamentals being affected by the spreads. Our estimations show that regressions based on “real-time” values of determinants offer better fit than regressions based on actual values, supporting the above reasoning.
Several studies have also looked at sovereign countries’ debt ratings as a proxy for sovereign risk. The evidence suggests that rating changes are often anticipated by the markets, thus leaving open an issue of reverse causality (Gonzales Rosada and Levy-Yeyati, 2008).
Common lender refers to the circumstance in which several countries depend financially on the same creditor, normally banks in a given country.
To be included in the EMBIG index, countries have to satisfy one of the following criteria: (i) be classified as low or middle per capita income by the World Bank; (ii) have restructured external or local debt in the past 10 years; (iii) have restructured external or local debt outstanding. For a given bond to be included in the instrument, they have to have a face value of over US$500 million, with maturity of more than two years and six months), and verifiable daily prices and cash flows.
On a similar point, see Cruces and Trebesch (2011). Also, the EMBI spreads are more directly comparable across countries than current yields, thus they are a more homogeneous measure of risk compared to current yields.
Tong and Wei (2011) use domestic financial development as an additional determinant. We did not use this variable since it is not available in the real time format.
Studies that include Total External Debt to GDP are for example, Bellas et al. (2010), Akitobi and Stratmann (2008). In a separate set of regressions, we replaced the real time external debt ratio with actual total debt ratio, for which real time data was not available. The results remain qualitatively unchanged when using the total debt ratio.
We apply the Pesaran’s test that augments the standard Im, Pesaran, Shin (2003) test with cross-sectional average of all series to correct for CSD. Under the null, all series are non-stationary.
Another class of model allows also the covariates in country j to directly affect the dependent variable in country i. This model is called Spatial Durbin model (SDM) and consist in adding to the set or regressors the spatially lagged covariates
Both tests calculate the degree of correlation across units. While the Moran’s I calculated the degree of similarity across neighbor, Pesaran’s test does not assume any structure. Both tests are distributed as a Normal under the null of cross sectional independence, if the cross sectional dimension is larger than the time series.
These tests are based on the residual of the model and follow a chi-squared distribution with one degree of freedom.
The corresponding statistics are 0.15 for the Robust LM error and 76.7 for the Robust LM lag test.
The decomposition is performed by multiplying the coefficients of the model estimated in Column 5, Table 4 by the spatial multiplier (I − ρW)−1 and the mean value of the variables. The shares represent which portion of the variation of the model is explained by the two set of factors.
See also Kose et al. (2008) for a similar point.
The data on bilateral portfolio holdings are not available for China, Cote d’Ivoire, Ecuador, and Peru.
Data on rating status is not available for Cote d’Ivoire.
There is no established way to test the relative statistical importance of the weighting matrices. The procedure of including all the weighting matrices at once and test the relative significance of the coefficients of the spatial lag is problematic for several reasons: data availability is different across weights; if one were to restrict the sample to be equal across all type of weights, it would probably run into the problem of information loss, rather than gaining information about the correct spatial structure of the data; since all the matrices provide an equally plausible structure in the data, one could encounter problems of multicollinearity, e.g., trade and distance being related.
We have tried also with averages of spreads in the month and the quarter after release, but results do not change.
Introducing dynamics allows the analysis of spatio-temporal impulse response of shocks to the regressors on the EMBI spreads. This is an interesting extension to the present work that we leave for future research.
The model has other interesting aspects, like for example, the positive effect of neighbors’ inflation on the spreads. This is another interesting extension that we leave for future research.