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)| false Kwiatkowski, Denis, Peter C. B. Phillips, Peter J. Schmidt, and Yongcheol Shin, 1992, “ Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root: How Sure Are We That Time Series Have a Unit Root?,” Journal of Econometrics, Vol. 54, pp. 159– 178.
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We thank Caroline Atkinson, Roberto Benelli, Ben Clements, Eduardo Levy Yeyati and seminar participants at the International Monetary Fund for valuable comments, Chris Jarvis for asking the pertinent questions, and Priya Joshi for outstanding research assistance. We also gratefully acknowledge remarks by Guillermo Calvo at the 2006 LACEA meetings, based on work by Izquierdo, Romero and Talvi (2007), which partly motivated us to write this paper. Österholm gratefully acknowledges financial support from Jan Wallander’s and Tom Hedelius’ Foundation.
With the notable exception of Izquierdo, Romero and Talvi (2007), the literature tends to look at subsets of these shocks, focusing mostly on U.S. growth and monetary policy shocks.
See, for example, Tierny (1994). The chain is serially dependent but there has been no thinning of it.
Using the Chicago Board of Trade “Volatility Index” (VIX), yields very similar results to the high-yield corporate bond spread. Results are not reported but are available upon request.
This represents the largest economies in the region (except for Venezuela, which was excluded from the index because of its different economic structure), accounting for almost 90 percent of Latin American output. In Figure A3 in the Appendix we report some results from applying our model to the individual countries making up the LA6 index.
A real effective exchange rate index for the region was initially also included, but had no effect on the results.
We tested for unit roots using the Augmented Dickey-Fuller (ADF) test (Said and Dickey, 1984) and KPSS test (Kwiatkowski et al., 1992); see Table A1 in the Appendix. For log world GDP and the log commodity price index, both tests support the presence of a unit root in levels, while for the other variables the evidence for a unit root in levels is mixed (in particular, stationarity in levels cannot be rejected using the KPSS test). We hence take model commodity prices, world (or US) GDP and—for consistency with our treatment of world/US GDP— Latin American GDP in first differences. The remaining variables are modeled in levels.
This is achieved using an additional “hyper-parameter” which is used to shrink the parameters on yt, ct and EMBIt in the equations for
Non-informative priors on the constant µ which allow the data to influence the steady state parameters to a larger extent, produced qualitatively similar results.
If the model with U.S. growth and inflation is used (see Appendix, Figure A2), the influence of external factors rises to about 57 percent. 16 percent corresponds to U.S. growth and 5 percent to commodity prices, while U.S. financial conditions account for 27 percent of the variance in this model; the latter rises to 36 percent if the contribution of U.S. inflation is included in this category.
The classical VAR is estimated using OLS. In this case, since no restrictions have been imposed on the model, OLS is equivalent to maximum likelihood.
For variables expressed in first differences, RMSEs were calculated for forecast growth rates with respect to the same quarter in the previous year.
When the WEO forecasts are compared with the final data rather than the “real time” data, the WEO still outperforms the model at (and only at) the one year horizon, albeit by a smaller margin.
This exact imposition of particular paths has been called “hard conditions,” see Waggoner and Zha (1999). It is a common approach in the VAR literature; examples include Sims (1982) and Leeper and Zha (2003).
In this and the next subsection, we present results from “world growth model” only, as the results from the model that includes U.S. growth are very similar.