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We are grateful to Caroline Atkinson, Ben Clements and seminar participants at the International Monetary Fund and Banco de la República for valuable comments on this paper. Österholm gratefully acknowledges financial support from Jan Wallander’s and Tom Hedelius’ Foundation.
Western Hemisphere Division, International Monetary Fund, 700 19th Street NW, Washington, DC 20431, USA. email: email@example.com Phone: +1 202 623 8754
Department of Economics, Uppsala University, Box 513, 751 20 Uppsala, Sweden. e-mail: firstname.lastname@example.org Phone: +1 202 378 4135
Private investment rose from 9 percent of GDP in 2002 to 19 percent of GDP in 2006.
For the priors governing the dynamics of the model, we employ a modified version of the Minnesota prior (Litterman, 1986). The prior mean on the first own lag is set to 0.9 if a variable is modelled in levels and 0 if it is in growth rates; all other coefficients in G have a prior mean of zero. The reason for the modification of the traditional Minnesota prior is that a prior mean on the first own lag equal to 1 is theoretically inconsistent with the mean-adjusted model, since a random walk does not have a well-specified unconditional mean.
It can be noted that the prior for this variable is centered on a number that exceeds the sum of the steady state GDP growth rate and an inflation target of, say, 3-4 percent. However, given that the variable we use is a lending rate, intermediation costs and a risk premium need to be added to that sum in order to arrive at a more relevant steady-state value.
A one-standard deviation shock is equivalent to 0.32 percentage points for global growth, 30 basis points for the U.S. treasury bill rate, 147 basis points for the EMBI spread, 43 basis points for the high yield bond spread, 1.25 percentage points for FDI, 2.09 percentage point for public spending growth, 0.70 percentage points for Colombian GDP growth and 165 basis points for the domestic interest rate.
The response to global growth shocks is stronger than that estimated by Österholm and Zettelmeyer (2008) for an aggregate of six Latin American countries. These authors’ estimates imply roughly a one-for-one relationship between domestic growth and global growth at the same time horizon. The stronger response of the Colombian economy could reflect its higher degree of trade openness (for most of the sample period), combined with a fair degree of sensitivity to changes in external financial conditions. It should be noted, however, that the two models are not fully comparable, as the set of variables they include is not the same; Österholm and Zettelmeyer do not include domestic variables in their model, while including a commodity-price variable.
For example, changes in the terms of trade could affect such variables. However, a version of the model including the terms of trade produced virtually the same results as our preferred specification. In particular, the domestic growth response to terms-of-trade shocks was not statistically different from zero, while the variance decomposition assigned a very minor role to that variable as a contributor to growth. Since FDI in the mineral sectors (oil and mining) could also respond to changes in the terms of trade, a model specification with the investment climate variable including only non-mineral FDI was also run. This, however, generated only very minor changes in the results.
In the exercise using the two BVAR models, for every draw from the posterior distribution of parameters a sequence of shocks is drawn and used to generate future data. This leads to as many paths for each variable as we have iterations in the Gibbs sampling algorithm. For each of the two models, a central forecast is then generated as the median forecast based on the forecast density at each horizon. These central forecasts are used for the point forecast comparison.