Using the Midas approach for now- and forecasting Colombian GDP

This study applies the Factor-MIDAS approach (Marcellino and Schumacher, 2007) in the forecasting of Colombian GDP. The main objective is to test the performance of the predictions generated under this framework by means of Mean Squared Error values and forecast evaluation tests. Two forms of MIDAS (Mixed Data Sampling) projections were studied, MIDAS with exponential almon and MIDAS with unrestricted coefficients. Also, two methods for factor were used, one based on the EM algorithm and the other based on the state-space model with the Kalman filter. Both methods are able to handle missing values at the end of the sample due lags of publication. In addition, the factors were calculated using a large dataset of macroeconomic variables and a subset of it. The regressions were estimated using fixed factor lags along with an automatic lag selection. The nowcast and forecast performance of these regressions were compared with a simple benchmark model AR(1) model. The empirical findings show in general, that the MIDAS projections do not outperform the benchmark when the forecast tests are applied. There is only slight evidence that the MIDAS projections do better in the nowcast horizon. In terms of lower Mean Squared Error values, the better results are achieved when the number of factor lags is at most 3. Moreover, in this case there is no difference in the performance of these two projections. The automatic factor lag selection did not show any improvement compared to the use of very few fixed factor lags.