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Appendix I. Data transformation
The authors would like to thank Steven Phillips, Trevor Alleyne, and Robert Rennhack for useful comments and discussion, and participants at the WHD seminar and the 2010 CEMLA conference.
The ten selected Latin American countries account for 94 percent of the regional GDP for Latin America.
Note that a more general specification would allow for lags
All quarterly series used in this paper are converted to the monthly frequency using linear interpolation. The results are robust to more sophisticated interpolation methods.
The monthly indicators are usually available ahead of the quarterly GDP release. The conditional forecast is constructed by imposing the latest observations of the monthly indicators on the VAR.
The coefficients β1,..., βp are assumed to be independent and normally distributed. Following Sims and Zha (1998), the covariance matrix of the residuals Ψ is assumed to follow an inverse Wishart distribution.
We de-mean and standardized the data series prior to estimation, see Appendix I for more details on data transformation.
In a separate paper Doz et al. (2006) show that by iterating steps 1 and 2, a quasi-maximum likelihood estimator for the factors is obtained.
We include industrial production, 3 retail sales series, the ISM survey for manufacturing, the unemployment rate, employment, and consumer confidence (Conference Board).
We found that including more than 10 variables generally led to a deterioration in forecast accuracy for both the pooled bridged equations and bivariate VAR forecasts.
For some countries, due to a lack of available data, we replaced one or more of these series with series that have a similar economic interpretation.
The BVARs contains 6 lags with λ set to 1, and the prior standard deviations on the autoregressive parameters are selected using error standard deviations from a AR(6) process.
Likewise, the ad-hoc criterion (of choosing the number of static factors to explain a certain proportion of the variation in key series, including GDP alone) used by Giannone, Reichlin, and Sala (2005) and Matheson (2010) greatly deteriorated forecast accuracy for some countries.
The monthly growth indicator is constructed based on a seven-month moving average of the estimated common component using the DFM model; the trend is the IMF World Economic Outlook estimate of the country’s potential GDP growth rate.
Private analysts estimates that real GDP growth for Argentina has been lower than the of official reports since the last quarter of 2008 (IMF WHD Regional Economic Outlook, October 2010), which could also distort the results of the analysis.
The results presented here are based on data downloaded from the Haver database on June 13, 2010.