Abstract
In this paper we provide a characterization of international consumption risk sharing among a sample of OECD countries based on panel cointegration and error-correction techniques. Our results indicate that around 30% of idiosyncratic consumption risks are shared in the short run. In the long run, however, only about 10% of idiosyncratic consumption risks are shared internationally. In addition, we find that countries characterized by relatively high foreign asset and liability positions are less exposed to shocks. Moreover, the time it takes until idiosyncratic shocks exert their full impact on consumption crucially depends on the foreign asset and liability position.
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Notes
Note, that Eq. 3 assumes weak exogeneity of \(\tilde{y}_{it}\), an assumption which is tested below. Also note, that country intercepts are not included in the short-run part of the ECM. An additional sensitivity analysis shows that the results are not qualitatively affected by this assumption.
Estimations and tests are carried out with STATA 9.2, EVIEWS 5.0 and RATS 6.10.
Remaining OECD countries are not included due to data problems. For example, Germany is excluded, as data are available for 1970 onwards only and due to a severe break in the data in 1989/1990.
The CIPS test basically is a cross-sectional augmented version of the IPS test as the standard (augmented) Dickey–Fuller regressions for each cross-section are augmented with the cross-sectional averages of lagged levels and first differences of the individual series. The non-truncated CIPS tests is the simple average of the individual cross- sectional augmented Dickey–Fuller test statistics (see Pesaran 2003, equation (5.60)). We use the non-truncated CIPS statistic as its size and power properties are similar to that of the truncated CIPS statistics for our sample of N = 21 and T = 50.
A testing strategy of this type is also recommended by Ng and Perron (1995). As MA(q)-errors are very likely in macroeconomic time series an overparameterization and hence a loss in the power of the unit root tests should be no problem.
A variable with its cross-section average subtracted at each time period is called demeaned and variables calculated as explained above are called nearly demeaned in what follows.
Results shown below are based on demeaned data. Results from nearly demeaned variables are very similar and can be obtained from the authors upon request.
The mean lag for an ARDL(1,1) is defined as θ = (1-γ)/λ (see Hendry 1995).
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We would like to thank Mathias Hoffmann and two anonymous referees for helpful comments.
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Leibrecht, M., Scharler, J. Reconsidering Consumption Risk Sharing among OECD Countries: Some Evidence Based on Panel Cointegration. Open Econ Rev 19, 493–505 (2008). https://doi.org/10.1007/s11079-007-9052-6
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DOI: https://doi.org/10.1007/s11079-007-9052-6