Abstract
This paper investigates the sensitivity of Latin American GDP growth to external developments using a Bayesian vector-autoregressive model with informative steady-state priors. The model is estimated using quarterly data from 1994 to 2007 on key external and Latin American variables. It finds that 50 to 60 percent of the variation in Latin American GDP growth is accounted for by external shocks. Conditional forecasts for a variety of external scenarios suggest that Latin American growth is robust to moderate declines in commodity prices and external growth, but sensitive to more extreme shocks, particularly a combined external slowdown and tightening of world financial conditions.
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Notes
See, for example, Tierny (1994). The chain is serially dependent but there has been no thinning of it.
The U.S. high-yield bond spread is sometimes interpreted as reflecting risk aversion; see Levy Yeyati and González Rozada (2005). An alternative measure, the Chicago Board of Trade Volatility Index (VIX), yields very similar results (not reported but 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.
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 and others, 1992); see Table A1. For 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 GDP, and Latin American GDP in first differences. The remaining variables are modeled in levels.
Table a1 Unit Root Tests This is achieved using an additional “hyper-parameter”, which is used to shrink the parameters on y t , c t and EMBI t in the equations for y t world and i t US and HY t to zero; see Villani and Warne (2003). Intuitively, this modeling approach amounts to imposing a tight prior distribution centered on zero for the parameters in question. This is somewhat less restrictive than imposing exogeneity directly, because it would allow an estimated nonzero posterior in the event that the data strongly disagree with our prior.
Lag length was set as 2 or 4. This did not make much difference. Below, results with lag length 2 are reported.
Noninformative priors on the constant μ, which allow the data to influence the steady-state parameters to a larger extent, produced qualitatively similar but less precise results.
Note that the ordering allows commodity prices to be contemporaneously affected by Latin American GDP shocks but not vice versa. The argument for this ordering is that GDP is a sticky variable while commodity prices are not. This said, it is also unlikely that Latin American GDP contemporaneously affects commodity prices, and our results did not change if we reversed the ordering of these two variables.
These appear sensible (see Figure A1). A world growth shock leads to an increase in U.S. short-term interest rates over two to three quarters and a decline in high-yield bond spread. A U.S. interest rate hike leads to lower world growth after a few quarters, as well as an increase in the high-yield bond spread. A shock to the latter leads to a dip in world growth and a gradual easing of U.S. interest rates. It also leads to a sharp and immediate jump in the Latin American EMBI, of a slightly larger magnitude as the high-yield bond spread shock itself. Note that the nine impulse response functions in the upper right quadrant of Figure A1 are flat, reflecting block exogeneity of world/U.S. variables.
The classical VAR is estimated using OLS. In this case, because 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.
GDP numbers are generally revised with some lag. For a discussion regarding real-time data issues, see, for example, Croushore and Stark (2002) and Orphanides and van Norden (2002).
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).
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Additional information
*Pär Österholm is a researcher at Uppsala University and a visiting scholar in the IMF's Western Hemisphere Department; Jeromin Zettelmeyer is an advisor in the IMF's Research Department. The authors thank Caroline Atkinson, Roberto Benelli, Eduardo Borensztein, Ben Clements, Alejandro Izquierdo, Chris Jarvis, Eduardo Levy Yeyati, Ernesto Talvi, and seminar participants at the International Monetary Fund and the LACEA 2007 Annual Meetings for valuable comments and discussions, and Priya Joshi for outstanding research assistance. Österholm gratefully acknowledges financial support from the Jan Wallander and Tom Hedelius research foundation.
Appendix
Appendix
Data Sources
World GDP: Haver Analytics and WEO database (export weighted index created using export shares of the LA6 countries to the rest of the world, export shares taken from the IMF's Direction of Trade Statistics).
Latin American (LA6) GDP: Haver Analytics, weighted using WEO PPP-GDP weights.
U.S. three-month Treasury bill rate and U.S. CPI: Haver Analytics.
U.S. high-yield corporate bond spread: Bloomberg.
Latin Emerging Market Bond Index Spread (EMBI): JPMorgan
Commodity price indices: Calculated based on UNCOMTRADE trade share and IMF commodity price data.
Unit Root Tests
Unit root tests were conducted for both the level and first differenced series, using the standard ADF test (null hypothesis: presence of a unit root in the time series), and the KPSS test (null hypothesis: absence of a unit root in the time series). The results are shown in Table A1.
Impulse Responses and Variance Decompositions
See Figures A1 and A2.
Variance Decompositions(Shown over 20-quarter horizon)
Source: Authors' estimations.Note: Shocks are in columns, variables are in rows.
Scenario Analysis
See Table A2.