Nowcasting Peruvian GDP using Leading Indicators and Bayesian Variable Selection

There exists a large set of leading indicators that are directly related with GDP growth. However, it is often very difficult to select which of these indicators can be used in order to choose the best shortterm forecasting (nowcasting) model. In addition, it may be the case that more than one model can do this job accurately. Therefore, it would be convenient to average these potentially non-nested models. Following Scott and Varian (2015), we estimate a Structural State Space model through Gibbs Sampling and a spike-slab prior in order to perform the Stochastic Search Variable Selection (SSVS) method. Posterior simulations can be used to then compute the inclusion probability of each variable for the whole set of models considered. In-sample GDP estimates are very precise, taking into account the large set of regressors considered for the estimation. Data comes from the BCRPs database plus other additional sources.